AI and GenAI adoption by local and regional administrations.
Date: 2024-12-18
Institutions: Commission for Economic Policy
Subjects: EU regional policy, artificial intelligence, civic engagement, decision-making, e-procurement, governance, local government, public sector, regional and local authorities, research infrastructure

Commission for
Economic Policy

AI and GenAI adoption 
by local and regional 
administrations

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© European Union, 2024 
Partial reproduction is permitted, provided that the source is explicitly mentioned. 
More information on the European Union and the Committee of the Regions is available online 
at http://www.europa.eu and http://www.cor.europa.eu respectively. 
QG-01-24-026-EN-N; ISBN: 978-92-895-3679-0; doi: 10.2863/6007868  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This report was written by  
Susanna Fontana, Beatrice Errico, Sara Tedesco, Fabio Bisogni 
(Fondazione FORMIT),  
Robin Renwick, Mikio Akagi, Nicole Santiago (Trilateral Research 
Limited). 

It does not represent the official views of the European Committee of the 
Regions 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of contents 

Executive Summary ................................................................................................................. 1 
Background and objective of this study ................................................................................. 1 
Methodology .......................................................................................................................... 1 
Report structure ...................................................................................................................... 2 
Key findings ........................................................................................................................... 4 
Policy recommendations ........................................................................................................ 5 
Introduction .............................................................................................................................. 6 
Part 1: State of play of AI and GenAI adoption at local and regional level ..................... 14 
1.1 Overview ........................................................................................................................ 14 
1.1.1 Local and Regional Authority (LRA) and Public Authority (PA) implementations .. 14 
1.1.2 Existing AI infrastructure to support LRAs ................................................................ 16 
1.1.3 Academic research ...................................................................................................... 19 
1.1.4 Citizen acceptance and user experience with AI in the public sector ......................... 21 
1.1.5 Summary ..................................................................................................................... 24 
1.2 State of play of AI adoption across the EU LRAs ......................................................... 25 
1.2.1 AI adoption, application sectors and main benefits for LRAs .................................... 26 
1.2.2 Human resources dedicated to AI and relations with the external environment ......... 34 
1.2.3 AI monitoring and evaluation mechanisms ................................................................. 46 
1.2.4 Challenges and drivers of AI adoption ........................................................................ 50 
1.2.5 Conclusions ................................................................................................................. 54 
Part 2: Factors for the successful AI and GenAI adoption in cities and regions ............. 59 
2.1 Fostering cooperation between the technical and political levels .................................. 60 
2.2 The facilitating role of political decision-makers and appropriate governance ............. 62 
2.3 Organisational context and AI procurement .................................................................. 65 
2.4 Enhancing citizen acceptance and engagement ............................................................. 67 
2.5 Data availability and governance ................................................................................... 70 
2.6 Making AI-based solutions scalable and applicable to other contexts ........................... 72 
2.7 Investment and funding .................................................................................................. 73 
Part 3: Foresight analysis, conclusion and policy recommendations ................................ 75 
3.1 Foresight analysis ........................................................................................................... 75 
3.1.1 The AI trends identified by survey respondents .......................................................... 76 
3.1.2 The Horizon Scanning for relevant weak signals detection ........................................ 80 
3.1.3 Megatrends .................................................................................................................. 86 
3.1.4 Results of the foresight analysis .................................................................................. 88 
3.2.1 Overview of the policy recommendations on the AI implementation ........................ 91 
3.2.2  Recommendations .................................................................................................. 94 
3.3 Conclusions of the study .............................................................................................. 102 
Annex I – The online survey ................................................................................................ 105 
Annex II – Eight case studies .............................................................................................. 114 
Case II.1. De Virtuele Assistent (‘The Virtual Assistant’) (Belgium) ............................... 115 
Case II.2. Green public transport on demand (Bulgaria) ................................................... 118 
Case II.3. Vaasan kadut kuntoon (‘Fix the Streets’) (Finland) .......................................... 122 
Case II.4. Satellites for Wilderness Inspection and Forest Threat Tracking (SWIFTT) 
(Latvia) ............................................................................................................................... 126 
Case II.5. Vadindekset (‘The wet index’) project (Denmark) ............................................ 130 
Case II.6. Policies for Holistic Urban Mobility and Accessibility (Cyprus)...................... 133 
Case II.7   The AI-based LisNav application dedicated to transforming the lives of the 
visually impaired (Portugal) ............................................................................................... 136 

 
 
 
Case II.8. Immediate and Long-Term Energy Reduction (RECITAL) (France) ............... 139 
Annex III – Bibliography ..................................................................................................... 142 

 
 
 
 
 
List of acronyms   

AI 
AI4Gov 

AI HLEG 
AIDA 
AIoD 
ALTAI 
AMTEGA 
AR 
ARISA 
BMA 
CML 
CO2 
CoR 
COVID-19 
DA 
DEET 
DESI 
DGA 
DIAT 

DIGIT 
EC 
ECON 

EIF 
EIT 
ELAN 

ERDF 
ESF+ 
EU 
GAIS 
G2B 
G2C 
G2G 
GDPR 
GenAI 
GovTech 
GPS 

Artificial Intelligence 
Trusted AI for Transparent Public Governance fostering 
Democratic Values 
High-Level Expert Group on Artificial Intelligence 
Artificial Intelligent in the Digital Age 
AI-on-Demand Platform 
Assessment List for Trustworthy AI 
Agency for the Technological Modernisation of Galicia 
Augmented Reality 
Artificial Intelligence Skills Alliance 
Better Mobility Accelerator 
Lisbon City Council 
Carbon Dioxide 
European Committee of the Regions 
Coronavirus disease of 2019 
Data Act 
Tertiary Eco-Energy Scheme 
Digital Economy and Society Index 
Data Governance Act 
Demonstrators of frugal artificial intelligence at the service 
of the ecological transition of territories 
European Commission’s Directorate-General for Informatics 
European Commission 
Commission for Economic Policy of the European 
Committee of the Regions 
European Interoperability Framework 
European Institution of Innovation & Technology 
French Law on changes in housing, land management and 
digital technology 
European Regional Development Fund 
European Social Fund Plus 
European Union 
Galician Artificial Intelligence Strategy 
Government-to-Business 
Government-to-Citizen 
Government-to-Government 
General Data Protection Regulation 
Generative Artificial Intelligence 
Government Technology 
Global Positioning System 

 
 
 
Hydrological Information and forecasting system 
The Digital Arts and Entertainment programme 
Information and Communication Technologies 
Internet of Things 
Intellectual Property 
Information Technology 
European Commission’s Joint Research Centre 
Local Government Denmark 
Key Performance Indicator 
Programme for the Environment and Climate Action 
Large Learning Model 
Local and Regional Authorities 
License Plate Recognition 
Machine Learning 
Nitrogen Dioxide 
Optical Character Recognition 
On-demand Public Transportation Platform 
Organisation for Economic Co-operation and Development 
Public Authority 
Polymerase Chain Reaction 

HIP 
HoWest 
ICT 
IoT 
IP 
IT 
JRC 
KL 
KPI 
LIFE 
LLM 
LRAs 
LPR 
ML 
NO2 
OCR 
ODPTP 
OECD 
PA 
PCR 
PSI Directive  Directive on the re-use of public sector information 
PSTW 
R&D 
RDI 
RECITAL 
RRF 
SaaS 
SCAIS 

Public Sector Tech Watch 
Research and Development 
Research, Development, and Innovation 
Immediate and Long-Term Energy Reduction 
Recovery and Resilience Facility 
Software-as-a-service 
Sustainability Criteria and Indicators for Artificial 
Intelligence Systems 
Sofia Development Association 
Sustainable Development Goal 
Spanish Digital Strategy 
Commission for Social Policy, Education, Employment, 
Research and Culture of the European Committee of the 
Regions 
Small and Medium Enterprises 
French National Strategy for Artificial Intelligence 
Technology-Organisation-Environment 
Technical Support Instrument 
Urban Innovative Action 
United States of America 
World Economic Forum 

SDA 
SDG 
SDS 
SEDEC 

SMEs 
Snia 
TOE 
TSI 
UIA 
US 
WEF 

 
 
XAI 

Explainable AI 

 
 
 
Executive Summary 

Artificial Intelligence (AI) is integrated in several sectors of society and its role 
and  influence  on  economic  and  social  domains  are  expected  to  increase  in  the 
years to come. The urge for a regulatory framework to support its assimilation 
became evident at European level. For this reason, the European Union adopted 
the  AI  Act  (Regulation  1689/2024),  which  will  be  an  influential  reference  in 
guiding the adoption and implementation of AI at European level and beyond due 
to its innovativeness and scale of application.  

Background and objective of this study 

The recently adopted European regulation offers a definition of AI, a term coined 
in the 1950s but whose boundaries are not conclusively defined. In the AI Act, 
the definition of AI is not limited to a single technological application but extends 
to all machine-based systems capable of adaptive behaviour that share the ability 
to  generate  outputs  of  various  typologies  following  the  processing  of  received 
inputs. The GenAI subcategory is related to systems characterised by the ability 
to  generate  new  content,  supporting  activities  ranging  from  text  generation  to 
predictive analysis. 

In this respect, there is much potential for the fruitful exploitation of AI by the 
public  sector.  Despite  to  date  it  is  noted  that  AI  is  most  widely  employed  at 
national level around Europe, exploitation of AI by local and regional authorities 
(LRAs, also known as subnational authorities) – as the closest level to citizens - 
is essential to foster the spreading and public acceptance of this technology. The 
benefits that AI could bring to local public administrations are varied and range 
from  improving  the  efficiency  of  internal  organisation  to  enhancing  the 
interaction between the government and citizenship and to supporting decision-
making  processes.  On  the  opposite  side,  the  risks  involved  in  the  use  of  this 
technology  are  various  and  derive  from  operational,  strategic  and  ethical 
challenges.  

With  the  aim  of  delving  deeper  into  the  opportunities  and  challenges  that 
European subnational authorities face with respect to AI and GenAI technologies, 
this study draws and expands on research conducted to date on their adoption at 
local and regional level.  

Methodology  

The study is built upon a systematic review of academic research, legislation and 
practices related to the adoption of AI at subnational level. Further analysis has 
been  conducted  through  evidence-based  observations  stemming  from  primary 

1 

 
 
 
 
 
 
data on the current state of play of AI adoption at local and regional level. Data 
was  collected  through  an  online  survey  targeting  LRAs  and  launched  in 
September 2024. The survey aimed at exploring the details of the concrete uptake 
of AI by  local administrations, investigating  aspects such as who  promoted  its 
adoption within the organisation, and their expectations and opinions on the future 
of AI use at subnational level. Additional primary data have been consolidated 
from interviews conducted to build eight case studies, as well as from experts who 
not  only  have  expertise  in  AI  but  also  have  a  deep  understanding  of  its 
implementation in public administration settings. Lastly, in order to contribute to 
the  academic  and  policy  debate  on  the  future  adoption  of  AI  with  original, 
evidence-based  insights,  this  study  provides  action-oriented  recommendations 
based  on  the  study  findings  and  on  a  foresight  analysis.  This  latter  analysis  is 
intended  to  offer  an  overview  of  possible  developments  of  the  AI  in  the  near 
future. 

The methodological approach used to conduct this study comprised the following 
steps: 

•  A  literature  review about  the state of play  of  AI  and GenAI adoption  at 
local  and  regional  level,  with  special  reference  to  the  previous  recent 
research results achieved in this area. 

•  Data  collection  through  an  online  survey  to  gather  evidence  on  the 
characteristics, drivers and barriers of AI use amongst LRAs. The survey 
results  are  interpreted  with  a  focus  on  the  demographic  size  and  the 
geographical area in which LRAs are located. 

•  Identification and analysis of AI-based solutions that are led by local and 
regional administrations to explore the critical factors that contribute to the 
successful adoption and implementation of the technology at subnational 
level. 

•  Foresight analysis based on the consolidation of (1) the AI trends identified 
by the LRAs that participated in the survey, (2) the Horizon Scanning for 
the relevant weak signals detection and (3) the Megatrend analysis. 

•  Drafting  of  political  and  operational  recommendations  to  the  European 

Union, as well as the Member States and the LRAs. 

Report structure  

The study is structured in three parts.  

Part  1  is  divided  in  two  main  sections.  The  first  section  offers  a  high-level 
summary of research on the use of AI in the public sector. An overview of the 
uses of AI in the public sector and a summary of the academic research on the 
topic is therefore provided. Specific emphasis is posed on topical aspects such as 

2 

 
 
 
 
 
 
the factors that enable or hinder AI adoption, citizen acceptance and the perceived 
risks  of  public  sector  AI.  The  second  section  has  the  objective  of  offering  an 
overview of the current state-of-play of AI adoption by the public sector at the 
subnational level across the EU Member States and draws conclusions based on 
the analysis of the responses of the online survey submitted to LRAs in all 27 EU 
Member States. The survey results were divided for analysis into four areas to 
investigate AI adoption and implementation processes in relation to aspects of the 
internal  organisation  of  local  public  authorities  and  their  relationship  with  the 
ecosystem in which they are integrated. 

Part 2 explores the factors for the successful AI adoption at the local and regional 
level. The section is based on the elaboration of primary data collected through 
qualitative research. In more detail, information is gathered from interviews with 
officials at the local and regional level and experts from academia and research 
centres.  Interviewees  contributed  to  enriching  the  analysis  by  sharing  the 
theoretical  knowledge  and  practical  insights  based  on  their  professional 
experience  into  AI's  role  in  local  and  regional  governance.  The  factors  that 
contribute to the successful adoption and implementation of AI technologies are 
analysed through the lens of the Technology-Organisation-Environment (TOE) 
framework. Elements considered to be of relevance for the successful use of AI 
include:  the  volume  of  investments,  both  from  the  public  sector  (at  regional, 
national or European levels) and the private sector; the degree of collaboration 
between  public  administration  and  private  companies  on  the  ground;  and  the 
concrete support provided by policy makers to digitisation projects. In addition, 
the  importance  of  digital  skills  and  a  deep  understanding  of  technological 
solutions  by  the  personnel  of  the  administrations  is  emphasised.  Crucial  also 
appears  to  be  the  involvement  of  citizens  in  the  implementation  of  digital 
solutions. In conclusion, the potential scalability as a criterion for evaluating the 
success of a project is discussed. 

Part 3 of the study is composed of two sections and is aimed at providing policy 
recommendations  addressing  relevant  aspects  of  AI  implementation.  The  first 
section  is  dedicated  to  the  foresight  analysis,  the  in-depth  explanation  of  the 
components that comprised it and the exposition of the related results.  

The  foresight  analysis  focuses  on  possible  future  trends  and  unforeseen 
developments  in  AI  technology.  The  analysis  benefitted  from  the  responses 
received to the online survey, that were clustered in ‘trends’ and used to derive 
insights and implication of AI for public administrations. Together with trends, 
the analysis made use of ‘weak signals’ and ‘megatrends’.  

‘Weak signals’ are considered as emerging signals of development with a likely 
future impact, analysed through the technique of Horizon Scanning. In particular, 

3 

 
 
 
 
 
the weak signals relevant to the present research, as described in the 2021 JRC 
report, are: Ethical AI, Green AI, Smart cities and Edge AI. Finally, the analysis 
considered  the  ‘megatrends’,  intended  as  long-term  driving  forces  that  are 
observable  now  and  will  continue  to  have  a  global  impact  in  the  future. 
Furthermore, it is postulated that megatrends can produce an effect on the trends, 
accelerating them and enhancing their impact.  

Based  on  the  development  of  the  foresight  analysis,  in  the  second  section 
proposals  for  political  actions  are  advanced,  directed  to  the  EU,  national 
governments  of  EU  Member  States  and  LRAs.  The  objective  of  the 
recommendations is to support a coordinated action on AI adoption for the public 
sector  at  local  and  regional  level  in  the  EU.  Eight  recommendations  were 
developed on the basis of the integration of the results of the analyses carried out 
in  the  study  and,  in  order  to  avoid  redundancies,  an  overview  of  the 
recommendations issued so far on the implementation of AI in the public sector 
is provided beforehand. The recommendations cover various thematic areas, from 
monitoring and evaluation to regulatory support and the relationship between the 
political and technical layers.  

Key findings 

The overall results of the study present interesting data. The results of the online 
survey addressed to LRAs show a good adoption rate of AI at European level. 
Indeed,  of  the  153  answers  received,  around  27%  of  respondents  report  to  be 
adopting AI solutions at present. According to the survey results, across European 
regions, the most common use of this technology is on ‘AI-based services for the 
public  administration’  and  the  benefit  most  frequently  reported  by  LRAs  is 
represented by the ‘optimisation of internal management’.  

Considering the aspects on which priority action should be taken to facilitate the 
dissemination  of  the  technology,  the  main  challenges  that  LRAs  face  in  the 
adoption and implementation phases relate to cross-cutting issues such as the ‘lack 
of  skills  and  expertise’,  ‘budget  constraints’  and  the  ‘lack  of  awareness  and 
knowledge on standardisation processes’. On the other hand, responding LRAs 
identified the most relevant driver of AI adoption in the ‘Improved Administrative 
Efficiency’. 

The analysis of success factors for the AI and GenAI adoption took advantage of 
the  eight  case  studies  explored  through  the  interviews  with  representatives  of 
selected LRAs. As anticipated, different transversal factors enabling successful 
AI  implementation  have  been  investigated  and  reference  to  these  case  studies 
provides an understanding of the concrete facets and challenges faced by the local 
public authorities, starting from the infrastructural point of view to that of political 

4 

 
 
 
 
 
and regulatory support. In addition to the case studies, two initiatives analysed 
stand out for their innovativeness and potential as examples on AI governance and 
regulation carried out by LRAs: the development of a regional AI Law by the 
Agency for the Technological Modernisation of Galicia (AMTEGA) and the AI 
governance  framework  led  by  the  Québec  Innovation  Council,  a  subnational 
authority based in Canada. 

Amongst  the  elements  that  emerge  as  progressively  important,  particular 
reference  was  made  to  ethical  considerations,  also  considering  divergence  in 
ethical  principles  amongst  EU  countries  and  to  environmental  sustainability, 
given  the  possibility  for  AI  to  simultaneously  increase  and  reduce  energy 
consumption and CO2 emissions. In addition, the development of smart cities and 
the integration of Edge AI have the potential to exponentially increase the use of 
the technology and serve most remote areas, thus encouraging the use of AI in 
different sectors and potentially addressing the digital divide. 

Policy recommendations 

The  eight  recommendations  resulting  from  these considerations  are  directed to 
policy makers at different levels and reflect the need to support the development 
of  the  use  of  AI  by  LRAs  from  technological,  social,  political  and  ethical 
perspectives.  The  recommendations  addressed  to  EU  institutions  concern  the 
supporting  role  of  monitoring  and  evaluation  frameworks,  legal  structures 
represented  by  regulatory  sandboxes  and  intersectoral  partnerships.  Member 
States  are  encouraged  to  support  the  application  of  regulatory  sandboxes  and 
partnerships,  as  well  as  to  invest  in  research  and  innovation  that  promotes 
openness,  data  interoperability,  scalability  and  public  trust,  without  forgetting 
their critical role in encouraging digitally inclusive initiatives. Moreover, greater 
efforts by the EU and the Member States for fostering the AI employment for 
sustainability  is  also  stressed.  The  recommendations  to  LRAs  point  towards 
greater  involvement  of  citizens  in  the  AI  design  process  through  co-creation 
practices. LRAs are also recommended to prioritise inclusive practices to address 
the  digital  divide  and  to  improve  cooperation  amongst  various  internal 
departments. 

Finally, with a view to further validate these results, a comparison between the 
latest  JRC  report  (2024)  on  factors  that  influence  public  managers  for  the  AI 
adoption and the findings of the present study is conducted. The results of both 
studies  suggest  recommending  context-sensitive  strategies  to  support  the 
enhancement of digital skills, AI governance, and public acceptance. 

5 

 
 
 
 
 
Introduction  

The AI Act (Regulation 1689/2024) defines AI as 

‘a  machine-based  system  that  is  designed  to  operate  with  varying  levels  of 
autonomy  and  that  may  exhibit  adaptiveness  after  deployment,  and  that,  for 
explicit or implicit objectives, infers, from the input it receives, how to generate 
outputs  such  as  predictions,  content,  recommendations,  or  decisions  that  can 
influence physical or virtual environments’ (Article 3(1)) 

In the context of AI, GenAI systems are those that generate ‘content’, e.g. realistic 
text, images, illustrations, audio (including speech) or video, typically based on a 
user prompt. GenAI can refer to simple chatbots, but typically refers to what the 
AI Act calls ‘general-purpose AI models’: 

‘general-purpose  AI  model’  means  an  AI  model,  including  where  such  an  AI 
model is trained with a large amount of data using self-supervision at scale, that 
displays significant generality and is capable of competently performing a wide 
range of distinct tasks regardless of the way the model is placed on the market 
and that can be integrated into a variety of downstream systems or applications, 
except AI models that are used for research, development or prototyping activities 
before they are placed on the market’ (Article 3(63); see also Recitals 97–100) 

It is widely believed that AI technologies, including GenAI, have the potential to 
benefit  local  and  regional  administrations.  The  2021  opinion  by  the  European 
Committee of the Regions (CoR) on the AI Act highlighted that: 

‘AI  systems  can  play  an  important  role  in  local  and  regional  authorities’ 
interaction with citizens and service provision. Furthermore, AI systems have the 
potential, among other things, to improve public-sector efficiency and help local 
and regional authorities to respond to the adjustments that need to take place at 
local and regional level in the context of the green and digital transitions. It is 
therefore important that the experience gained by local and regional authorities 
is actively used in the ongoing revision of the Regulation’ (European Committee 
of the Regions, 2021, Policy Recommendations, point 6) 

The  same  year,  a  European  Parliament  briefing  on  ‘Artificial  Intelligence  and 
public services’ (Timan, van Veenstra and Bodea, 2021) articulated a variety of 
benefits and risks of AI adoption for the provision of public services. The main 
benefits include (3–6): 

6 

 
 
 
 
 
 
 
 
 
•  Improved  efficiency  and  optimisation  of  internal  processes,  leading  to 
increased  economic  growth  and  the  creation  of  jobs  (see  also  Purdy  and 
Daugherty, 2016; Gartner, 2017); 

•  More accurate predictions and optimised workflows, such as algorithms that 
aim to predict which citizens are at risk of debt so that case workers can target 
pre-emptive interventions (see also Steen, Timan and van de Poel, 2021); and 
•  Better service delivery through personalisation, provided through chatbots or 
other  personalised  interfaces  for  citizens  in  web  portals  and  apps  (see  also 
Eurofound, 2020). 

These benefits are echoed in a report by AI Watch (EC Joint Research Centre, 
2022b), which adds that AI systems can help public sector bodies by (12–13): 

•  Integrating  data  from  different  domains  to  better  understand  the  impacts  of 

decisions; 

•  Monitoring data concerning the regular or daily uses and processes to improve 

efficiency (e.g., energy consumption or traffic flow); and 

•  Simulating scenarios to aid decision-making by modelling possible outcomes 

(e.g. urban sustainable planning). 

The European Parliament briefing (Timan, van Veenstra and Bodea, 2021) also 
identified several main risks of AI adoption in the public sector, in particular for 
AI technologies based on machine learning (ML). These risks include (6–7): 

•  Lack  of  transparency  and  explainability,  since  ML-based  systems  are  often 
‘black box’ technologies that produce outputs without explicit justifications or 
reasoning that can be easily reconstructed (see also BEUC, 2019); 

•  Dehumanisation, since AI technologies can contribute to systems with opaque 
processes,  limited  accountability  or  opportunities  for  redress,  and  fewer 
exceptions to the rules based on common sense or sympathy (see also Ananny 
and Crawford, 2016); and 

•  Discrimination due to biases in data and algorithmic assumptions (see also The 

Markup, 2020). 

AI  Watch  reinforced  these  concerns,  and  noted  additional  risks  (EC  Joint 
Research Centre, 2022, 16–17): 

•  Job  losses,  since,  contrary  to  some  suggestions,  it  is  not  clear  that  all 
efficiencies will result in higher net employment (Tolan, et al., 2021) and risks 
can be mitigated by initiatives such as job reallocation and re-training; 

7 

 
 
 
 
 
 
•  Privacy  violations,  since  the  development  of  AI  technology  incentivises 

surveillance and the collection of large quantities of data; and 

•  Environmental damage, since AI technologies such as ML are computationally 

demanding and consume large amounts of energy (see Dhar, 2020). 

Previous studies have examined the adoption of AI and GenAI technologies by 
LRAs  and  other  public  sector  bodies  in  Europe.  Two  of  the  recent  efforts  to 
catalogue use cases of AI in the European public sector are represented by the 
work of AI Watch and, more recently, of Public Sector Tech Watch (PSTW).  

AI Watch is a knowledge service provided by the European Commission’s Joint 
Research Centre (JRC) since 2018. PSTW is an observatory co-established by the 
European  Commission’s  Directorate-General  for  Informatics  (DIGIT)  and  the 
JRC.  

Since 2023, PSTW has combined the 686 cases collected by AI Watch with cases 
catalogued by the Innovative Public Sector Observatory to create a repository of 
over  1,300  cases  of  AI  use  in  the  public  sector  (as  well  as  use  cases  of  other 
innovative technologies, such as Blockchain; EC Directorate-General for Digital 
Services, 2024; EC Public Sector Tech Watch, n.d.). 

Some examples of use cases detailed in a 2022 report (EC Joint Research Centre, 
2022a, 48) are: 

•  Greece’s ‘Eva’ system (case #2): a machine learning system trialled during the 
2020 COVID-19 crisis, designed to allocate scarce PCR tests at border control 
points based on personal characteristics of travellers. 

•  Amsterdam’s Object Detection Kit (case #5): a computer vision system that 
analyses images taken from vehicles around the city to identify garbage and 
dispatch garbage management services. 

•  The Finnish Tax Administration’s captioning system (case #7): a speech-to-

text system for improving accessibility and searchability. 

•  The Spanish Tax Agency’s income estimation system (case #8): an automated 
reasoning system designed to predict the annual income of tax subjects (such 
as SMEs and self-employed individuals) who elect to pay their taxes in phases 
and do not declare an exact income in advance. 

The PSTW repository includes cases from 33 European countries (including all 
EU  Member  States).  Cases  in  the  repository  are  frequently  updated  and  are 
categorised according to the PSTW taxonomy. This taxonomy includes: 

8 

 
 
 
 
 
 
 
 
•  Organisation features (such as the responsible organisation and governmental 

sector); 

•  Service description features (such as whether a system is in development, a 
pilot, etc. and whether it is designed to serve citizens, private organisations or 
other government bodies); 

•  Value-of-service features (such as personalisation, cost-reduction, improved 

transparency); and 

•  Artificial intelligence features (such as whether the system involves machine 
learning, natural language processing, connected and automated vehicles)1.  

By cataloguing features for each use case, the PSTW repository makes it possible 
for  inquirers  to  answer  various  fine-grained  questions  about  European  AI 
adoption  using  its  freely  available  online  case  viewer  (EC  Public  Sector  Tech 
Watch, n.d.). 

According  to  PSTW2,  European  States with  the  most  repository  use  cases  are: 
Italy (197), Germany (195), the Netherlands (191), the United Kingdom (117), 
Spain (114), Belgium (86), Estonia (81), Finland (74), France (72), Portugal (72), 
Norway (66), Denmark (62), Sweden (44), Poland (33), and Greece (32). Other 
included  countries  have  fewer  than  30  documented  use  cases  each  (EC  Public 
Sector Tech Watch, n.d.).  

While these numbers may give an impression of the relative adoption rates of AI 
by public administrations, there are limitations to the data collection approach and 
AI  Watch  cautions  against  making  comparisons  between  countries,  since there 
may be varying degrees of underreporting (EC Joint Research Centre, 2022a, 34–
5). 

AI Watch and PSTW also note that a majority of use cases occurs at the national 
level, as opposed to local, regional or international cases. It is plausible that this 
is because national governments have the most resources for AI tools (EC Joint 
Research Centre, 2022a, 37; EC Directorate-General for Digital Services, 2024, 
53).  

Nevertheless, we have seen rising rates of adoption of AI by LRAs facilitated, in 
some  cases,  by  private  services  and  open-source  options  (EC  Joint  Research 
Centre, 2022a, 45). Both local and regional use cases have gained in their share 
of described cases, rising from 27% to 33% and 10% to 11%, respectively, since 
2022 (EC Joint Research Centre, 2022a, 36; EC Public Sector Tech Watch, n.d.). 

1 For further details, see EC Joint Research Centre, 2022a, 30–34; EC Directorate-General for Digital Services, 
2024, 36–42. 
2 Last update: December 4, 2024.  

9 

 
 
 
 
 
 
 
 
The most common governmental sector to be augmented by AI technologies is 
‘general public services’ (about a quarter of cases, mostly chatbots on websites), 
followed  by  public  order  &  safety,  economics  affairs,  health,  and  social 
protection.  There  is  also  a  recent  increase  in  environmental  use  cases.  Cases 
relating to national security may be under-reported due to secrecy considerations 
(EC  Directorate-General  for  Digital  Services,  2024a,  50–51).  This  ranking  of 
sectoral uses across all levels is largely mirrored at the local and regional level 
(EC Public Sector Tech Watch, n.d.).  

AI Watch reports that many local use cases concern initiatives such as smart cities, 
digital twins,  and sustainability  efforts  (EC  Joint  Research  Centre,  2022b, 13). 
Larger,  wealthier  cities  are  more  likely  to  adopt  smart  city  measures;  90%  of 
European cities with populations over 500,000 have smart city initiatives of some 
sort,  compared  with  43%  of  cities  with  100,000–200,000  residents,  in  2021. 
However,  not  all  smart  city  initiatives  necessarily  involve  AI  technologies 
(Pellegrin, Colnot and Delponte, 2021, 25). 

AI Watch discusses the influence of various kinds of factors on selected use cases, 
including  political,  organisational,  infrastructural,  technical,  demand-related, 
supply-related,  financial,  legal  and  ethical  factors  (EC  Joint  Research  Centre, 
2021,  16;  2022a,  47–54).  In  many  cases,  attention  to  these  factors  reveals 
differences between the use of AI technologies in the public sector compared to 
the ones used by private organisations.  

By  way  of  example,  AI  solutions  for  the  public  sector  may  require  greater 
transparency and explainability, even at the cost of accuracy, so decisions may be 
justified  to  the  public.  In  another  example,  relevant  information  (such  as  a 
subject’s occupation) might be excluded from the inputs of an AI system because 
data are not collected on pre-existing instruments, or because there are questions 
about the legal basis for data processing according to GDPR (EC Joint Research 
Centre, 2022a, 51). 

These differences between public sector and private sector requirements for AI 
technologies can become barriers to AI adoption within public administrations. 
Other barriers include the recognition of genuine risks of deploying AI systems 
(described above), as well as other factors recognised in existing reports: 

•  Existing datasets,  algorithms and training techniques often lack  a  degree  of 
transparency  and  explainability  that  is  desirable  in  public  sector  activities 
(Timan, van Veenstra and Bodea, 2021, 7; EC Joint Research Centre, 2022a, 
51; 2022b, 18). 

•  Many  datasets  and  data-collection  mechanisms  are  not 

tailored  for 
governmental use, e.g., with sufficient documentation of privacy protections 

10 

 
 
 
 
 
 
or consideration of regulatory restrictions (Timan, van Veenstra and Bodea, 
2021, 8; EC Joint Research Centre, 2022a, 51; 2022b, 18). 

•  Many  public  bodies  lack  the  in-house  expertise  to  develop  and  operate  AI 
systems,  as  well  as  the  sociotechnical  expertise,  to  exercise  appropriate 
oversight and risk management (EC Joint Research Centre, 2022a, 52; 2022b, 
18;  Timan,  van  Veenstra  and  Bodea,  2021,  7–8;  Pellegrin,  Colnot  and 
Delponte, 2021, 28). 

•  In some cases, there is a lack of leadership support for innovative technologies 
or for deploying the labour necessary to collect high-quality data (EC Joint 
Research Centre, 2022a, 51–52; 2022b, 18). 

•  Public  trust  in  AI  technologies  and  algorithmic  techniques  is  low  in  many 
areas. This lack of trust can be exacerbated by the fact that many AI systems 
rely  on  sensitive  data  or  partnerships  with  private  companies  (EC  Joint 
Research Centre, 2022b, 19; see also BEUC, 2020; Medaglia et al., 2021). 
•  AI  technologies  can  be  perceived  as  especially  risky,  since  they  are  still 
regarded as relatively novel in the public sector; risk management measures 
and  impact  assessment  techniques  are  seen  as  requiring  further  empirical 
validation  (EC  Joint  Research  Centre,  2022a,  53–54;  2022b,  19;  EC  Joint 
Research Centre, 2020). 

Drivers of AI adoption by LRAs can include the mitigation of these barriers, as 
well as other factors such as: 

•  Simplification of regulatory frameworks in order to enable data-sharing and 
confidence in cross-border applications of AI systems (Timan, van Veenstra 
and  Bodea,  2021,  5;  EC  Joint  Research  Centre,  2022b,  19;  European 
Committee of the Regions, 2024b); and 

•  Alignment of government values, strategies, processes, and AI development 
practices, so that cost savings can be achieved through efficiencies delivered 
by  suitable  AI  systems,  whether  developed  in-house  or  by  private 
organisations (Timan, van Veenstra and Bodea, 2021, 5). 

Significant  agreement  exists  about 
these  drivers  and  barriers  amongst 
organisations such as AI Watch and the European Parliament. Correspondingly, 
there are major commonalities amongst the recommendations offered by the same 
bodies  and  by  the  EC  for  how  public  bodies  can  increase  the  adoption  of 
‘responsible  AI’  technologies.  Member  States  and  public  administrations  are 
encouraged to: 

•  Promote  awareness  regarding  AI,  including  both  the  positive  impacts  of 
adopting AI technologies and the complexity and risks of these technologies 
(EC  Directorate-General  for  Digital  Services,  2024,  68;  EC  Joint  Research 
Centre, 2022a, 58–59; European Committee of the Regions, 2024b); 

11 

 
 
 
 
 
•  Share practices and experiences, including success stories and failures, with 
others  across  Europe  in  order  to  foster  cross-border  solutions  (Timan,  van 
Veenstra  and  Bodea,  2021,  5;  EC  Directorate-General  for  Digital  Services, 
2024, 69; European Commission, 2019; European Committee of the Regions, 
2024b); 

•  Build  community  by  co-creating  solutions  and  developing  synergies  across 
national  and  European  initiatives  (EC  Directorate-General  for  Digital 
Services, 2024, 70; European Commission, 2019); 

•  Develop  AI  skills,  including  AI  technical  and  sociotechnical/management 
expertise within public sector organisations and AI skills training programmes 
in  national  education  curricula  (EC  Joint  Research  Centre,  2022a,  56; 
European Commission, 2019; European Committee of the Regions,  2024b); 
and 

•  Develop  partnerships  between  national  authorities  or  LRAs  and  other 
significant  actors  such  as  universities,  start-up  companies  and  large-cap 
companies (EC Joint Research Centre, 2022a, 56–57; European Commission, 
2019; European Committee of the Regions, 2024b). 

Many  of  these  recommendations  have  been  incorporated  into  the  EC’s  new 
strategic  investment  framework  in  ‘trustworthy  AI’  (European  Commission, 
2019).  This  framework  includes  investment  in  supercomputing  infrastructure, 
startup funding, educational initiatives and other programmes. These initiatives 
are  meant  to  promote  the  development  of  AI  skills  and  the  distribution  of 
infrastructure  related  to  AI  and  data,  especially  near  ‘AI  Factories’,  where 
investment  in  supercomputing  and  data  infrastructure  will  be  concentrated. 
Additionally, the EC has a wide range of funding instruments such as Horizon 
Europe,  Digital  Europe,  and  the  Technical  Support  Instrument  (TSI)  which 
contribute to fostering the AI adoption. 

Importantly for LRAs, the ‘trustworthy AI’ framework includes the ‘GenAI4EU’ 
initiative,  which  aims  to  support  collaborations  between  AI  start-ups  and 
deployers of AI in industry and the public sector by focusing on fostering practical 
applications  of  AI  technologies  in  thirteen  key  sectors.  These  sectors  include: 
robotics;  healthcare;  biotechnologies  and  chemicals;  materials  and  batteries; 
manufacturing  and  engineering;  mobility;  climate  change  and  environmental 
sustainability;  virtual  worlds  and  digital  twins;  cybersecurity;  aerospace; 
AgriFood; sciences; and the public sector. In addition, the framework includes 
initiatives  such  as  ALT-EDIC  that  are  meant  to  promote  the  development  of 
language models for European languages, including languages with few speakers 
and  limited  language  data.  The  GenAI4EU  initiative  will  provide  EUR  500 
million  through  the  Horizon  Europe  and  Digital  Europe  grant  programmes  by 
2027. 

12 

 
 
 
 
The  most  significant  effort  to  harmonise  the  regulatory  framework  for  AI 
throughout the European Union is the AI Act (Regulation 1689/2024). The AI Act 
outlines a ‘risk-based approach’, classifying AI systems with respect to their risks 
and  setting  out  different  requirements  and  obligations  based  on  risk  levels.  AI 
systems that present ‘unacceptable’ risks are prohibited. AI systems that are ‘high-
risk’ may have significant impacts on health, safety or fundamental rights and are 
subject to demanding requirements and obligations. For AI systems that present 
limited  risks,  the  AI  Act  outlines  information  and  transparency  requirements. 
There are also specific rules for ‘general-purpose AI models’ (i.e., ‘foundation’ 
models such as many GenAI models), and in particular for those models that could 
pose systemic risks and have significant impacts on the European market. 

Now that the AI Act has passed into force, focus will shift from how to regulate 
AI  to  implementing  the  AI  Act.  These  different  emphases  are  reflected  in  the 
division of labour by CoR commissions.  

The CoR Commission for Social Policy, Education, Employment, Research and 
Culture (SEDEC) is concerned with the development and regulatory framework 
of AI technologies (see in particular European Committee of the Regions, 2021, 
2024a).  The  CoR  Commission  for  Economic  Policy  (ECON)  focuses  on  the 
implementation of AI at the local and regional level and its implication for digital 
policy.  LRAs  can  benefit  from  guidance  from  the  ECON  Commission.  In 
implementing the AI Act, LRAs should identify  AI technologies’ opportunities 
and challenges and prepare themselves accordingly. 

In summary, this study analyses and builds on the results of previous publications, 
and delves deeper into the opportunities and challenges LRAs have with AI and 
GenAI  adoption  through  analysis  of  responses  from  a  dedicated  survey  and 
development of case studies. The information provides insights into the impact of 
AI on the quality of LRAs’ interactions with citizens, as well as the efficiency of 
internal  and  cross-administration  processes.  Reflecting  on  these  findings,  the 
report considers how AI and GenAI could contribute to bridging digital divides 
and examines the role of political leaders in promoting AI and GenAI, concluding 
in a set of policy recommendations tailored to policymakers at the EU, national 
and subnational level. 

13 

 
 
 
 
 
 
 
Part 1: State of play of AI and GenAI adoption at local and regional 
level 

1.1 Overview 

This literature review provides a high-level summary of research on the use of AI 
in the public sector. The sources include academic publications and select reports 
from key international organisations. When possible, the sources focus on GenAI 
research  or  issues  faced  by  LRAs  and  municipalities  when  developing  and 
adopting AI.  

Section  1.1.1  begins  with  a  summary  of  research  on  how  AI  has  been,  and  is 
currently,  used  in  public  sector  contexts,  along  with  research on  the  perceived 
benefits  of  public  sector  AI.  Section  1.1.2  provides  information  about  select 
infrastructure  and  initiatives  available  to  support  LRAs  with  the  successful 
adoption of AI. Section 1.1.3 is an overview of the academic research on public 
sector AI, including discussions on analytical trends and enablers and barriers to 
AI adoption. Section 1.1.4 discusses research on citizen acceptance and perceived 
risks of public sector AI. Section 1.1.5 summarises research on regulatory and 
policy perspectives. 

1.1.1  Local  and  Regional  Authority  (LRA)  and  Public  Authority 
(PA) implementations 

Research on the use of AI in the public sector reveals that, despite widespread 
expectations for perceived benefits, the use of AI by governments, particularly 
municipalities, is limited both in terms of number of initiatives and early-stage 
deployment (Selten & Kleivink, 2024; Mikalef et al., 2019; Mikalef et al., 2021; 
van  Noordt  &  Misuraca,  2022).  This  is  due,  in  part,  to  a  tension  between  the 
‘flexibility and agility that are needed to explore and adopt AI and, on the other 
hand,  the  formal  and  rigid  bureaucratic  structures  and  mechanisms’  of  public 
institutions (Selten & Kleivink, 2024). 

To  date,  research  finds  that  public  sector  use  of  AI  has  focused  primarily  on 
automating  simple  and  repetitive  tasks,  predictive  analytics  (e.g.,  in  law 
enforcement and financial fraud detection), and facial recognition for surveillance 
and security (e.g., Selten & Kleivink, 2024; Qin & Zi, 2024).  However, the recent 
wave  of  AI  systems  using  machine  learning  allows  for  more  complicated  and 
sophisticated uses (e.g., Young et al., 2019). Furthermore, even in the absence of 
clear or formal AI policies at the organisational level, civil servants are informally 
using GenAI tools for a range of functions (Cantens, 2023). AI for surveillance, 
in particular, has received attention, as it is widely used by public authorities and 

14 

 
 
 
 
 
 
 
raises significant privacy and ethics concerns for the general public (Ardabili et 
al., 2024; Floridi, 2024; Salgado et al., 2024). 

The public sector is motivated to develop and adopt AI technologies primarily 
because  of  the  perceived  benefits.  Those  benefits  include  perceived  financial 
advantage through efficiencies (in personnel and resources), enhanced decision-
making  and  support  for  policy  initiatives,  and  improved  service  delivery  to 
citizens  due  to  AI-enabled  accessibility  and  inclusivity  capabilities  (Qin  &  Zi, 
2024;  Kreuels  et  al.,  2021).  Referring  to  GenAI  specifically,  Cantens  (2023) 
argues that AI can have a ‘transformative impact on intellectual production of the 
state’ due to improvements in the analytical and critical faculties of civil servants 
and  greater  equality  in  policy-making  globally.  Additionally,  LRAs  may  be 
motivated  to  explore  potential  AI  technologies  because  of  perceived  pressure 
from industry, national government, and the general public (Kreuels et al., 2021). 
In the eyes of various stakeholders, the State should do both: innovate and use its 
resources to deploy technologies to gain operational efficiencies. 

However, research on whether, and to what extent, these perceived benefits are 
actualised is limited. Selten & Kleivink (2024) suggest success may depend, in 
part, on how exactly AI is adopted within the public organisation. AI teams that 
are structurally separate from other departments may develop technically more 
robust AI systems, but the systems may not align with operational needs, or be 
compatible  with  existing  IT  infrastructure;  integrating  AI  expertise  across 
departments does not face the same challenges but the AI solutions rarely progress 
beyond initial conception or small-scale piloting (Selten & Kleivink, 2024). In 
either case, further research is needed to better understand which organisational 
conditions create better opportunities to benefit from the use of public sector AI.  

GenAI  has  received  some  attention  for  its  proposed  ability  to  transform 
government functioning  - raising efficiency and increasing the effectiveness of 
public service delivery. However, unlike general AI, there is very little evidence 
of GenAI adoption by LRAs, and research is scarce on adoption success factors 
or challenges specific to GenAI. Where it has been studied, civil servants seem to 
be informally using GenAI tools (Cantens, 2023). One example of a formal study 
in  the  United  Kingdom  found  that  GenAI  was  prevalent  across  government 
functions,  with  22  percent  of  public  sector  professionals  reporting  they  used 
GenAI tools (Bright et al., 2024). The researchers found that due to the accessible, 
often browser-led,  proliferation  of GenAI  tools  (e.g.,  Chat GPT,  Google  Bard, 
Microsoft Copilot, etc.), government employees were beginning to integrate the 
functions  into  their  daily  work  practice  –  leading  to  perceived  (and  actual) 
efficiency gains. The researchers noted how this type of deployment is different 
from formal top-down deployment approaches, where strategies and funding are 
decided  through  formal  processes,  policies,  and  projects.  While  the  informal 

15 

 
 
 
 
uptake of GenAI is improving agency and flexibility for government employees, 
this bottom-up approach was not always viewed as entirely beneficial, specifically 
where there is a lack of clarity regarding oversight and guidance on appropriate 
use (Bright et al., 2024).  

The limited evidence of ongoing deployments of GenAI at the local or regional 
level could be attributed to a) a lack of acknowledgement, or awareness, of how 
GenAI tools are starting to become embedded through informal work practices, 
b)  the  fact  that  many  AI  projects  are  at  an  early  stage  of  development  and 
deployment, and there has not been sufficient time for reflection and analysis, or 
c)  a  lack  of  top-down  formality  being  affected  within  local  or  regional 
departments.  

This  might  be  due  to:  trust,  confidentiality,  accountability  concerns  (Cantens, 
2024); a lack of research and innovation projects that have moved past very initial 
prototyping  or  research  ideation  phase  (Williams  et  al.,  2024);  or  ongoing 
concerns  over  the  interaction  between  the  EU  AI  Act  and  governmental 
accountability,  as demonstrated  by  the  Kortrijk use  case within  this  study  (see 
Case II.1).  

Despite little published evidence of GenAI adoption and benefits at the local or 
regional  level,  the  potential  benefits  of  its  adoption  are  widely  discussed.  For 
example, the Organisation for Economic Cooperation and Development (OECD) 
has  outlined  how  GenAI  might  support  efforts  towards  anti-corruption  and 
governmental  integrity  (OECD,  2024),  finding  evidence  within  enforcement, 
investigative  and  tax  audit  functions  of  government  across  the  EU.  Though,  it 
should be said that the vast majority of examples were at the exploratory stage, 
and perhaps this is why evidence of this did not appear through our surveying. 
The OECD also highlights the Netherlands National Strategy for the Adoption of 
GenAI (2024) as being a good example of how top-down approaches might be 
most suitable, given the rapidly evolving nature of the technology, and its well 
communicated  risks,  and  potential  consequential  impacts,  both  positive  and 
negative. National governments equipping local and regional governments with 
the  ability  to  assess  and  adapt  to  these  impacts  might  improve  the  overall 
deployment of GenAI tools into sub-national governmental regions.  

1.1.2 Existing AI infrastructure to support LRAs 

There  are  several  existing  European  initiatives  that  intend  to  support  LRAs  in 
developing  and  adopting  AI.  These  initiatives  prioritise  knowledge  sharing, 
networking  and  tools  for  trustworthy  AI,  built  on  strong  European  ethical 
principles. 

16 

 
 
 
 
 
 
 
AI4Gov Project 

The ‘Trusted AI for Transparent Public Governance fostering Democratic Values’ 
(acronym AI4GOV) project is a European project funded under the EC Horizon 
Europe call Artificial intelligence, big data and democracy. The project, which 
runs from 2022-2025, aims to develop evidence-based innovations, policies and 
policy recommendations to mitigate the negative impacts of Big Data and AI, and 
increase  citizen  trust.  Outputs  from  the  project  will  include  a  Reference 
Framework  for  Ethical  and  Democratic  AI,  AI  explainability  techniques,  AI 
fairness monitoring and bias mitigation tools, and training for stakeholders.  

AI4Gov Hub 

AI4Gov  Hub  is  ‘a  leading  international  ecosystem  of  research,  training,  and 
innovation opportunities aiming to use AI to improve public services’. Its goal is 
to empower public administration to use human-centric AI to transform public 
service delivery. AI4Gov hosts events, a Knowledge Hub for shared research and 
peer  learning,  and  a  Master’s  Programme  on  AI  in  Public  Services  (hosted  at 
Universidad Politécnica de Madrid and Politecnico di Milano; co-financed by the 
Connecting Europe Facility of the EU).  

AI4Europe 

Building  on  the  work  of  the  AI4EU  project  (which  ran  from  2019-2021), 
AI4Europe  project  (2022-2025)  is  the  ‘enabling  engine’  behind  the  AI-on-
Demand  Platform  (AIoD),  the  first  European  AI  On-Demand  Platform  and 
Ecosystem.  It  is  funded  under  the  call  European  coordination,  awareness, 
standardisation  and  adoption  of  trustworthy  European  AI,  Data  and  Robotics. 
AIoD  is  intended  to  be  a  resource  for  knowledge  sharing  to  facilitate 
experimentation and the development of solutions and technologies. AIoD ‘will 
offer  interoperable  services,  data  and  tools  from  several  related  projects  and 
to  facilitate  research  productivity, 
communities,  and  provide  solutions 
reproducibility,  and  collaboration’.  AI4Europe 
is  establishing  support 
mechanisms to foster engagement between academia and industry.  

GovTech Network 

GovTech  Network  is  a  global  cross-sectoral  initiative  at  the  World  Economic 
Forum  (WEF)  focused  on  exploring  the  effective  and  responsible  use  of 
technology for public service delivery. The network is open to members of the 
WEF (through an annual membership fee) representing government, industry and 
experts. The global network aims to connect national GovTech Centres into an 

17 

 
 
 
 
 
 
 
 
innovation ecosystem. The network publishes briefing papers on a broad range of 
issues related to technology and government.  

GovTech4All 

GovTech4All  project  is  an  EU-funded  project  that  aims  to  foster  a  cohesive 
GovTech ecosystem across multiple countries. The project will develop a pan-
European GovTech market and ecosystem, aimed at delivering innovative digital 
public services for European citizens, residents, and beyond. The project has three 
primary pilot projects: a Secure Cross-Border  Information Sharing initiative to 
enhance secure communication and data sharing at borders; a Personal Regulation 
Assistant  to  help  citizens  apply  for  social  benefits;  and  a  GovTech  Start-Up 
challenge for energy efficiency solutions at the municipality level. GovTech4All 
also runs a ‘Boot Camp’ for European start-ups to develop and scale government 
technology solutions.  The GovTech4All community is open to all stakeholders, 
offering  a  chance  for  public  sector  representatives  to  connect  and  learn  best 
practices from industry and other public administrations. 

AI Skills Academy 

AI  Skills  Academy  is  an  independent  resource  offering  online  courses  on  AI. 
Their mission is ‘to empower individuals and organisations with cutting-edge AI 
skills  tailored to specific  roles  and industries’.  The  course  curriculum includes 
foundational knowledge and hands-on experience.  

ARISA AI Skills Training 

The  Artificial  Intelligence  Skills  Alliance  (ARISA)  is  a  network  of  European 
organisations focused on training in AI and connected disciplines. The network is 
co-funded  by  the  EU  and  is  part  of  the  EC  Pact  for  Skills  initiative.  ARISA 
provides a platform for knowledge sharing and capacity building, as well as skills 
training in the form of an online course offering a ‘comprehensive, yet accessible’ 
overview of AI. The free, 12-week course covers core AI concepts, key players 
and  trends  in  the  AI  ecosystem,  and  concerns  such  as  transparency  and  data 
privacy. The course is marketed towards all stakeholders, including policymakers.  

Digital Innovation Hubs 

European Digital Innovation Hubs exist at both the EU and national level. The 
Hubs  are  designed  to  serve  as  ‘one-stop  shops’  for  companies  and  SMEs  to 
receive information and support in the design and deployment of technologies, 
including AI for the public sector. The European Digital Innovation Hubs, part of 
the  Digital  Europe  Programme,  bring  together  and  support  national  Hubs, 

18 

 
 
 
 
 
 
 
 
 
increasing their capacity and enabling knowledge building and transfer across the 
EU. With this wider network, the EC wants to foster networking, cooperation, and 
knowledge transfer activities between the private and public sectors.  

1.1.3 Academic research 

While the number of publications in recent years on public sector AI is indicative 
of increasing interest in the topic, literature on AI in the public sector is limited. 
The following sections provide an overview of existing literature, with a focus on 
analytical  trends,  enablers  and  barriers  to  AI  adoption,  research  on  citizen 
acceptance  and  perceived  risks,  and  research  on  regulatory  and  policy 
perspectives.  

Systemic reviews 

There are some literature reviews (e.g., de Sousa  et al., 2019; Reis, Santo, and 
Melão,  2019;  Pencheva  et  al.,  2020)  and  case  studies  of  specific  tools  and 
initiatives in early stages of development and piloting (e.g., G20 DEWG, 2024; 
Androniceanu,  2023;  Neumann  et  al.,  2024;  Sun  &  Medaglia,  2019).  These 
landscape analyses of AI deployment in the public sector show that few public 
administrations have actually ventured into AI (van Noordt and Misuraca, 2022). 
There is no literature or online resource offering a comprehensive summary  or 
catalogue of public sector AI projects around the world. As such, ‘[t]here is still 
a  gap  in  terms  of  identifying  and  characterising  the  main  actors,  governance 
structures, and related policies’ (Criado, 2024).  

Most of what is published come from a theoretical perspective (e.g., Wirtz et al., 
2019; Zuiderwijk et al., 2021) and focus on the perceived challenges and barriers 
(technological, legal and regulatory, normative and societal) to AI deployment in 
the public sector (Wirtz et al., 2019). Medaglia et al. (2021) found that studies 
grouped around seven topics: (1) AI definitions and attributes; (2) AI techniques 
and  methodologies;  (3)  AI  uses  and  applications;  (4)  AI  results,  impacts,  and 
benefits; (5) AI challenges and determinants; (6) AI strategies, best practices, and 
guidelines; and (7) ethical considerations about AI.  

Furthermore,  articles  focus  on  the  ‘macro’  governance  level  (international, 
regional  and  national)  and  do  not  often  investigate  issues  at  the  local  or 
organisational level. While these conceptual frameworks and case studies provide 
valuable insights (Selten & Kleivink, 2024), there is a need for more research on 
how  LRAs  use  (or  plan  to  use)  AI,  and  the  challenges  and  benefits  at  an 
organisational and individual level.   

19 

 
 
 
 
 
 
 
 
Two additional considerations also characterise existing research on public sector 
AI.  First,  much  of  the  discourse  is  ‘fragmented  and  predominantly  defensive’ 
especially in regard to GenAI (Cantens, 2023). Second, most research makes clear 
that it considers AI to be not ‘just another’ IT innovation, but rather something 
distinct and more significant due to the pace, extent, and nature of change that AI 
might bring to the public sector (Selten & Kleivink, 2024). 

Enablers and barriers for public sector AI adoption 

Existing  literature  identifies  conditions  and  characteristics  that  impact  the 
effectiveness of AI uptake in the public sector. When present, these conditions 
and characteristics become enablers of public sector AI; when not present, they 
can be seen as barriers.  

One condition is an AI system’s compatibility with existing IT systems (or the 
LRA’s willingness and ability to update existing IT infrastructure), identified by 
Kreuels et al. (2021) as prerequisite. Other conditions related to resources include 
personnel with technical AI competencies, staff capacity to manage AI projects, 
and financial assets to support a project to fruition (Kreuels et al., 2021; Mikalef 
et al., 2021). 

Due to the perceived risk of AI and discourse around potential harms, ‘AI can be 
expected  to  pose  more  operational  and  strategic  challenges  for  public 
organisations’ as compared to other IT tools (Selten & Kleivink, 2024). Therefore, 
additional  conditions  of  successful  adoption  are:  proposed  AI  initiatives  are 
strategically aligned with organisational and/or departmental needs; staff have a 
shared common understanding and acceptance of AI technologies; and a certain 
level  of  ‘organisational 
towards  AI  as  well  as  digital 
transformation’, particularly from top-management and leadership (Kreuels et al., 
2021; Mikalef et al., 2021; Neumann et al., 2024; Sun & Medaglia, 2019; Vogl, 
Seidelin,  Ganesh,  and  Bright,  2020).  Misalignment  within,  and  across, 
departments (e.g., data science departments and IT departments) and between data 
scientists  and  domain  experts  must  also  be  avoided,  or  minimised,  especially 
when  IT  departments  are  resistant  to  change,  or  subject  matter  experts  are 
reluctant to use (or do not see value in) AI tools (Selten & Kleivink, 2024). 

innovativeness 

Regulatory and legal concerns are not explored in the literature, suggesting that 
they do not pose as significant of a challenge to public sector AI in comparison to 
the internal organisational barriers (Selten & Kleivink, 2024). 

20 

 
 
 
 
 
 
 
 
 
1.1.4 Citizen acceptance and user experience with AI in the public 
sector 

Citizens can be both subject to the use of AI by public servants and users of AI-
enabled  public  services.  In  both  contexts,  concerns  about  the  use  of  AI  in  the 
public sector (closely connected to general concerns about AI) exist, relating to 
issues  such  as  explainability,  confidentiality,  authorship  and  ownership,  and 
machine error resulting in harm (Cantens, 2023). These general public concerns 
may present a significant hurdle to successful implementation and uptake of AI 
within LRAs (e.g., Galloway & Swiatek, 2018 on ‘AI anxiety’; Kowalkiewicz & 
Dootson,  2019,  discussing  how  digital  sanitisation,  or  the  removal  of  human 
interaction  in  service  delivery,  is  not  welcomed  by  citizens).  Understanding 
citizen acceptance, and the roots of existing concerns about the use of AI in public 
services is important. However, it is suggested there is a knowledge gap in what 
is known about citizens’ acceptance and user experience with  AI  in the  public 
sector  (Gesk  &  Leyer,  2022,  Chen  et  al.,  2021).  Whether  existing  (or  future) 
deployments  in  the  public  sector  can  bridge  this  knowledge  gap  is  somewhat 
unknown, especially as user feedback is not necessarily collected in a formalised 
manner within a notable number of public sector innovation projects. 

Researchers have identified different factors that influence citizens’ perception of 
AI within public services. Factors that positively influence citizens’ perception of 
AI in public services include perceived usefulness, personalisation capabilities, 
aesthetics of the user interface, level of trust in government, whether there is a 
‘human-in-the-loop’, and level of knowledge of AI technologies (Gesk & Leyer, 
2022; Chen et al., 2021; Schmager et al., 2023; Willems et al., 2023). Factors that 
negatively  influence  citizens’  perception  include  fear  of  AI  failure,  lack  of 
transparency, lack of citizen participation in decisions about AI, privacy concerns, 
and perceived time spent on the system (Gesk & Leyer, 2022; Chen et al., 2021; 
Schmager et al., 2023; Willems et al., 2023). Furthermore, Kleizen et al. (2023) 
found  that  already  existing  attitudes  and  experiences  (e.g.,  existing  privacy 
preferences, trust in government, trust in AI) have more impact on the general 
public’s level of trust in governmental AI projects, regardless of how the public 
authority communicated ethics considerations.  

Research  studies  have  solicited  direct  feedback  from  citizens  on  their 
acceptability of public sector AI to identify these factors. Gesk & Leyer (2022) 
investigated  the  general  public’s  acceptance  by  asking  citizens  in  Australia  to 
choose between a human, or AI-based software, to perform a public service. Then, 
they  were  asked  to  complete  a  questionnaire  on  their  acceptance  of  AI  (e.g., 
usefulness, time savings, transparency). They found a higher rate of acceptance 
when AI is used for general services, as opposed to specific services, in the public 
sector and that citizens did not believe AI could handle ‘exceptional concerns.’ 

21 

 
 
 
 
Factors that negatively influenced on public acceptance of AI included: fear of AI 
failure;  lack  of  transparency;  and  lack  of  citizen  participation  in  the  choice  of 
design and use of AI. The researchers also found that citizens felt they do not have 
‘much of a choice regarding the introduction of AI’.  

Chen et al. (2021) asked citizens about the user experience with AI-enabled self-
service machines in a public service centre in China. They found that users related 
positively to personalisation capabilities and aesthetics, and related negatively to 
perceived  time  spent  on  the machines. The  researchers  also noted that  citizens 
were more likely to report a pleasant experience using the AI-enabled technology 
if they also reported a higher level of trust in government more generally.  

Schmager et al. (2023) surveyed Norwegian citizens on their comfort level and 
experience  using  a  potential  AI-enabled  system  in  public  services.  The  results 
were generally positive, with almost all participants consenting to the use of AI 
and expressing positive levels of comfort with the use of AI tools in the public 
sector.  The  researchers  identified  three  contributing  factors  for  positive 
acceptance: participants’ self-rated high level of trust in government, the existence 
of a ‘human-in-the-loop’, and transparency about the AI tool.   

the 

to  download 

information  required 

Willems et al. (2023) looked at the acceptance of AI-driven chatbots for public 
services  by  asking  Austrian  citizens  whether  they  would  download  such  an 
application.  To  investigate  privacy  issues,  the  researchers  varied  the  level  of 
personal 
theoretical  application. 
Respondents  were  then  asked  to  rate  the  usefulness  of  and  the  degree  of  their 
privacy concerns regarding the application. The researchers found that perceived 
usefulness was the main factor in citizens’ willingness to download, and privacy 
concerns would reduce their willingness to download, and use, the application. 
However, respondents were willing to ‘trade-off’ general privacy concerns if the 
perceived usefulness was high. The research confirmed the existence of a privacy 
paradox  (i.e.,  inconsistency  between  general  concerns  and  actual  behaviour). 
Supporting  this  theory,  Wang,  Chen,  and  Chien  (2023)  found  that  perceived 
transparency  of  an  AI  system  increases  trust,  while  privacy  concerns  about 
sensitive  information  decrease  trust.  The  researchers  also  found  that  citizens 
familiar  with  AI  are  more  likely  to  trust  the  recommendations  made  by  AI 
platforms for decision-making purposes. 

The literature consistently notes the need for future research to better understand 
citizen perception, and acceptability, of public sector AI. This includes calls for 
further research to inform recommendations for national and local governments 
to  improve  citizen  perception,  and  acceptance of  AI.  For  example,  Osborne  & 
Nasi (2024) suggest further research on the co-creation and co-design of AI as 
innovation methods for the future of public service delivery. 

22 

 
 
 
 
 
Perspectives of Regulation, Law and Policy 

Governance  frameworks  relevant  to  AI  are  multi-level;  they  exist  at  the 
international, regional (including European), national, and local level. There are 
many  articles,  reports  and  websites  cataloguing  these  legal  and  policy 
developments (e.g., Jobin et al., 2019, UN Governing AI for Humanity Report 
2024). 

The literature points to several concerns regarding the regulation of AI globally. 
Predominant concerns focused on the necessity, and desirability, of regulating AI 
at  all,  with  arguments  against  regulation  generally  citing  the  existence  of 
sufficient  non-legal  governance  mechanisms  (e.g.,  self-regulation,  voluntary 
principles,  codes  of  conduct)  and  concerns  about  the  potential  stifling  of 
innovation (e.g., Strous, 2019). However, the emergence of AI regulation over the 
past few years seems to have reduced the efficacy of this argument. 

Looking at new, and developing, AI laws and policies, experts argue there is a 
distinct need to better define AI (e.g., Folberth et al., 2022; Ulnicana et al., 2021). 
Currently, there is no single accepted definition of AI used consistently across 
regulatory, or governance, frameworks, and not enough attention has been paid to 
the difference in typologies of AI systems (Robles Carrillo, 2020). Additionally, 
noting the many forms of regulation, researchers contend the type of regulation 
should, and will, vary depending on the deployment context (e.g., Folberth et al., 
2022). 

While each AI application in the public sector will have unique legal challenges, 
all  ‘must  not  only  conform  to  legal  principles  in  their  design  but  also  in  the 
outcomes they produce’ (Correia et al., 2024). 

AI Act 

The EU AI Act is the first law in the world dedicated to regulating AI. Drawing 
on the EC independent High-Level Expert Group on Artificial Intelligence (AI 
HLEG)  Ethics  Guidelines  for  Trustworthy  AI  (2019)  and  Assessment  List  for 
Trustworthy AI (ALTAI) (2020), the EU AI Act takes a risk-based approach to 
regulating  AI,  laying  out  legal  obligations  related  to,  for  example,  risk 
management, fundamental rights, transparency, and human oversight.  

The  AI  Act,  which  came  into  force  in  August  2024,  is  both  welcomed  and 
critiqued by experts and researchers. Proponents argue that the AI Act positions 
the  EU  as  a  global  leader  in  the  development  and  deployment  of  ethical  and 
human-centric AI (e.g., Feldstein, 2024). Critics suggest it will stifle innovation, 
and  point  to  concerns  such  as:  the  conflation  of  ‘trustworthiness’  and 

23 

 
 
 
 
 
 
 
 
‘acceptability’ (Laux et al., 2023); the consideration of the administrative burden 
of  compliance  (Finocchiaro,  2024);  and whether the  AI  Act will do  enough to 
protect fundamental rights and EU values (Finocchiaro, 2024). 

Data protection, governance and sharing 

Data governance is a key area of concern for LRAs, due to the increasing use of 
data-driven technologies, the exacting nature of current data protection regulation 
within  the  EU,  and  the  emerging  regulations  regarding  open  data  and  data 
commons. In the EU, the relevant regulatory frameworks are: the General Data 
Protection  Regulation  (GDPR);  the  Data  Act  (DA);  the  Data  Governance  Act 
(DGA);  the  Open  Data  Directive  (Directive  on  the  re-use  of  public  sector 
information  or  the  PSI  Directive);  and  relevant  national  legislation.  While  the 
GDPR’s primary purpose is the right to privacy and protection of personal data, 
the  DA,  DGA,  and  PSI  Directive  support  the  sharing  of  public  sector  data, 
including to privacy entities. The framework ‘expands and deepens’ data sharing 
based on the Open Data Government movement, but scholars note limited positive 
results due to barriers on both the demand and supply sides. These may include a 
lack  of  opportunities,  motivation  and  ability  to  use  public  data,  and  a  lack  of 
appropriate incentives to share data (Buttow & Weerts, 2024).  

1.1.5 Summary 

The literature shows a growing interest in public sector AI deployment, reflecting 
both  the  transformative  potential,  and  significant  challenges,  the  technology 
brings  with  it.  As  evidenced  by  the  literature,  research  suggests  that  AI  might 
enhance  efficiency,  decision-making,  and  accessibility.  However,  there  are 
concerns about ethics, privacy, accuracy, and trust (in both AI and government), 
especially from citizens. Additionally, research suggests there are resources and 
organisational conditions that must be in place for the successful adoption of AI. 
It would seem that further research is required to fill an existing knowledge gap 
on the AI success factors, to better improve the development and adoption of AI 
in the public sector, particularly on maximising the benefits of AI and addressing 
citizen concerns.  

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
1.2 State of play of AI adoption across the EU LRAs 

This section is aimed at offering an overview of the current state-of-play of AI 
adoption  by  the  public  sector  at  sub-national  level  in  EU  Member  States. 
Considerations are based on the results of an online survey the authors submitted 
to LRAs in all 27 EU Member States and supported by desk research.  

The questions at the core of the survey were structured in three sections targeted 
to gather evidence on the following: 

•  General data on the respondents; 
•  Drivers, barriers, opinions and best practices on the AI adoption by LRAs; and 
•  Views on the future of AI for public administrations to support policymaking. 

To  avoid  selection  bias,  the  selection  of  LRAs  was  done  by  random  sampling 
within each of the 27 EU Member States. Invitations to the survey were sent by 
email to employees and managers of LRAs, having care to include the personnel 
of departments in charge of IT and digital transition operations. Where possible, 
personal email addresses were preferred over generic email addresses. 

The invitations to the survey were initially directed at the same number of local 
public  authorities  in  all  Member  States.  The  sample  was  then  extended  for 
countries with a lower response rate, in particular for Austria, Czech Republic, 
France  and  Italy.  The  consultation  was  launched  on  2nd  September  2024  and 
concluded on 15th  October 2024. The survey received a total of 153 responses 
from all 27 EU Member States.  

The  analysis  of  the  survey  results  was  carried  out  considering  the  findings  of 
recent research on the use of AI in the public sector and the implications of the AI 
Act. With the aim of gathering evidence on the drivers and barriers to the adoption 
of  AI  at  local  level,  the  data  are  elaborated  primarily  in  relation  to  the 
demographic size and the geographical area in which LRAs are located. 

25 

 
 
 
 
 
 
 
 
 
 
1.2.1 AI adoption, application sectors and main benefits for LRAs 

Figure  1  provides  an  overview  of  the  adoption  of  AI  and GenAI  solutions  by 
LRAs within the sample of respondents. 

Figure 1. Proportion of LRAs adopting AI within the sample of respondents 

Figure  1  shows  how  the  majority  of  LRAs  responding  to  the  survey  are  not 
adopting  AI  or  GenAI  solutions.  The  information  on  the  lower  rate  of  LRAs 
adopting  AI  is  in  line  with  the  expectations  and  with  background  literature. 
However, it is worth noting how the percentage of those adopting this technology 
reaches  around  27%  of  respondents,  thus  pointing  to  an  emerging  level  of 
adoption across the EU.  

26 

 
 
 
 
 
 
 
 
 
Figure 2. Number of LRAs adopting AI by European region 

Looking at AI adoption cases by European region (Figure 2), the survey results 
show  that  the  highest  number  of  cases  is  recorded  in  Western  Europe  (18), 
followed by Northern Europe (11), Southern Europe (7) and finally Central and 
Eastern Europe (5). These figures are consistent with the differences in the degree 
of  digital  maturity  amongst  European  regions.  The  low  adoption  rate  of  AI 
technologies  in  Central  and  Eastern  Europe  is  in  line  with  data  on  digitisation 
performance  summarised  by  the  Digital  Economy  and  Society  Index  (DESI) 
indicators  (EC,  2022)  that  see  countries  in  this  geographic  area  registering 
amongst  the  lowest  levels  of  indicators  on  ‘AI  adoption  rate’  and  on  ‘Digital 
public services for citizens’, the latter being a proxy for policy advancements in 
the digital field. 

The  following  graphs  show the  data on AI use divided by  Member States  and 
demographic groups of the LRAs responding. 

27 

 
 
 
 
 
 
Figure  3.  LRAs  adopting  AI  by  Member  State  within  the  sample  of 
respondents 

In line with the PSTW results, the countries with most AI use cases are Spain and 
Denmark, followed by Belgium and Sweden. In contrast, other countries that in 
PSTW were found to have the most cases of AI use in the public sector at national 
level,  turned  out  to  have  few  cases  of  implementation  at  the  local  level  (e.g., 
Germany, the Netherlands and Portugal). 

28 

 
 
 
 
 
 
 
Figure 4. LRAs adopting AI by demographic group  

Looking at adoption rates in local governments divided by demographic group, in 
Figure 4 it can be seen that LRAs where AI is most widely used are the largest 
ones. More specifically, in the group of LRAs with a number of residents between 
100,000 and 500,000, AI is adopted by about 54% of respondents. In the largest 
demographic  group  (population  range  between  500,000  and  2  million),  AI  is 
adopted by 6 out of 7 LRAs in the sample. 

Sun & Medaglia (2019) recognise how the public sector is one of those that can 
benefit  the  most  from  the  use  of  AI,  and  different  studies  investigate  the 
implementation of AI in public authorities of different administrative levels. To 
date,  research  shows  that  AI technology  is  most widely used in  administrative 
authorities  at  national  level,  followed  by  local  and  lastly  regional  authorities. 
Concerning  the  adoption  of  AI  by  local  public  authorities,  according  to 
Yigitcanlar et al. (2024), the earliest use case was in 2004. The review finds that 
the level of implementation of AI by local governments varied over time, with a 
constant increase in the number of cases registered globally since 2014, a doubling 
of cases was recorded in the years between 2017 and 2018, and a peak of cases 
was observed in 2021. On the other hand, the number of AI adoption cases seems 
to decrease from 2021 to 2024. 

29 

 
 
 
 
 
 
Certainly, the degree and type of AI use are not fortuitous, but  many factors lead 
a local public authority to decide to adopt such technology. Influencing factors 
can  stem  from  supra-national  trends  as  well  as  from  specific  characteristics  of 
local  territories.  As  explained  by  Yigitcanlar  et  al.  (2024),  it  is  intuitive  to 
understand how the use of a particular AI system depends on the specific needs 
and priorities of a locality. Different localities have different needs based on their 
intrinsic  characteristics  such  as  geographic  location,  population  size,  core 
economic activity and social demographics. Also, resource availability cannot be 
overlooked: budget, technological infrastructure and human capital are crucial in 
the  process  towards  the  adoption  of  AI  technology.  Moreover,  as  with  any 
innovative  element,  the  regulatory  and  policy  frameworks  have  the  power  to 
influence  the  introduction  of  AI  in  the  public  sector,  both  at  national  level  by 
setting priority objectives and resource allocation, and locally, by shaping local 
governance structures and directing political dynamics. 

The survey data reflect the combination of these dynamics, and the analysis of the 
responses from the LRAs on key aspects provides a more complete picture of the 
factors  that  most  induce  or  hinder  a  local  authority  from  exploiting  this 
groundbreaking technology. 

One of the first aspects examined in the survey is the application sector of AI, 
where  the  sectors  were  selected  based  on  the  taxonomy  proposed  by  PSTW 
(2024). In this survey question, respondents were  able to select more than one 
application sector. Consequently, the number of options elaborated exceeds the 
number of LRAs responding. The fact that many municipalities indicated more 
than one application sector for AI also reflects an experimentation process where 
AI is tested for different purposes to assess benefits and obstacles. 

This variety of application sectors allows for a more complete overview of all uses 
for which the technology is employed within local government. 

The sectors in which AI is most used are in order of frequency in which they are 
mentioned: 

1)  ‘AI-based services for the public administration’;  
2)  ‘Municipal public services’; 
3)  ‘AI-based services for citizens’ and ‘Mobility and transport’.  

Cases in the two categories of sectors where AI is most frequently used are found 
in a similar proportion in all four European regions (reference was made to the 
classification of European regions proposed by EuroVoc3, i.e., Western Europe, 

3 Central and Eastern Europe: BG, CZ, HR, HU, PL, RO, SI, SK; Northern Europe: EE, FI, IE, LT, LV, SE; 
Western Europe: AT, BE, DE, DK, FR, LU, NL; Southern Europe: CY, EL, ES, IT, MT, PT. 

30 

 
 
 
 
 
 
 
 
Central  and  Eastern  Europe,  Northern  Europe,  and  Southern  Europe).  It  is 
interesting to compare this information with the sectors in which AI is most often 
used by administrations at national level. According to recent data released by the 
European Commission (2024), also in national initiatives AI is most often used in 
the  sectors  of  ‘General  Public  Services’  and  ‘Economic  Affairs’  (the  latter 
including  transport  and  mobility  services).  The  difference  with  respect  to  the 
national  data,  however,  is  the  lower  percentage  of  AI  adoption  in  the  areas  of 
health and education in local governments.  

Figure  5  illustrates  a  comparison of the  application  sectors  and benefits  of AI 
stated by the LRAs already adopting AI, divided by European region. 

Figure 5. AI application sectors by European region 

Data in Figure 5 show that in all European regions (except Central and Eastern 
Europe) the sector in which AI is most frequently used is ‘AI-based services for 
the public administration’, followed by ‘Municipal public services’ in Western 
and Northern Europe and by ‘AI-based services for citizens’ and 'Mobility and 
transport'  in  Southern  Europe.  While  there  is  a  preponderance  of  application 
cases in municipal public services, such as waste collection, road maintenance, 
this result indicates different secondary priorities in Southern Europe compared 
to Western and Northern regions.  

31 

 
 
 
 
 
 
Even  though  the  response  rate  does  not  allow  for  statistically  significant 
conclusions, it can be noted that data shows how in Central and Eastern Europe 
the application sectors are evenly distributed amongst those most frequently used 
in the rest of the continent.  

The data on the use of AI in the ‘Energy and sustainability’ sector, which is 
proportionally  more  widespread  in  Northern  Europe,  stands  out,  possibly  as  a 
result  of  a  long  tradition  and  attention  to  environmental  protection.  The  least 
frequent application sectors are in ‘Research’, ‘Public order and safety’, ‘Social 
protection’ and ‘Communication’. The results for these sectors can also be related 
to the other factors. For instance, ‘Research’ and ‘Communication’ would require 
the  use  of  GenAI;  however,  as  previously  mentioned,  its  complexity  has 
implications that are currently not governable by local authorities, and/or the use 
of GenAI is widely informal and unreported. On the other hand, for sectors such 
as ‘Public order and safety’ and ‘Social protection’, the processing of sensitive 
data raises concerns about possible legal consequences if not managed well.  

Figure 6. AI benefits by European region 

Figure 6 offers an overview of the main benefits that LRAs identify thanks to the 
use  of  AI.  The  options  are  in  line  with  the  ones  considered  by  the  European 
Parliament (2021), including: efficiency gains and internal process optimisation; 
delivery of  more  accessible and inclusive services; personalisation; increase of 

32 

 
 
 
 
 
anticipatory  governance;  policy  through  more  accurate  predictions;  and  less 
human error and fraud. 

The three areas in which AI appears to support the most public administrations 
are  the  following,  in  order  of  frequency  in  which  they  were  mentioned  by 
respondents: 

1)  To optimise internal management; 
2)  To improve the delivery of public services; 
3)  To enhance decision-making processes. 

Disaggregating  these  data  by  European  regions,  it  can  be  seen  that  the 
identification  of  the  optimisation  of  internal  management  as  a  main  benefit  is 
more frequent in Northern and Western European countries. In addition, most of 
the  LRAs  indicating  the  improvement  of  public  service  delivery  as  the  main 
benefit  are  from  Western  Europe.  The  use  of  AI  to  enhance  decision-making 
processes is  proportionally  more  frequent  in  Southern  Europe.  In line  with the 
results on AI application sectors, the least frequent benefit reported by LRAs is in 
‘Communication’.  

The results on the main benefit of AI recognised by respondents are found to be 
consistent with the AI application sectors. More specifically, AI seems to be used 
more to improve the internal organisation of local administrations, and only in 
second  place  to  improve  or  extend  the  range  of  services  offered  to  the  public. 
Moreover, AI seems to be used and benefited more in areas that are cross-cutting 
rather  than  in  specific  fields  of  intervention,  such  as  health  care  or  decision-
making (with reference to the benefits noted by the LRAs). 

While  interpreting  the  results  with  caution  due  to  the  different  number  of 
responses from each region, it is possible to make considerations on results by 
geographical area. It is noted that in Northern Europe the application sectors and 
recognised benefits of AI are more numerous, signalling a greater awareness of 
the theme. Furthermore, there are commonalities between Northern and Western 
European LRAs, where the two most frequent application sectors coincide and 
both areas use AI mainly to optimise the internal management. These are also the 
only two regions that reported to use AI in the area of communication. LRAs in 
Southern Europe also use AI in many sectors, but unlike Northern and Western 
Europe  the  benefit  they  derive  from  is  equally  distributed  amongst  the  three 
proposed in the survey. This is in line with a possible process of experimentation 
of the technology in order to understand which purposes are best pursued through 
AI. 

33 

 
 
 
 
 
 
 
1.2.2  Human  resources  dedicated  to  AI  and  relations  with  the 
external environment 

This section is dedicated to a discussion of the role of human  resources in the 
processes of initiating and managing AI solutions within local public authorities 
and its implications for relations with external stakeholders. Evidence from the 
survey have been elaborated and used to make considerations on the extent and 
qualification of human resources currently dedicated to the implementation of this 
technology by LRAs. 

As  found  in  the  JRC  study  on  AI  for  interoperability  (Tangi  et  al.,  2023),  a 
decisive  determinant  for  the  adoption  of  AI  or  GenAI  solutions  by  a  public 
administration  is  the  role  of  the  initiator,  often  identified  in  an  individual  or  a 
small group of employees.  

Analysing  the  responses  provided  by  LRAs  that  are  already  adopting  AI,  the 
professional who most often promotes AI uptake is a manager (in 29% of cases), 
a  figure  internal  to  the  public  authority.  The  second  category  most  often 
mentioned as initiator  is the residual ‘Other’. In specifying this answer, many 
respondents stated that AI was promoted by the Digital Department, thus again 
professional  figures  internal  to  the  public  administration.  Following  this,  the 
importance of the individual initiative of an expert in the field is noted. In 8 out 
of 41 cases, the expert was declared as initiator. On the other hand, ‘partnership’ 
and ‘in-house agency’ categories appear less common as initiators of AI adoption. 
This may be due to the fact that most municipalities are small-sized and do not 
have many partnerships and in-house agencies located in them, as compared to 
larger municipalities which have more resources.  

With regard to geographical distribution, the ‘manager’ and the ‘partnership’ are 
the only AI promoters found in all observed European regions. The role of the 
‘individual expert’ is also fairly transversal geographically as an initiator, being 
found in three of the four geographical areas of Europe. Conversely, the category 
of ‘inhouse agency’, besides being less widespread, appears more specific to the 
local context and, in particular, more considered in Northern Europe. 

The  fundamental  role  of  internal  personnel  in  promoting  it  makes  the  need  to 
examine the human resources specifically dedicated to AI even more evident. The 
following graph in Figure 7 shows the percentage of LRAs that are dedicating 
specific  human  resources  to  one  or  more  phases  of  AI  implementation.  The 
information indicates the level of awareness of AI and the availability of financial 
and human capital within local public authorities across Europe.  

34 

 
 
 
 
  
 
 
 
Figure 7. LRAs dedicating or not human resources to AI solutions, broken 
down by size 

Overall, the numbers of LRAs that have a dedicated AI office or person and those 
that do not, are balanced in the sample (19 and  22  answers, respectively).  The 
graph also shows the percentages of LRAs with human resources dedicated to AI 
and  the  demographic  size  of  the  municipality.  Given  the  different  number  of 
responses  in  each  demographic  group,  the  data  are  analysed  in  percentages  in 
order to appreciate the dynamics within each group. From the data in the graph, 
the decision to dedicate staff to AI does not appear to be correlated with the size 
of the local authority, unlike as postulated for the AI adoption. The demographic 
groups that appear to devote more staff in percentage terms are the largest and 
small to medium-sized LRAs. On the other hand, it cannot be overlooked that the 
smallest  LRAs  have  the  lowest  rate  of  AI-dedicated  personnel.  Examining  the 
responses of LRAs that have offices and roles dedicated to AI, almost all reported 
that they work within IT and Digitisation Departments. 

Moreover,  the  detail  of  AI-related  skills  available  to  local  administrations  is 
considered. It is known that skills availability is one of the biggest determinant 
and  at  the  same  time  one  of  the  biggest  challenges  in  implementing  AI  in  the 
public  domain.  Data  in  Figure  8  offer  an  insight  into  the  availability  of  skills 
related to AI development and adoption across respondent LRAs. 

35 

 
 
 
 
 
Figure  8.  Presence of  AI-related  skills and  competencies,  broken  down  by 
size   

Again,  no  clear  trend  can  be  seen  with  respect  to  the  relation  of  AI  skills  and 
demographic size of the LRAs. The most widespread competence in LRAs of all 
demographic groups is the ‘Basic knowledge on AI’, which is a percentage more 
prevalent in the smallest LRAs. In the opposite spectrum, the least common skills 
are those on ‘Advanced knowledge of AI’. Consistently with the data on human 
resources  availability,  the  presence  of  advanced  knowledge  of  AI  is  in  fact 
reported in only two LRAs within the small to medium-sized demographic group 
(10,000 to 100,000 residents). The second least widespread skills are those ‘on 
ethical  and  transparency  requirements’  that  are  completely  absent  in  the 
smallest LRAs.  

Considering the presence of digital skills also amongst population groups and the 
potential of  AI to increase  the digital divide, LRAs were  asked  to indicate the 
measures adopted to enable AI to include digitally disadvantaged groups. Figure 
9 below gives a perception of the most and least common actions that LRAs are 
currently considering addressing the digital divide. 

36 

 
 
 
 
 
 
 
 
Figure 9. Measures adopted to enable AI to include digitally disadvantaged 
groups, broken down by European region 

The  graph  above  shows  that  most  respondents  are  not  acting  to  address  the 
differences  in  digital  competencies  amongst  population  groups,  and  the 
percentage  of  those  who  are  not  implementing  any  of  the  listed  actions  is 
particularly high amongst Western European LRAs (54.5%). This result points to 
an area for improvement and offers space to reiterate the cruciality of social and 
public  acceptance  in  adopting  AI.  Looking  at  the  LRAs  that  are  already 
implementing measures to enable AI to include digitally disadvantaged groups, 
the  most  common  is  the  ‘Support  the  affordable  access  to  AI  tools  and 
technologies to help tackle the digital divide’, followed in equal measure by 
‘Online  guidance  and  assistance’  and  ‘Specific  training  to  vulnerable  groups’. 
Survey  results  show  that  the  least  adopted  action  is  the  development  of 
Explainable AI (XAI) systems. XAI is defined as a set of processes and methods 
that enable human users to understand and trust the results and outputs generated 
by machine learning algorithms. XAI is considered fundamental in organisations 
to  create  trust  and  confidence  when  putting  AI  models  into  production.  It  is 
indicative  for  great  potential  for  improvement  its  total  absence  in  Central  and 
Eastern Europe and even more so in Western Europe, where use cases are more 
numerous.  

37 

 
 
 
 
 
Related to considerations on skills availability, there is the decision of a Local 
Public  Authority  to  outsource  services  in  the  AI  development  process. 
Outsourcing  may  be  necessary  due  to  limited  internal  expertise,  and  it  has 
controversial  aspects  since  it  raises  issues  on  transparency  and  data  and  IP 
ownership.  

Figure  10  shows  the  percentage  of  LRAs  that  are  outsourcing  any  AI 
development, divided by demographic group. 

Figure 10. LRAs outsourcing or not AI development, broken down by size 

Unlike  the  data  on  the  availability  of  skills,  a  clear  trend  with  respect  to 
demographic  size  is  evident  in  this  case.  Indeed,  the  data  show  how  the 
percentage of LRAs outsourcing increases with population size, whereas none 
of the smallest LRAs declare to outsource. By combining this information with 
the one on skills availability, it can be noted how the balanced presence of AI-
related skills in the largest LRAs, is combined with a total reliance on outsourcing. 
On the other hand, it is interesting to note how exactly half of the LRAs within 
the  category  with  the  broadest  skills  availability  in  the  sample  (those  in 
municipalities with inhabitants ranging from 10,000 to 100,000) are outsourcing. 
It can be concluded that the percentage of outsourcing by LRAs seems to be more 
correlated with the geographic dimension rather than with its skills availability. 

38 

 
 
 
 
 
 
However, there is certainly a clear benefit in having the right in-house skills to 
better supervise and guide the external providers. 

The involvement of expertise beyond the public administration may be facilitated 
through  either  the  formal  awarding  of  contracts  to  external  providers  or  the 
establishment of strategic partnerships that play a crucial role in developing AI 
systems.  Collaboration  between  public  and  private  sectors  is  encouraged  by 
European institutions and generally deemed as possible catalyst of innovation. In 
the context of AI adoption, amongst the advantages of building partnerships with 
academia  are  the  attraction  of  young  experts  and  the  openness  to  technology 
innovation. Within the private sector, key partners are represented by start-ups, 
also at international level, a category that in Europe faces difficulties in exploiting 
its growth potential, as evidenced by the relatively minority share of total global 
funding for AI start-ups (6% compared to 61% to US start-ups and 17% to those 
in China) (Draghi, 2024).  

From the analysis of the survey results by geographical area of LRAs adopting 
AI, it can be noted that Southern Europe is the geographic area with the highest 
percentage  of  respondents  that  have  developed  partnerships.  Conversely,  in 
Central and Eastern Europe there are no reported cases of partnerships. Western 
Europe is the area with the highest absolute number of LRAs adopting AI and 
here partnerships in local public administrations are present in about one-third of 
cases. 

Listed  below,  are  four  examples  of  partnership  cases  reported  by  respondents. 
These good practices signal collaborations with different categories of private and 
public representatives and highlight territorial specificities across Europe. 

-  The Municipality of Sollentuna (Sweden) reports the collaboration between 
Swedish authorities, municipalities, regions and businesses, coordinated by the 
Sweden’s national centre for applied AI4. The project has the aim to support 
employees in the public sector and promote national collaboration for textual-
based  tasks.  In  more  detail,  the  project  works  on  a  prototype  for  a  digital 
assistant, as a tool to promote the use of AI in the public sector. The aim is to 
design a prototype that is accessible to employees regardless of their technical 
competence  and  to  allow  them  to  devote  attention  to  less  automatic  tasks. 
Another  initiative  by  AI  Sweden  is  ‘Kraftsamling  för  AI  i  kommuner  och 
civilsamhälle’ (‘Gathering Power for AI in municipalities and civil society’). 
The project provided assistance and financial support to municipalities and civil 
society to better understand the opportunities offered by AI in welfare and to 
address societal challenges. 

4 AI Sweden. 

39 

 
 
 
 
 
 
-  Another  example  of  a  multi-sectoral  partnership  is  provided  by  the 
Municipality of Tilburg (the Netherlands) with the MasterMinds initiative. 
The  project  sees  the  participation  of  15  partners  from  academia,  knowledge 
institutions,  the  industrial  and  public  sectors.  MasterMinds  consists  of  5 
research projects united by three main objectives: reducing the territorial gap 
in  AI  technologies,  strengthening  the  collaboration  between  knowledge 
institutions and business partners in the field of AI technologies, and fostering 
innovation. 

-  The City of Tallinn (Estonia) reported different partnerships, including one 
related  to  the  GreenTwins  project.  Within  the  project,  the  collaboration  is 
between  representatives  from  academia  and  the  local  public  sector,  more 
specifically between the City of Tallinn, the Tallinn University of Technology, 
Aalto University and the City of Helsinki. The pilot project is funded by the 
European Regional Development Fund and the Estonian Ministry of Education 
and  Research.  The  main  objective  of  the  initiative  is  to  deal  with  digital 
solutions for modelling blue (i.e., water-related) and green (i.e., land-related) 
networks in the urban environment and to develop software and models that 
provide information on the effects of urban planning to various stakeholders. 
The distinctive trait of the solution is the use of digital tools to promote urban 
vegetation for public well-being and to create physical and virtual spaces to 
allow citizens’ participation. 

-  From Spain, the initiative of the Agency for Technological Modernisation of 
Galicia is worth noting. Within the Agency, the galicIA node was designed as 
a  cooperation  structure  for  the  development  of  activities  that  allow  the 
promotion  of  AI  in  the  Galicia-North  Portugal  Euroregion.  Public-private 
collaboration  promotes  AI  in  the  economic,  educational,  labour  and  social 
fields. At its basis, there is an agreement between public institutions at local, 
national  and  international  levels  (e.g.,  Ministry  of  Economy,  Industry  and 
Innovation, European Grouping of Territorial Cooperation Galicia - Northern 
Portugal), and universities, such as the University of Santiago de Compostela 
and University of Vigo. 

These cases are a symbol of the different modalities in which local administrations 
can cooperate with other sectors to promote AI for the public benefit, in particular 
with  research  institutions  and  private  organisations.  In  general,  entities  in  the 
public  sector  can  be  involved  in  three  main  types  of  relations  in  the 
implementation  of  AI 
(G2G), 
Government-to-Citizen (G2C) and Government-to-Business (G2B). According to 
the  EC  study  report  ‘Adopt  AI’  (2024),  G2B  solutions,  i.e.,  interactions  with 
private  organisations  and  other  economic  activities,  are  the  least  numerous, 
representing 12% of the total.  

solutions:  Government-to-Government 

40 

 
 
 
 
From the survey results, interactions with the private sector often materialise in 
the development of digital platforms and clusters aimed at creating networks and 
promoting innovation. Cases of partnership with the private sector were reported 
by the Municipality of Liepaja (Latvia) with the ‘Liepājas tehnoloģiju klasteris’ 
initiative  (‘Liepaja  technology  cluster’)  –  that  sees  the  collaboration  of  IT 
professionals and companies with educational institutions and the city.  

Other cases are from the City of Dornbirn (Austria). The Austrian Municipality 
indicated  the  projects  ‘Plattform  für  digitale  Initiativen’  (‘Platform  for  Digital 
Initiatives’) - and ‘Plattform V’ (‘Platform V’). Both initiatives have the objective 
of encouraging networking and implementation of new technologies through the 
involvement of public and private actors. In particular, the project ‘Platform V’ 
promotes  cross-company  collaboration  and  enables  the  cooperation  between 
employees  of  different  companies  for  the  benefit  of  companies,  educational 
institutions and the population of the Lake Constance region.  

The relevance of programmes such as these, which encourage collaboration with 
the private sector and have a strong focus on the market, is particularly significant 
considering the low commercial exploitation of knowledge produced by European 
researchers  (Draghi,  2024).  Amongst  the  reasons  for  the  low  commercial 
exploitation  of  research,  the  report  recognises  exactly  the  low  integration  of 
researchers into innovation clusters in Europe. 

Data  on  the  development  of  partnerships  by  LRAs  mostly  overlap  with  the 
percentages of local administrations that are working on data interoperability and 
security for enabling AI to work properly.  

In the sense recognised within the European Interoperability Framework (EIF), 
interoperability  is  the  ability  of  organisations  to  interact  towards  mutually 
beneficial goals. Enhancing interoperability implies facilitating the digitalisation 
of  public  administrations  and  according  to  Tangi  et  al.  (2023),  amongst  its 
benefits  for  the  public  sector  are  improved  service  delivery,  increased 
transparency  and  accountability,  enhanced  data  quality,  greater  efficiency  and 
cost  savings,  and  facilitated  cross-sectoral  collaboration.  When  implementing 
interoperability, a crucial aspect to consider is security, in terms of protection of 
sensitive data and maintenance of robust security mechanisms.  

A question from the survey was aimed at detecting the level of implementation of 
interoperability and security at the benefit of AI technology adoption. The data in 
Figure 11 show that amongst the LRAs adopting AI, the number of those who are 
working on data interoperability balances out with those who are not (19 LRAs 
are working on interoperability and 16 are not).  

41 

 
 
 
 
 
 
 
Figure 11. Proportion of LRAs working on data interoperability and security  

Moreover,  as  anticipated,  the  percentage  of  LRAs  actively  addressing  data 
interoperability is encouraging (46.3%), showing that nearly half of the surveyed 
LRAs are actively working on it. A considerable share of LRAs (39.0%) is not 
currently  taking  steps  toward  data  interoperability  and  security.  This  could  be 
related to a specific lack of technical expertise or to a perceived lack of benefits 
in  addressing  this  topic.  Finally,  a  minority  of  LRAs  (14.6%)  indicated 
uncertainty and it suggests that there may be a low degree of communication and 
alignment within the organisation. 

42 

 
 
 
 
 
 
 
Figure  12  below  illustrates  the  interoperability  implementation  rate  amongst 
LRAs divided by demographic group. 

Figure  12.  LRAs  working  on  data  interoperability  and  security  by 
demographic group 

From the data in the graph, the interoperability implementation rate increases with 
demographic size. Approximately 83% of LRAs in the population range between 
500,000  and  2  million  are  working  on  data  interoperability,  while  the  same 
percentage amounts to about 17% in the category of the smallest municipalities. 
The  percentages  in  the  middle  demographic  groups  do  not  differ  much  and 
demonstrate that the rate of local administrations working on interoperability is 
not far below 50%. 

Moving from the internal facilitating factors to the external conditions impacting 
LRAs’  action,  the  use  of  AI,  on  the  one  hand,  can  strengthen  trans-sectoral 
cooperation, while at the same time, it is the regulatory, cultural and economic 
environment that influences its adoption. The AI Act remarks the importance of 
fostering an environment that supports responsible research and innovation and 
includes risk mitigation measures. In particular, the recent regulation encourages 

43 

 
 
 
 
 
 
the  establishment  of  regulatory  sandboxes5  with  the  aim  of  fostering  AI 
innovation whilst ensuring compliance with the EU and national laws. Together 
with  the  legislative  aspect,  infrastructural  and  scientific  frameworks  are 
fundamental in supporting AI adoption. Tools such as test beds and living labs6 
have the potential to support the experimentation of new technologies and assess 
the  feasibility  and  impact  of  real-world  applications.  Figure  13  provides  an 
overview of the level of advancement in the organisation of enabling actions in 
the process of AI development by LRAs. 

Figure 13. Organisation of enabling actions for AI development 

According to Figure 13, it can be seen that around one-third of the respondents 
adopting  AI  state  that  they  do  not  organise  any  of  the  listed  enabling  actions. 
Looking at the LRAs that organise actions, the graph shows how, in all European 
regions,  the  most  common  enabling  action  is  the  ‘best  practices  exchange’, 

5  Regulatory  sandboxes  refer  to  ‘regulatory  tools  allowing  businesses  to  test  and  experiment  with  new  and 
innovative products, services or businesses under supervision of a regulator for a limited period of time’ (Madiega 
and Van de Pol, EPRS 2022). 
6 Living labs are defined as ‘open innovation ecosystems in real-life environments based on a systematic user co-
creation  approach  that  integrates  research  and  innovation  activities  in  communities  and/or  multi-stakeholder 
environments’ (ENoLL). A test bed is identified in as a ‘controlled experimentation platform, in which solutions 
can be deployed and tested in an environment that replicates real-world conditions. Its goal is to create growth 
opportunities for new products, services or business development’ (EIT Community Testbeds). 

44 

 
 
 
 
 
 
followed by the organisation of test beds and, thirdly, by regulatory sandboxes. 
With respect to geographical differences, it is observed that the establishment of 
regulatory  sandboxes  is  more  common  in  Southern  Europe,  and  no  cases  are 
reported  in  Northern,  Central  and  Eastern  Europe,  while  test  beds  are 
proportionally more prevalent in Northern and Western Europe. 

Concerning the implementation of best practices exchange, from the survey two 
good practices emerged: 

-  The  Brøndby  Municipality  (Denmark)  reports  a  perfect  example  of  an 
initiative aimed at sharing knowledge and inspiration on the use of AI amongst 
municipalities. The solution is offered by a Danish knowledge centre and includes 
various  services.  With  the  objective  of  providing  guidance  and  fostering 
networking,  the  knowledge  centre  designed  a  ‘Municipalities’  AI  Map’  that 
collects  municipal  AI  solutions.  The  AI  projects  are  registered  directly  by  the 
municipalities  and  use  cases  can  be  searched  all  over  the  national  territory  by 
filtering by municipality and implementation sector. The centre also provides two 
‘Inspiration catalogues’, on ‘How the public uses GenAI’ and on ‘AI solutions 
with potential for implementation’. The ‘Inspiration catalogue: How the public 
uses GenAI’ was created in collaboration with the Digital Agency and  contains 
cases with solutions such as citizen-facing chatbots, minute writing and internal 
chatbots. The aim of the catalogue is to show how GenAI can help solve everyday 
tasks and make a difference for citizens and employees. For this purpose, advice 
from the collected cases is synthesised and shared. The ‘Inspiration catalogue: AI 
solutions  with  potential 
the 
Municipalities’  AI  Map  that  are  in  operation  or  in  pilot  in  one  or  more 
municipalities.  The  cases  are  selected  based  on  their  ability  to  inspire  other 
municipalities  to  implement  the  technology  and  focus  is  placed  on  both  the 
capacity of the AI solution to reduce the workload for employees and to improve 
the quality and the public satisfaction. 

implementation’ 

reports  cases 

from 

for 

-  The  Karlsruhe  Municipality  (Germany)  reports  the  KI-Allianz  Baden-
Württemberg, which creates and connects AI innovation clusters in various cities 
and  regions  of  the  German  federal  state  of  Baden-Württemberg.  The  Alliance 
encourages  connections  between  business,  science,  politics,  and  public 
administration. Its determination to promote the exchange of useful knowledge is 
symbolised  by  a  newsletter  that,  from  June  2024,  provides  information  about 
events, workshops, collaborations, ideas and joint projects. An explicit objective 
of  the  newsletter  is  to  share  inspiring  use  cases  and  promote  new  ideas  in  the 
development and application of AI. Moreover, a sub-project in development of 
the Alliance focuses on the design of an AI data platform that offers stakeholders 
the possibility to access, create and manage data sets and AI models. 

45 

 
 
 
 
 
Regarding the design of regulatory sandboxes, the case of the Agencia para la 
Modernización  Tecnológica  de  Galicia  (Galician  Agency  for  Technological 
Modernisation) in Spain is pioneering. The Agency is working on a legislative 
proposal to regulate the application of AI in the region of Galicia. The ‘Law for 
the Development and Promotion of Artificial Intelligence’ is soon going to be sent 
by the Xunta of Galicia to the Parliament for discussion, and it represents the first 
European regional law for the development and promotion of AI (see Section 2.2). 
The characteristic of the legislative proposal is to be aligned with the AI Act, and, 
according to representatives of the Agency, it has three main objectives: 

- Establish the framework for the design, acquisition, implementation and use of 
AI in public administration; 

- Regulate the legal regime for the use of AI by the public sector; and 

- Promote the development of AI in the Galician R&D ecosystem and the business 
regional network. 

The  law  is  a  self-regulating  legislation,  so  that  it  does  not  apply  to  markets. 
Nevertheless, in pursuing its objectives, it addresses various domains, from the 
fiscal incentives for enterprises, to promoting skills acquisition in the public and 
private sectors, to raising public awareness on the risks of AI. Further details and 
analysis on this case will be covered hereafter in this report. 

1.2.3 AI monitoring and evaluation mechanisms  

After analysing the key characteristics of AI adoption and implementation, a less 
investigated  but  crucial  aspect  is  related  to  the  monitoring  and  evaluation 
mechanisms to track key performance indicators (KPIs) and evaluate the success 
of AI-based solutions. As explained by Yigitcanlar et al. (2024), the introduction 
of  regular  evaluation  would  help  address  barriers  to  AI  adoption  and  support 
public administrations  in  identifying  areas  for improvement  and  optimising AI 
systems, both in internal efficiency and in satisfying external stakeholders.  

Figure  14  provides  an  overview  of  the  LRAs  that  are  currently  assessing  the 
effectiveness and efficiency of the AI and GenAI systems they are using. The pie 
chart shows that the majority of respondent LRAs that are adopting AI are 
not currently working on assessing its impact or effectiveness.  

46 

 
 
 
 
 
 
 
 
 
 
 
Figure  14.  Proportion  of  LRAs  assessing  or  not  the  effectiveness  and 
efficiency of AI applications 

To  better  understand  the  characteristics  of  local  public  authorities  adopting 
evaluation  mechanisms,  Figure  15  shows  the  percentages  of  respondents  by 
demographic group of related territories. 

47 

 
 
 
 
 
 
 
Figure  15.  LRAs  assessing  or  not  the  effectiveness  and  efficiency  of  AI 
applications by demographic group 

Figure 15 clearly demonstrates how the percentage of respondents that assess the 
effectiveness and efficiency of AI is higher in the largest local administrations 
while  no  assessment  is  performed  in  the  smallest  LRAs.  In  contrast,  in  the 
municipalities  pertaining  to  the  middle  demographic  groups,  the  percentage  of 
adoption of evaluation mechanisms is comparable and is around 35%. 

Figure  16  below  goes  into  detail  about  the  use  of  monitoring  and  evaluation 
systems and intends to examine whether within the local public administrations 
there  are  evaluation  mechanisms  aimed  at  adapting  ongoing  projects  to 
innovations,  in  the  sense  of  population  evolving  needs  and  technology 
advancements.  

48 

 
 
 
 
 
 
 
 
Figure  16.  LRAs  divided  by  demographic  group  that  foresee  or  not 
continuous  monitoring  and  evaluation  of  AI  projects  to  adapt  to  evolving 
needs and technologies 

The data partly match those of the previous figure, but it can be seen that in this 
case,  monitoring  and  evaluation  mechanisms  are  also  in  place  in  the  smallest 
municipalities, while in the largest ones, they are adopted by all LRAs. On the 
other hand, the gap in adoption rates widens between intermediate-sized groups 
of municipalities, with those with between 10,000 and 100,000 residents (small 
to  medium-sized)  showing  a  greater  presence  of  evaluation  systems. 
Disaggregating this information by geographical areas, it is noted that the highest 
percentages of LRAs foreseeing monitoring and evaluation systems are those in 
the  areas  of  Northern  and  Southern  Europe.  No  monitoring  and  evaluation 
systems are instead foreseen in LRAs located in Central and Eastern Europe. 

Linked  to  the  crucial  role  of  monitoring  and  evaluating  processes  of  AI-based 
solutions is the aspect of gathering public feedback. The collection of feedback is 
instrumental  to  increase  transparency  and  trust  of  citizens  and  businesses  and 
ensure the correct and confident usage of AI by end-users. Again, adoption rates 
are higher in Northern and Southern Europe, and no cases are registered in Central 
and Eastern Europe. In addition, data in Figure 17 make evident that the decision 
to  gather  public  feedback  for  shaping  AI  and  GenAI  strategies  is  not 

49 

 
 
 
 
 
homogeneous  across  LRAs  of  different  demographic  groups.  In  more  detail, 
LRAs  with  between  10,001  and  100,000  residents  and  LRAs  in  the  largest 
municipalities (with between 500,000 and 2 million residents) show the highest 
percentage rates. 

Figure  17.  LRAs  that  foresee  or  not  the  gathering  of  public  feedback  for 
shaping the AI strategies, broken down by size 

1.2.4 Challenges and drivers of AI adoption 

In the survey submitted to LRAs, part of the questions was devoted to gathering 
the opinions of the respondents to assess their perception on the current state of 
the AI integration process and the factors facilitating or hindering it. As a first 
aspect, an analysis was made of the drivers of AI adoption at subnational level. 

In identifying the drivers of AI and GenAI adoption, reference was made to the 
‘value drivers’ proposed in the AI Watch study ‘European Landscape on the Use 
of Artificial Intelligence by the Public Sector’ (Tangi et al., 2022). The drivers 
are grouped into three categories and are intended to summarise the main aspects 
able to produce value in the public sector and to influence the introduction of AI 
within it. The ‘Open government capabilities’ category refers to the impact on the 
transparency of public operations, participation in government and public control 

50 

 
 
 
 
 
 
 
of  government.  The  option  ‘Improved  Administrative  Efficiency’  encloses 
efficiency  and  effectiveness  purposes  and aims  at  capturing  the  ability  to  both 
improve  management  of  internal  resources  and  deliver  quality  services.  The 
option ‘Improved Public Service’ focuses instead on the goal of enhancing the 
quality of the service for the end-user, for example, by customising it or improving 
accessibility. 

In  line  with  previous  research  on  AI  adoption  in  the  public  sector,  the  data  in 
Figure 18 show that the driver of the AI adoption most often considered as ‘Very 
relevant’ by the LRAs is the ‘Improved Administrative Efficiency’. 

Figure 18. LRAs’ view on the drivers of AI adoption by public services at 
subnational level 

At  present,  the  main  incentive  in  AI  adoption  appears  thus  to  be  its  ability  to 
support internal efficiency, that is, reducing the amount of time and effort needed 
to produce and/or deliver a certain service. This may be linked to budget constraint 
considerations  and  more  generally  to  the  widespread  need  to  improve  public 
resources  management  in  local  governments.  The  driver  ‘Improved  Public 
Service’  is  considered  ‘Very  relevant’  by  a  slightly  smaller  number  of  LRAs, 
highlighting how AI is considered more useful for improving internal efficiency 
rather than for enhancing the quality of the public service. It could be argued that 
improving internal efficiency is a less risky application (or perceived as such) than 
an AI in support of citizen services. 

51 

 
 
 
 
Finally, only the option ‘Open government capabilities’ is considered (in 7 out of 
41 responses) as ‘Not relevant’, while most respondents deem this driver of the 
AI and GenAI adoption to be ‘Not so much relevant’. 

Compared to  what drives the  adoption of  AI, there  was  a desire  to understand 
what are the aspects that according to the LRAs should be prioritised or avoided 
in the future, also in the perspective of supporting policymaking. It is interesting 
to note that for LRAs, the aspects that should be prioritised in the use of AI and 
those  that  drive  it  to  date  differ  to  some  extent.  Figure  19  below  provides  a 
synthesis of the results of LRAs’ responses on the uses of AI and GenAI that, in 
their  opinion,  should  be  prioritised  or  avoided  in  the  future  to  maximise  their 
benefits. 

Figure 19. AI uses to be prioritised or avoided according to LRAs 

The data in the graph demonstrate that the use of AI and GenAI that most LRAs 
consider worth to be ‘strongly prioritised’ is to ‘improve public services delivery 
and citizen/government interaction’. On the other hand, the use that was most 
often  found  to  be  ‘strongly  avoided’  or  ‘possibly  avoided’,  is  to  ‘enhance 
decision-making  processes’. This  may  be  explained  by the fact  that AI-based 
decision-making could be perceived by LRAs as less acceptable to citizens. 

52 

 
 
 
 
 
 
 
These results can be commented on by combining both the responses on the uses 
that LRAs  are  currently prioritising  and on  the  factors considered by LRAs  as 
drivers of AI adoption. Interestingly, while ‘optimisation of internal management’ 
is  seen  as  the  main  use  of  AI  to  date  (see  Figure  6),  and  ‘improving  internal 
efficiency’ is perceived by respondents as the main current driver of AI adoption, 
this is not considered by LRAs to be the first aspect to be prioritised in future AI 
adoption. At the same time, the use that according to respondents can maximise 
the  benefits  of  AI  use  in  the  future  (‘improve  public  services  delivery  and 
citizen/government interaction’) was not considered to be the most relevant driver 
of AI adoption at present at subnational level.  

Lastly, the question on the main challenges and barriers faced in adopting AI and 
GenAI technologies was open to all respondents in the sample so as to analyse the 
perspectives of the LRAs that are already implementing AI-based solutions and 
of those that are not. The challenges identified concern potential obstacles in the 
AI  implementation  process  related  to  different  domains  such  as  the  lack  of 
political/community interest, the lack of resources in terms of human capital and 
budget  availability,  and  technical  barriers  such  as  on  data  quality  or 
interoperability.  

53 

 
 
 
 
 
Figure  20.  Main  challenges  in  the  adoption  and  implementation  of  AI 
technologies 

Figure  20  shows  that  the  three  barriers  more  often  seen  as  ‘great  challenges’ 
concern different areas and none of them is about technical infrastructure. In order 
of number of times mentioned, they are the ‘lack of skills and expertise’, ‘budget 
‘lack  of  awareness/knowledge  on  standardisation 
constraints’,  and 
processes’. On the opposite side, the aspects more often considered to be ‘Not a 
challenge’ are equally the ‘Lack of political interest in the AI adoption’ and the 
‘Lack  of  engagement  with  stakeholders  (e.g.,  private  actors,  regulatory 
authorities, academia)’. 

1.2.5 Conclusions 

AI adoption, application sectors and main benefits for LRAs 

Around  27%  of  LRAs  that  responded  to  the  survey  are  currently  adopting  AI. 
Analysing AI use cases by country and comparing them with the PSTW findings, 
in countries such as Germany,  Portugal, and the Netherlands, AI appears to be 
less  adopted  at  local  level  compared  to  the  national  level.  The  degree  of  AI 
adoption  increases  with  LRAs’  territorial  dimension,  measured  in  terms  of 
demographic size, so that in the largest LRAs the percentage of AI adoption is 
maximum.  

54 

 
 
 
 
 
 
 
 
At local public level, in all European regions, AI is found to be most used in ‘AI-
based  services  for  the  public  administration’.  As  for  initiatives  at  the  national 
level, priority use is given to municipal public services, whereas fewer use cases 
are found in the health and education sectors. Different secondary priorities in AI 
use are noticed amongst European regions. In Western and Northern Europe, the 
second  most  frequent  sector  is  ‘Municipal  public  services’,  while  in  Southern 
Europe the two sectors most widespread after ‘AI-based services for the public 
administration’, are ‘AI-based services for citizens’ and ‘Mobility and transport’.  
The  ‘Energy  and  sustainability’  sector  is  proportionally  more  common  in 
Northern Europe. 

More generally, the main areas in which AI support the public sector according 
to  LRAs  are  (1)  the  ‘optimisation  of  internal  management’  (more  frequently 
reported by Northern and Western LRAs), (2) the ‘improvement in the delivery 
of public services’ (more common in Western Europe) and (3) the ‘enhancement 
of  decision-making  processes’,  a  benefit  more  frequently  reported  in  Southern 
Europe.  The  area  that  seems  to  benefit  least  from  the  AI  implementation  is 
‘communication’.  Overall,  commonalities  are  found  in  Northern  and  Western 
Europe,  where  the  two  most  frequent  application  sectors  coincide,  and  both 
regions  use  AI  mainly  to  optimise  internal  management.  Additionally,  the 
application  sectors  and  the  recognised  benefits  of  AI  are  found  to  be  more 
numerous in Northern Europe. 

Human  resources  dedicated  to  AI  and  relations  with  the  external 
environment 

A  fundamental  role  in  AI  adoption  and  management  is  played  by  the  human 
resources dedicated to it, both in terms of the number of dedicated employees and 
the specific  qualifications.  According to  the  survey  results,  the  initiators of AI 
adoption in LRAs are most frequently identified, in order, in the figures of the 
‘manager’, the ‘digital departments’ in the public authority and of an ‘individual 
expert  in  the  field’.  ‘Partnership’  and  ‘in-house  agency’  categories  appear  less 
common. Comparing data by geographical areas, the roles that are recognised as 
initiators of AI adoption in all European regions are those of the ‘manager’ and of 
‘partnerships’. The figure of the ‘individual expert’ is also quite transversal across 
Europe, while ‘in-house agencies’ are more considered in Northern Europe. 

Looking  at the  presence of  internal AI dedicated staff,  the  decision to  allocate 
specific human resources does not result in a correlation with the demographic 
size of the LRAs. In particular, the groups that show the highest percentages of 
AI dedicated staff are the largest and the small to medium-sized LRAs. It is to 
note that almost all LRAs that have offices and roles specifically dedicated to AI 
are employing them in IT and Digitisation Departments.  

55 

 
 
 
 
 
To  capture  the  spread  of  AI-related  skills,  five  main  areas  of  expertise  were 
investigated:  technical  qualification  (e.g.,  data  science),  skills  on  ethical  and 
transparency requirements, basic knowledge on AI, management of procurement 
activities,  advanced  knowledge  of  AI.  According  to  the  survey  results,  the 
presence of AI-related skills and competencies does not appear to be markedly 
related to demographic size either. However, it is noted that as the demographic 
size increases, the presence of specific skills is more balanced. In particular, the 
‘Basic  knowledge  on  AI’,  which  is  the  most  widely  spread  competence  in  all 
European regions, is proportionally more prevalent amongst the smallest LRAs. 
The least common AI-related skill is the ‘Advanced knowledge of AI’ (reported 
in  only  two  LRAs  within  the  small  to  medium-sized  demographic  group), 
followed by the ‘skills on ethical and transparency requirements’, the latter being 
completely absent in the smallest LRAs. 

Regarding the potential of AI to increase the digital divide, most respondents are 
not acting to address the differences in digital competencies amongst population 
groups. The  data  related to  Northern  Europe  stands  out,  where  the  majority  of 
LRAs are not implementing measures for the inclusion of digitally disadvantaged 
groups. Looking at the LRAs that are already implementing measures, the most 
common is the ‘Support the affordable access to AI tools and technologies to help 
tackle  the  digital  divide’,  followed  in  equal  measure  by  ‘Online  guidance  and 
assistance’ and ‘Specific training to vulnerable groups’. 

In contrast with data on AI-related skills availability, a clear trend with respect to 
demographic size is found in the case of outsourcing. Indeed, the percentage of 
LRAs outsourcing increases with their demographic size. The correlation between 
outsourcing rates appears to be stronger with the demographic size of LRAs rather 
than with the availability of skills. 

The survey results point to a higher percentage of LRAs developing partnerships 
in Southern Europe, while no cases of partnership are reported from Central and 
Eastern Europe. Data on partnership development overlap with those on the LRAs 
working on data interoperability and security. This implies that while the number 
of LRAs that work on data interoperability balances out with those that do not, 
the interoperability implementation rate increases with demographic size.  

It is observed that one third of respondents that adopt AI is not organising enabling 
actions in the process of AI development, such as regulatory sandboxes, test beds 
or living labs. In all European regions the most common action organised is the 
‘best practices exchange’. With respect to geographical differences, it is noted that 
the establishment of regulatory sandboxes is more common in Southern Europe, 
while  test  beds  are  proportionally  more  prevalent  in  Northern  and  Western 
Europe. 

56 

 
 
 
 
 
AI monitoring and evaluation mechanisms  

To  evaluate  the  degree  of  implementation  of  monitoring  and  evaluation 
mechanisms of AI solutions amongst LRAs, three aspects were investigated in the 
survey:  (1)  the  practice  of  assessing  the  effectiveness  and  efficiency  of  AI 
applications currently in use, (2) the monitoring and evaluation of AI projects to 
adapt to evolving needs and technologies, and (3) the gathering of public feedback 
for shaping the AI strategies. 

The first finding shows that amongst the three actions investigated, ‘monitoring 
and evaluation of AI projects to adapt to evolving needs and technologies’ is the 
most  widespread  amongst  LRAs.  This  indicates  relatively  less  attention  to  the 
aspect of measuring the effectiveness and efficiency of AI applications currently 
in use. Indeed, only a minority of LRAs that are adopting AI solutions are working 
on this aspect (34%). Disaggregating data by demographic group, it is noted that 
the  highest  percentage  of  respondents  working  on  effectiveness  and  efficiency 
assessment is registered by the largest LRAs, while no assessment procedures are 
reported by the group of the smallest ones.  

The result is partly replicated in terms of differences amongst demographic groups 
when  looking  at  the  inclusion  of  ‘monitoring  and  evaluation  of  AI  projects  to 
adapt  to  evolving  needs  and  technologies’.  In  this  case,  as  anticipated,  the 
adoption level is overall higher and particularly both amongst the smallest and the 
largest  LRAs.  All  LRAs  in  the  largest  demographic  group  are  foreseeing  the 
monitoring and evaluation of AI projects.  

Regarding the third aspect investigated, the percentage of LRAs that are gathering 
public  feedback  for  shaping  the  AI  strategies  is  not  homogenous  amongst 
demographic groups. Public feedback result to be more taken into consideration 
by the LRAs in the small to medium-sized and the largest demographic groups.  

When  considering  geographical  differences,  the  adoption  level  of  the  three 
dimensions is consistent within each European region and no actions are reported 
to  be  implemented  in  the  LRAs  located  in  Central  and  Eastern  Europe.  The 
highest  percentage  of  LRAs  assessing  the  effectiveness  and  efficiency  of  AI 
applications is observed in Western Europe, while the monitoring and evaluation 
of  AI  projects  and  the  gathering  of  public  feedback  are  proportionally  more 
widespread in Northern Europe. 

Challenges and drivers of AI adoption 

The opinions expressed by LRAs on the main challenges faced in the adoption 
and  implementation  of  AI  offer  an  overview  of  the  aspects  on  which  priority 

57 

 
 
 
 
 
 
 
 
action should be taken to facilitate the dissemination of the technology. The three 
barriers more often seen as ‘great challenges’ by the entire sample of respondents 
are  the  ‘lack  of skills  and  expertise’,  the ‘budget  constraints’, and  the  ‘lack  of 
awareness/knowledge  on  standardisation  processes’.  Aspects  that  are  not 
considered  to  be  limiting  the  adoption  of  AI  are  instead  the  ‘Lack  of  political 
interest in the AI adoption’ and the ‘Lack of engagement with stakeholders (e.g., 
private actors, regulatory authorities, academia)’.  

Focusing  on  the  LRAs  that  are  adopting  the  technology,  the  driver  of  the  AI 
the  ‘Improved 
adoption  most  often  considered  as  ‘Very  relevant’ 
Administrative  Efficiency’,  followed  by  the  driver  ‘Improved  Public  Service’. 
The  aspect  ‘Open  Government  Capabilities’,  which  concerns  the  impact  on 
transparency of public operations, participation in government and public control 
on government, is considered by most respondents as ‘Not so much relevant’. 

is 

A discrepancy is noted between the opinions on the current drivers of AI adoption 
and the use of AI to be prioritised in the future to maximise its benefits. The use 
of AI that most LRAs consider worth to be ‘strongly prioritised’ is to ‘improve 
public  services  delivery  and  citizen/government  interaction’,  in  contrast  to  the 
main use of AI to date (‘optimisation of internal management’) and the current 
main driver that is identified in the improvement of administrative efficiency. This 
could be interpreted as the LRAs’ view of the priority shifting from improving 
internal management within the local public authority to improving the perceived 
quality of the public service by citizenship. On the other hand, the use of AI that 
was  most  often  considered  to  be  ‘strongly  avoided’  or  ‘possibly  avoided’  by 
LRAs, is to ‘enhance decision-making processes’. 

58 

 
 
 
 
 
Part 2: Factors for the successful AI and GenAI adoption in cities 
and regions 

The integration of AI and GenAI technologies into the operations of LRAs has 
the  potential  to  transform  public  sector  services,  improve  governance,  and 
enhance citizens' lives. However, the successful adoption of these technologies is 
influenced  by  a  variety  of  factors  that  differ  depending,  inter  alia,  on  the 
administrative context, the technological readiness of the LRA, and the specific 
use  cases  envisioned  for  AI  and  GenAI  deployment  at  place-based  level.  This 
chapter explores the critical factors that contribute to the successful adoption and 
implementation of AI and GenAI technologies by LRAs. 

In  the  context  of  this  study,  success  factors  are  defined  as  the  key  elements, 
conditions, or practices that significantly increase the likelihood of successful AI 
and GenAI adoption and integration into public sector services at the local and 
regional  level.  These  factors  have  been  examined  through  the  lens  of  the 
Technology-Organisation-Environment 
(Tornatzky  & 
Fleischer, 1990), a widely used theoretical model for understanding technology 
adoption in various settings. The TOE framework highlights three interdependent 
dimensions  that  influence  an  organisation's  ability  to  adopt  and  successfully 
integrate new technologies, namely: 

framework 

(TOE) 

•  Technology: The characteristics of the technology itself, including the benefit 
the organisation can gain from its adoption and implementation, as well as its 
complexity, compatibility, ease of use, and data availability and quality. 

•  Organisation: The internal characteristics of the adopting organisation, such as 

its structure, resources, culture, and readiness for change. 

•  Environment: The external factors that influence adoption, such as legal and 
regulatory environments and societal expectations (e.g., citizen acceptance and 
user engagement). 

By applying this framework, this chapter aims to provide a structured approach to 
understanding the factors that contribute to the successful implementation of AI 
and GenAI at the local and regional level.  It is grounded in primary data collected 
through qualitative research, specifically a set of semi-structured interviews with 
a diverse range of stakeholders, including officials at local and regional level and 
experts from academia.  

The officials interviewed are representatives from selected LRAs featured in the 
eight  case  studies  annexed  to  this  study.  These  case  studies  illustrate  the 
successful adoption and implementation of AI technologies at subnational level, 
providing concrete examples of how AI is being integrated into public services 

59 

 
 
 
 
 
 
and governance practices. In addition to these stakeholders, academic experts with 
extensive experience in AI and public administration were  also consulted. The 
experts' selection process aimed to capture perspectives from individuals who not 
only possess expertise in AI but also have a deep understanding of how AI is being 
implemented in public administration contexts.  

Four experts were consulted for the scope of this study, including authors of the 
EU Public Sector Watch Report (who provided an overview of the work carried 
out by PSTW), researchers focused on AI adoption and implementation by Italian 
public administrations, and a representative of an LRA that is amongst the most 
forward-thinking  subnational  governments  in  the  EU  investing  in  digital 
transformation, including AI initiatives, i.e., the Catalonia region. 

By  consulting  these  experts,  the  study  ensures  that  the  findings  reflect  both 
theoretical  knowledge  and  practical  insights  stemming  from  their  professional 
experience into AI's role in local and regional governance, particularly with regard 
to successful adoption strategies and challenges faced by public administrations 
at the local and regional level. 

2.1 Fostering cooperation between the technical and political levels 

The  successful  adoption  of  AI  at  local  and  regional  level  is  contingent  upon 
effective  collaboration  between  two  key  groups:  political  leaders  and  public 
managers, and technical experts. On one hand, politicians may lack the technical 
expertise to fully understand the potential, limitations, and risks associated with 
AI technologies, as they are often more concerned with societal issues related to 
public  trust,  policy  implications,  and  the  democratic  values  underlying 
governance. On the other hand, technical experts may struggle to communicate 
the benefits and implications of AI in ways that resonate with political leaders. AI 
technologies  are  often  perceived  as  highly  technical  and  abstract,  and  their 
implications are not always intuitive. Without a clear understanding of what AI 
can and cannot do, political leaders may either reject AI innovations out of caution 
or fail to implement them effectively, risking missed opportunities or unintended 
consequences.  It  is  therefore  important  that  the  two  groups  have  a  shared 
understanding  of  AI,  for  instance  by  translating  technical  jargon  into  plain 
language  or  by  fostering  the  use  of  XAI,  thus  enabling  a  common  full 
understanding of what AI is and how it works. 
Without proper communication and understanding between these two groups, the 
risk of creating or exacerbating a digital divide amongst different territories across 
the EU becomes a significant concern. This divide can undermine the equitable 
distribution of AI-driven benefits, particularly for less digitally advanced regions, 
thus hindering the effective use of AI in public administration settings and the 
alignment with the broader EU goals of social and territorial equality. Through 

60 

 
 
 
 
 
the EU cohesion policy,  the EU has been actively working to reduce disparities 
between  regions  by  promoting  economic,  social,  and  territorial  cohesion. 
Ensuring equitable access to AI technologies is a critical part of this vision, as it 
can drive development and innovation in less digitally advanced areas, helping to 
reduce the gap between high-tech urban centres and rural or peripheral regions. 

The CoR has further emphasised the importance of digital cohesion (CoR, 2022), 
which  focuses  on  ensuring  that  digital  technologies  and  infrastructures  are 
accessible to all regions and territories, regardless of their level of technological 
development.  Digital  cohesion  is  particularly  important  in  the  context  of  AI 
adoption, as technological advancements must not lead to greater inequality but 
promote inclusive growth and improved public services for all citizens. 

According  to  the  CoR,  digital  cohesion  requires  a  multi-level  governance 
approach, where LRAs are key players in shaping the digital transition in ways 
that are both inclusive and sustainable. AI technologies, when implemented with 
the right skills and understanding, can contribute to achieving these objectives by 
improving  public  services,  increasing  government  transparency,  and  fostering 
economic growth across regions. 

However, as already mentioned in Part 1 above, many LRAs are not internally 
equipped with the technical skills needed to adopt and implement AI solutions by 
their own  means.  According  to  this  study’s  findings,  a  great number  of public 
administrations at local and regional level tend to outsource the AI development 
and implementation, and/or to build external partnerships with other organisations 
that have the technical expertise needed.  

This is the case of the Municipality of Lisbon (Portugal), which in 2022 started 
the ‘Lisnav’ project (see Case II.7). The aim of this 1-year project was to leverage 
on AI and AR to improve the well-being of residents with visual impairments. To 
achieve this, the Municipality capitalised on a technological solution designed by 
3FINERY  LTD,  a  start-up  with  expertise  in  augmented  reality  and  inclusive 
technologies.  

Also,  the  Municipality  of  Vaasa  (Finland)  implemented  the  ‘Fix  the  Streets’ 
project  with  support  from  a  private  sector  technology  provider,  Crowdsorsa 
(formally  Crowdchupa)  (see  Case  II.3).  Crowdsorsa  is  a  company  located  in 
Tampere,  Finland,  which  has  been  developing  technologies  for  public 
administrations to delegate several tasks to citizens, such as providing information 
on the health of the streets and roads across the city, paying them for their time 
and effort.  
The experience of the Municipality of Jammerbugt (Denmark) is another use 
case that is worth mentioning as driven by a multi-stakeholder partnership (see 

61 

 
 
 
 
 
 
Case  II.5).  Considering  the  growing  risks  of  floods  in  the  city  due  to  climate 
change, in 2021 the Municipality designed the ‘wet index’, an AI-based tool to 
predict  floods  48  hours  in  advance  and  contribute  to  decision-making  in 
monitoring  and  local  planning  for  urban  and  rural  areas.  Although  the 
Municipality  had  internally  the  expertise  to  develop  such  a  tool,  a  multi-
stakeholder partnership with private suppliers, universities and research institutes 
was  effective  in  designing  the  project  and  defining  specific  processes  and 
methodologies  that  have  contributed  achieving  a  positive  outcome  upon 
completion of the project.  

2.2  The  facilitating  role  of  political  decision-makers  and 
appropriate governance 

Political leaders play a central role in driving and validating the adoption of AI at 
the local and regional level. While technology itself is often seen as neutral or 
self-implementing, its successful integration into public administrations heavily 
relies on the political will and capacity to lead and support digital transformation 
initiatives, including AI adoption and implementation. At the local and regional 
level,  the  importance  of  political  decision-makers  lies  in  their  ability  to  set  a 
strategic vision, assess the benefits and impacts of technological uptake, allocate 
both human and financial resources, and, crucially, foster a culture that embraces 
innovation. 

In  many  LRAs,  traditional  models  of  public  administration  often  prioritise 
stability, established practices, and risk aversion. AI adoption, however, presents 
both opportunities and challenges that require a shift in mindset. Political leaders 
can  be  pivotal  in  creating  a  culture  where  experimentation  and  innovation  are 
encouraged, and where staff at all levels of government are trained to understand 
and  engage  with  AI  technologies.  Without  political  leadership,  the  necessary 
organisational  changes,  investments  in  skills  development,  and  adaptation  of 
procedures will be delayed or ineffective. 

This is the case of the Municipality of Bologna (Italy), which is committed to 
the challenge of turning itself into the most progressive Italian city by adopting a 
Digital Twin, i.e., a city model using data to evolve in real-time to help generate 
public value. As pointed out by the Mayor of Bologna, Matteo Lepore, in a recent 
interview (2024):  
“for us, the digital twin project is […] amongst the pillars of a political strategy 
driven by the city’s needs”7.  

7 “Per noi il progetto del gemello digitale è […] un pilastro di strategia politica di protagonismo della città” 
(Italian original version). 

62 

 
 
 
 
 
 
 
Kicked off in 2024, the project is part of broader European, national and regional 
strategic goals aimed at fostering digital innovation, including the adoption of AI 
for the setup of new research infrastructure, sustainable urban development, and 
climate action (Comune di Bologna, 2024). Bologna is participating in the EU's 
Cities Mission for climate-neutral smart cities by 2030, and has incorporated the 
Digital Twin project within its flagship initiative towards urban regeneration and 
research collaboration uptake, the ‘Knowledge City’ (FUTURAnetwork, 2024). 
To  achieve  this,  the  City  is  benefitting  from  the  ‘Metro  Plus  Città  Medie  Sud 
2021-2027’,  the  Italian  National  Programme  approved  by  the  European 
Commission to provide funding to Italian cities for sustainable urban development 
projects.  

Although political decision-makers at the local and regional level are crucial for 
driving the adoption of AI within public organisations, it is equally important that 
appropriate  higher-level  governance  is  in  place  to  develop  a  set  of  terms  and 
conditions  -  both  regulatory  and  operational  -  enabling  public  authorities  at 
subnational level to adopt and implement AI solutions.  In this regard, the main 
benefit  of  clear  governance  is  the  establishment  of  a  transparent  and  effective 
mechanism  for  accountability,  able  to  demarcate  the  responsibilities  of  the 
municipalities from those of the regions. The most recent and wide-agreed model 
for AI governance is represented by the AI Act, as the first EU law regulating AI, 
that,  when  appropriately  implemented  at  subnational  level,  is  able  to  capture 
specific needs and governance requirements pertaining to a particular locality.  

In this regard, a pioneering initiative has been recently steered by Galicia, a region 
located in the Northwest of Spain, on the Atlantic coast. The region includes an 
Agency  for  the  Technological  Modernisation  of  Galicia  (AMTEGA,  2024). 
AMTEGA  has  four  primary  responsibilities:  (1)  oversee  the  digitalisation  of 
Galician  public  services,  (2)  promote  innovation  and  R&D  in  the  region,  (3) 
manage  and  operate  ICT  services  for  the  Galician  government,  and  (4)  foster 
collaboration and partnerships inside the region and with national, European, and 
international entities.  

In 2023, the Galician government tasked AMTEGA with developing a regional 
AI  Law  (Ley  de  IA,  2024).  The  Ley  de  IA’s  primary  goal  is  to  support  the 
implementation of the AI Act within the region and to establish a set of rules, 
principles, and standards concerning the development and implementation of AI 
by the regional government itself. The law has three primary objectives: (1) to 
establish the framework for the design, acquisition, use, and implementation of 
AI  technologies  in  public  administration,  (2)  to  regulate  AI  use  by  the  public 
sector and its government entities, and (3) to promote the development of AI in 
the Galician R&D ecosystem. It should be noted that the scope of the regulation 
applies  only  to  the  regional  public  sector,  the  citizenship,  and  the  region’s 

63 

 
 
 
 
relations with other public administrations – not to the region’s private sector AI 
market.  

The core principles of the Ley de IA are aligned with the EU AI Act, as it contains 
six  core  tenets:  (1)  transparency  and  explainability,  (2)  security,  privacy,  and 
reliability,  (3)  human  supervision,  (4)  accessibility  and  equity,  (5)  impact 
evaluation and continuous improvement, and (6) training and collaboration. The 
overarching  goal  of  the  Ley  de  IA  is  to  promote  trust  and  foster  innovation, 
preparing the region for the wider enactment of the EU AI Act. It also orientates 
the  regional  authority  at  the  forefront  of  the  public  sector,  focusing  on  AI 
innovation  as  it  continues  its  path  towards  digitalisation,  as  outlined  in  broad 
national strategies such as the Spanish Digital Strategy 2026 (SDS, 2026), and 
regional strategies such as the Galician Artificial Intelligence Strategy 2021-2030 
(GAIS, 2021). 

Analysing AI adoption and implementation as part of a broader framework, it is 
also  worth  taking  into  account  an  emerging  need  due  to  the  fact  that  AI 
technologies are by their nature borderless. The infrastructure supporting AI can 
be distributed across different regions, with data centres and cloud servers located 
in  several  parts  of  the  world.  This  implies  that  AI  governance  should  not  be 
limited to one jurisdiction, especially when the technology’s use may violate any 
laws pertaining to other regions. This global reach needs to be translated into a 
broader cooperation amongst international counterparts, to develop consistent and 
enforceable frameworks for AI governance and avoid regulatory fragmentation. 
Regulatory divergence - where different jurisdictions adopt incompatible AI rules 
- can create compliance challenges and undermine the effectiveness of the laws 
themselves.  Without  harmonised  frameworks,  there  is  a  risk  of  both  over-
regulation and under-regulation, each of which can have negative consequences 
for innovation and public trust. 

In this regard, the Canadian province of Québec is also positioning itself as a 
leader in responsible AI governance. While efforts at the Canadian federal level 
to enact the Artificial Intelligence and Data Act (AIDA, 2022) progress slowly, 
Québec is developing its own AI governance framework. 

The initiative is being led by the Québec Innovation Council (hereafter referred 
to as ‘the Council’), created by the Québec Government in December 2020. Its 
mandate is to boost innovation in Québec companies and within Québec society. 
It does this in four ways: (1) advising the provincial government on innovation 
strategies and approaches, (2) promoting and developing a culture of innovation, 
(3)  supporting  businesses  to  boost  performance,  and  (4)  documenting  and 
measuring innovation to guide decision-making. The 10-member Council is made 
up of experts from both the public and private sectors. One of the first tasks of the 

64 

 
 
 
 
 
Council  was  to  conduct  a  ‘non-partisan,  transparent  and  inclusive  public 
reflection’ (Québec Innovation Council, 2024a) as a framework for AI in Québec. 

The process of developing a framework began in the summer of 2023, when the 
Council convened expert workshops and conducted a public consultation to ‘help 
define the issues and opportunities associated with AI in order to ensure its ethical 
and  responsible  development  use’  (Québec  Innovation  Council,  2024b).  The 
workshops focused on six themes: (1) AI governance framework, (2) framework 
for investments in research and the private sector, (3) framework for the use of AI 
by the State, (4) impacts of AI on work and employment, (5) other societal impacts 
of AI, and (6) Québec’s role in the international framework for AI. The public 
consultation, which ran from 8 June to 14 July 2023, received 422 contributions, 
77% of which came from individuals responding in their own capacity. The results 
of both the expert workshops and public consultations were summarised in final 
reports and presented in a public forum in November 2023.  

These  consultations  informed  the  development  of  the  ‘Ready  for  AI  Report: 
Meeting  the  challenge  of  responsible  development  and  deployment  of  AI  in 
Québec’  (Québec  Innovation  Council,  2024c),  published  by  the  Council  in 
January  2024.  The  report  lays  out  12  priorities  and  25  complementary 
recommendations, aimed at ensuring the responsible development, and use, of AI 
in Québec. The recommendations relate to ensuring agile governance, anticipating 
and adapting to AI-induced changes, training stakeholders and impacted groups, 
empowering research and development, and positioning Québec to be a leader in 
responsible AI. The report ends with a recommendation to continue the work by 
creating a transitional steering committee on AI governance. 

Additionally,  in  June  2024,  the  Québec  Minister  of  Cybersecurity  and  Digital 
Technology  adopted  a  set  of  Principles  for  the  Responsible  Use  of  Artificial 
Intelligence  by  Public  Bodies  (Québec  Minister  of  Cybersecurity  and  Digital 
Technology, 2024), which should guide the use of AI across all sectors of public 
administration  in  the  province.  The  principles,  which  draw  on  the  OECD  AI 
principles, are: (1) respect for individual rights and the rule of law, (2) inclusion 
and  equity,  (3)  reliability  and  robustness,  (4)  security,  (5)  efficiency  and 
effectiveness,  (6)  sustainability,  (7)  transparency,  (8)  explainability,  (9) 
responsibility, and (10) competence. 

2.3 Organisational context and AI procurement  

Assessing  the  organisational  context  and  the  internal  staff  skills  is  considered 
fundamental for a successful implementation of AI in public organisations.  

65 

 
 
 
 
 
 
 
The organisational  context  can largely vary  from  municipality  to  municipality. 
Whether there is a dedicated IT department or even an AI office can profoundly 
affect  the  AI  implementation.  As  previously  mentioned  (see  Section  1.2), 
according to the primary data gathered for the scope of this study, the outsourcing 
is  more  frequent  in  larger  municipalities.  This  is  probably  due  to  the  cost  of 
charging  an  external  provider  with  the  AI  development  and  maintenance. 
However, there is a clear correlation between outsourcing degree and in-house 
digital  skills  presence.  In  fact,  having  specific  digital  skills  allows  for  a  more 
thorough  supervision  of  the  external  contractor  service.  Outsourcing  without 
appropriate digital skills within the municipalities, on the other hand, can create a 
vendor lock-in effect, which can become an obstacle to the implementation of an 
AI solution that is aligned with the end-users’ needs or simply to the shift to other 
solutions.  

In this regard, Zick et al. (2024) postulate that it would be important to consider 
the establishment of an AI procurement expert within public administrations, in 
order to endure effective and transparent implementation. 

In the case of the Municipality of Kortrijk (Belgium), the AI development as 
part of the ‘Virtual Assistant’ initiative was outsourced to a private company but 
the municipality retained control over certain aspects, in particular related to the 
integration  of  the  AI  solution  with  Kortrijk  IT  architecture  and  to  the  citizens 
engagement (see Case II.1). However, the private company retained the licensing 
rights, thus creating a dependency for future modifications.  

Differently, outsourcing the AI development to a university in the context of a 
partnership  seems  to  guarantee  greater  flexibility  and  transparency.  The 
INNOAIR project carried out by the Municipality of Sofia (Bulgaria) reported 
to  have  benefitted  from  a  collaborative  effort  of  universities  and  research 
organisations,  which  ensured  the  alignment  with  the  municipality’s  needs  (see 
Case II.2). A similar approach and results are found in the case of the SWIFTT 
project  involving  SIA  Rīgas  meži,  (i.e.,  the  municipal  forestry  and  park 
management agency in Riga, Latvia), which being an EC Horizon Europe funded 
project, saw the collaboration of public, private and academic institutions.  

Although the  case studies considered include two European capitals (i.e., Riga 
and Sofia), there is no evidence of the AI development through internal resources 
or in-house agencies.  
Nevertheless, some European public administrations adopted hybrid approaches 
to deal with the AI development.  

By  way  of  example,  the  Catalonia  Region  (Spain)  adopts  a  hybrid  approach 
combining outsourcing and internal development. In particular, the region made 

66 

 
 
 
 
 
 
a  strategic  assessment  which  found  low  feasibility  in  approaching  the  AI 
development with internal resources, mainly due to the low volume of use cases 
for AI application. Therefore, the region decided to exploit the advantages of AI 
development outsourcing but with a strong in-house oversight. In this way, the 
region is able to retain internal expertise for  effectively managing and guiding 
external contractors, also suggesting that internal development is not necessarily 
the optimal approach. 

Another  example  is  represented  by  the  AI4PublicPolicy  project  of  the 
Municipality of Nicosia (Cyprus) (see Case II.6). The initiative, funded under 
the EC Horizon 2020 programme, significantly relied on external technical skills 
on AI and data analytics, which was not present in the municipality. Such external 
expertise  was  provided  by  the  CYENS  Centre  of  Excellence,  a  public-private-
academic partnership including the Cyprus government, local universities (i.e., 
University of Cyprus, Cyprus University of Technology, and Open University of 
Cyprus)  and  private  companies  participating  in  the  project.  This  ‘inclusive’ 
outsourcing  approach  was  considered  to  ensure  balance  and  community  needs 
alignment. 

In this regard, it is useful to recall the effort made by the EC in 2023 to propose a 
contractual model for AI procurement that has been developed by a community 
of buyers. Moreover, such contractual model is drawn in two versions, one for 
high-risk AI systems and another for non-high-risk systems. 

The  research  conducted  indicate  that  the  AI  procurement  process  is  still  in  an 
experimental  phase,  where  gains  and  challenges  of  internal  versus  external  AI 
development are yet to be fully assessed.  

2.4 Enhancing citizen acceptance and engagement  

Since the early adoption of AI technologies in the 2010s, citizens acceptance has 
been a central topic in the broader context of digital transformation. Mentioned as 
a  key  aspect  in  almost  all  the  main  European  policies  and  strategies,  citizen 
acceptance  is  often  associated  with  the  idea  of  trust  on  aspects  such  as  risks, 
transparency and accountability.  

The  lack  of  citizen  acceptance  when  implementing  AI  solutions  can  indeed 
contribute to a digital divide, particularly at the local and regional levels. When 
citizens are not on board with AI technologies, it can hinder the effectiveness and 
equity  of  their  implementation.  This  lack  of  acceptance  often  stems  from  a 
combination of factors, including a lack of transparency in how AI systems work, 
insufficient  infrastructure  to  support  the  necessary  technologies,  and  a  general 
lack  of  digital  skills  to  understand  AI's  practical  applications  and  benefits. 

67 

 
 
 
 
 
 
 
Citizens who do not fully understand how AI operates or how it can benefit them 
are less likely to engage with or trust AI solutions. In this context, the research 
carried  out  by  Carter,  Liu,  and  Cantrell  (2020)  is  particularly  relevant.  When 
framing  the  digital  divide’s  implications  in  relation  to  AI  through  a  literature 
review analysis, they highlighted that, beyond the traditional digital divide, a new 
component  emerges  in  the  AI  landscape,  i.e.,  subjects'  perceptions  and  beliefs 
about  AI.  These  perceptions,  influenced  by  factors  such  as  transparency, 
accessibility, and public trust, play a critical role in the uptake of AI technologies. 
This reinforces the need for LRAs to prioritise both transparency in AI operations 
and  targeted  efforts  to  increase  digital  literacy  through  training,  education  and 
information  campaigns  (Vassilakopoulou  &  Hustad,  2021),  ensuring  that  AI 
solutions are not only technically viable but also socially accepted by citizens. 

According  to  Horvath  et al.  (2023), the  most  effective  approaches  to  fostering 
social acceptance are based on human supervision and digital skills. 

Specifically,  it  is  noted  that  the  greater  is  the  involvement  of  humans  in  the 
functioning  and  supervision of  AI, the  higher  is  the perception  of fairness  and 
accuracy, even if there is little scientific evidence that the humans’ contribution 
is actually able to mitigate AI errors. 

Moreover, according to scholars, being more fluent and savvy with technologies 
in general, and AI in particular, can significantly improve acceptance. In fact, it 
is found that people that are used to interact with technologies are less sensitive 
to human involvement in the AI process. 

On this latter point, not all the scientific literature agrees on the fact that digital 
skills are a major enabler for the AI acceptance. It can be argued that, at this stage 
of technological evolution, the interaction with most of the general AI solutions 
at disposal of the public, also considering the ones offered by the private sector, 
is very intuitive and user-friendly. This is even more true when considering the 
AIs based on a Large Learning Model (LLM).  

However, this is true for end-users acceptance of AI, while it becomes a decisive 
factor  for  potential  implementers.  In  this  regard,  in  the  process  of  deciding 
whether to adopt and develop AI solutions, AI and technological knowledge of 
the decision maker deeply influence the outcome. Having less familiarity with AI 
makes it more difficult to understand its benefit and possible applications. 
Risk and trust are identified as more crucial themes, possibly being the reasons 
for both accepting or not an AI solution, in particular when dealing with specific 
tasks over general ones (Gesk & Leyer, 2022; Robles & Mallinson, 2022). 

68 

 
 
 
 
 
 
 
On  this  matter,  collaborative  decision-making  and  co-creation  processes 
substantially increase trust and the risk mitigation perception, whilst fostering a 
sense  of  ownership  and  ensuring  that  the  AI  actually  responds  to  the  citizens’ 
needs (He Yue et al., 2024). An effective example is found in the Virtual Assistant 
of the  Municipality of  Kortrijk  (Belgium),  where  the development  of the AI 
solution has been preceded by users panels and inclusion advisory groups (see 
Case  II.1).  This  consultation  allowed  the  project  implementers  to  integrate 
transparency  features  that  aligned  with  user  needs.  A  focus  on  inclusion  and 
accessibility  has  also  been  placed  by  the  INNOAIR  project  carried  out  by  the 
Municipality of Sofia (Bulgaria), aiming at improving urban mobility through a 
sustainable, on-demand public transportation service (see Case II.2). During the 
citizens  and  end-user  consultation  phase,  which  took  place  before  the 
development  stage,  elderly  and  digitally  inexperienced  people  were  included 
through  accessible  request  centres  and  hybrid  service  options.  Moreover,  the 
project  also  included  iterative  feedback  collection  mechanisms,  which  were 
integrated throughout the development process.  

Similarly, fostering acceptance of AI solutions by municipal employees requires 
the implementation of engagement techniques. For instance, the SWIFFT project 
in Riga (Latvia), aiming at monitoring forest risks, included sessions for the field 
staff to gain confidence and explore the technology (see Case II.4). 

Feedback collection is also an approach used to engage end-users. However, as 
shown by our primary data (see Section 1.3), feedback gathering is not widely 
used, even if there is evidence that it raises the technological acceptance of end-
users (Palmas et al., 2021). This is the case of the ‘Fix the Streets’ project of the 
Municipality of Vaasa (Finland) (see Case II.3). The initiative was based on a 
gamification approach encouraging citizens to contribute with feedback both to 
the aim of the project (i.e., improving street maintenance) and to improve the AI 
solution. Moreover, the project also foresaw the inclusion of less digitally fluent 
users through the provision of both email and phone channels to give feedback. 

Testing  sessions  with  end-users  have  also  been  investigated  in  relation  to 
technological  acceptance,  in  particular  when  end-users  are  sceptical  about  the 
benefits of AI employment (Lin, 2013). This is also the case of the ‘wet index’ 
project of the  Municipality of  Jammerbugt  (Denmark), aimed at forecasting 
flooding in the area (see Case II.5). The project organised a testing session with 
citizens to boost trust and validate the AI technology functionality. Similarly, it is 
also reported in scientific literature that usability tests and user  experience  can 
affect citizens' acceptance (Mlekus et al., 2020). 

Moreover, the employment of narrative and storytelling to facilitate concepts on 
AI  technologies  has  been  positively  valued  by  the  scientific  community 

69 

 
 
 
 
 
(Macnaghten & Guivant, 2020; Prior & Leston-Bandeira, 2020). Indeed, evidence 
points  out  that  narratives  are  particularly  effective  in  conveying  messages  and 
allowing the public to understand and retain information (Lehne et al., 2015; Mar, 
2018; Marmolejo-Ramos et al., 2022).  

Finally, primary data collected in the survey show that the adoption of XAI is not 
widely  spread  amongst  European  municipalities  (see  Section  1.2),  but  there  is 
agreement in the scientific community that it has the potential to promote trust 
(Shin, 2021; Jangoan et al., 2024). 

2.5 Data availability and governance  

When establishing the framework for the design, adoption and implementation of 
AI, LRAs are asked to address ethical tensions associated with such a use, such 
as fairness, transparency, privacy and human rights (Madan & Ashok, 2023). In 
this  context, data  are  the  raw  material  from  which AI systems  learn  and  make 
decisions. Ensuring that data are available, secure, and compliant with legal and 
ethical standards is crucial to leveraging AI in a way that is transparent, fair, and 
respects citizens' rights. 

In  this  regard,  it  is  not  only  important  to  rely  on  accessible  large  amounts  of 
qualitative data, but also to have the adequate expertise to produce, collect and 
manage  data  in  an  ethical  and  transparent  way  whilst  adopting  a  security-by 
design approach at the time of developing the AI solution.  

This is the case of the  Municipality of Nicosia (Cyprus), which carried out a 
piloting activity on ‘Policies for Holistic Mobility and Accessibility’ within the 
EC Horizon 2020 AI4PublicPolicy project (see Case II.6). In a three-year period 
ranging from March 2021 to April 2024, the pilot aimed to unlock the potential 
of AI for the development of public policies by implementing a citizen-centric 
evidence-based open cloud platform to improve mobility and public transport, as 
well as to optimise parking accessibility for disabled people. Given the central 
role  of  disabled  people  participating  in  the  activities,  the  municipality,  in 
cooperation with the other partners involved in the pilot, was forced to delay the 
kick-off of the activities, as it found itself unprepared to collect smart city data in 
a proper way. To address the issue, the municipality worked in close collaboration 
with  ethics  and  GDPR  experts,  who  checked  that  the  data  were  secured  and 
cleaned, not including personal data, prior to their use. 
The complex, tension-ridden relationship between data privacy compliance and 
the potential benefits of adopting AI at the local and regional level has been further 
confirmed by a use case involving the Municipality of Turin (Italy). In 2020, 
the municipality was expected to deploy over 200 surveillance cameras across the 
city  as  part  of  the  ARGO  project,  aiming  to  provide  an  innovative  video 

70 

 
 
 
 
 
 
surveillance solution to enhance urban  and integrated security (Città di Torino, 
2020). These  cameras were  envisioned to  be  ‘smart’,  using AI-powered  image 
analysis algorithms to improve the monitoring of traffic and crime across the city. 
However, the project encountered significant privacy concerns that ultimately led 
to  a  shift  in  its  implementation.  In  response  to  the  proposed  AI-enhanced 
surveillance,  privacy  advocacy  groups  claimed  a  report  to  the  Italian  Privacy 
Guarantor,  raising  alarm  over  the  system’s  potential  capability  to  identify  and 
track individuals in real-time. Although the AI-powered solutions were promising 
in  their  design  and  application,  their  use  was  modified,  as  the  Italian  Privacy 
Guarantor  considered  the  collection  and  use  of  personal  metadata  to  be 
ambiguous. Therefore, in 2022 a reassessment of the ARGO project was made, 
implying that the cameras and the broader video surveillance network were no 
longer  equipped  with  AI  for  image  analysis  as  it  was  originally  planned 
(D’Albergo, Fasciani, and Giovanelli, 2023).  

In  order  to  ensure  the  ethical  and  responsible  use  of  AI  in  the  public  services 
managed by the regional government, in April 2024, the Digital Transformation 
Department of the Catalonia Region (Spain) announced the establishment of an 
Artificial  Intelligence  Commission.  This  body  is  responsible  for  ensuring 
compliance with emerging laws regulating AI, safeguarding personal data, and 
promoting  the  ethical  application  of  AI.  Managed  by  the  Vice  Presidency 
Department of the region, it also involves senior officials from all Government 
departments and key representatives from public sector bodies with expertise in 
telecommunications, information technologies and cybersecurity operating within 
the region, such as the Centre de Telecomunicació i Tecnologies de la Informació, 
the  Consorci  d'Administració  Oberta  de  Catalunya,  and  the  Agència  de 
Ciberseguretat de  Catalunya. The task force falls within the scope of the work 
initiated by the regional government in February 2020 with the implementation 
of  the  ‘Catalonia’s  Artificial  Intelligence  Strategy’  (Government  of  Catalonia, 
2020), to turn the public administration into ‘a benchmark in the reliable use of 
AI,  following  the  principles  of  transparency,  responsibility,  equity,  security, 
sustainability and rootedness in the country’8. 

Despite the fact that data availability and governance are likely to be significant 
barriers to the adoption and implementation of AI at local and regional level, there 
are  nevertheless  sectors  in  which  the  use  of  data  is  more  straightforward  and 
beneficial, such as environmental protection. AI-powered systems used to predict 
natural  hazards  (e.g.,  floods,  wildfires)  are  increasingly  being  developed  and 
trained with publicly available large amounts of environmental data, which can 
be  collected  and  used  without  violating  personal  privacy.  This  presents  an 
opportunity  for  LRAs  to  adopt  and  implement  AI  solutions  with  fewer  data 

8 https://www.aoc.cat/en/blog/2024/la-generalitat-de-catalunya-constitueix-la-comissio-de-la-intelligencia-
artificial/.  

71 

 
 
 
 
governance  obstacles,  as  confirmed  by  the  Municipality  of  Jammerbugt 
(Denmark) (see Case II.5). As outlined by a representative of the  municipality 
during the interview that was conducted within the scope of this study, one of the 
strengths of the project was the availability and quality of the data used to develop 
the ‘wet index’, which was designed to address one of the city’s priorities, i.e., 
environment  and  climate  change.  Under  such  circumstances,  the  technical  and 
ethical challenges associated with AI adoption are mitigated by relying on non-
personal, open data sources, thus enabling smoother AI uptake and increasing the 
potential for scaling the solutions to other regions.  

2.6  Making  AI-based  solutions  scalable  and  applicable  to  other 
contexts  

When used to improve the delivery of public services and/or to enhance decision-
making processes, it is crucial that AI solutions are designed according to users’ 
needs and tested prior to shifting to large-scale experimentations, which may risk 
eroding citizen trust (Madan & Ashok, 2023). However, once validated in their 
concept and application, it is crucial to ensure that these solutions are scalable and 
fit  for  various  settings,  in  order  to  enhance  their  use,  impact  and  cost-
effectiveness.  

The ‘Virtual Assistant’ initiative developed by the Municipality of Kortrijk in 
the Flanders Region (Belgium) was designed with the ambition to make the tool 
scalable and fit for purpose for other LRAs (see Case II.1). The Virtual Assistant 
was designed to easily integrate with content management systems such as Drupal 
and  WordPress, which  are  commonly used  across  municipalities in  the region. 
This  flexibility  ensures  that  the  tool  can  be  rolled  out  across  other  regions  in 
Flanders and even nationally without requiring significant customisation.  

The successful integration of AI into urban mobility solutions to enhance public 
transport  and  improve  environmental  conditions  in  Sofia  (Bulgaria)  led  the 
Municipality to decide to make the AI solution scalable to other settings (see Case 
II.2).  Upon  completion  of  the  piloting  activities  in  the  neighbourhoods  of 
‘Manastirski  Livadi’  and  ‘Baxton’,  which  confirmed  the  capability  of  the  AI-
powered solution to provide citizens with an alternative to car travel thanks to on-
demand electric buses, the municipality has decided to scale up the service into 
three other suburbs (i.e., ‘Malinova dolina’, ‘Vitosha’ and ‘Gorna Banya’).  

The EC Horizon Europe project  SWIFTT  involving  SIA Rīgas meži, (i.e., the 
municipal  forestry  and  park  management  agency  in  Riga,  Latvia),  is  another 
successful use case related to a promising go-to-market AI-powered solution (see 
Case II.4). Aiming to enhance forest management and biodiversity conservation, 

72 

 
 
 
 
 
 
the AI system is being developed in order to be interoperable with the Copernicus 
satellite imagery to detect and map the various risks to which forests are exposed.  

Finally,  mention  should  be  made  specifically  to  GenAI  solutions.  From  the 
findings that emerged within the present study, GenAI solutions are not currently 
adopted at local level. In the cases where solutions based on OpenAI models are 
adopted, this is mainly for the implementation of chatbots that have been strongly 
limited in their generative capabilities. 

In fact, it is found that without specific regulatory guidance, municipalities do not 
believe they can properly assess risks and implications of GenAI use. The lack of 
specific skills is also a factor contributing to the perception that GenAI may raise 
additional legal and ethical concerns regarding possible errors and hallucinations 
generated by the AI (Minguez Orozco & Welin, 2024). Other barriers to GenAI 
uptake may be represented by emotional and psychological factors that are still to 
be further investigated. 

2.7 Investment and funding 

One of the most significant factors for the successful adoption of AI at the local 
and  regional  level  is  the  availability  of  investment  and  funding.  Despite  the 
potential of AI to transform public services, enhance operational efficiency, and 
drive  innovation,  one  of  the  most  common  and  persistent  challenges  faced  by 
LRAs is limited financial resources. Budget constraints often create significant 
barriers to the effective implementation of AI initiatives at subnational level, as 
pointed  out  by  most  LRAs  who  participated  in  the  survey  that  has  been 
implemented within the framework of this study (see Section 1.2.4). The lack of 
adequate  funding  can  hinder  the  procurement  of  necessary  technology,  the 
development of digital infrastructure, and the recruitment of a skilled workforce, 
all of which are essential to drive AI projects forward. 

LRAs are often at a disadvantage when it comes to accessing large-scale funding 
compared to national governments or private sector companies. Many AI adoption 
projects at subnational level require significant upfront investment, which can be 
difficult  to  justify  when  budgets  are  already  stretched  thin.  In  addition,  LRAs 
frequently  face  challenges  in  maintaining  long-term  funding  for  AI  projects, 
especially given the rapid pace of technological change and the evolving nature 
of  AI  applications.  Without  a  sustainable  funding  strategy,  AI  initiatives  risk 
becoming  unsustainable  from  being  diffused  or  failing  to  achieve  their  full 
potential. 

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As a result, the long-term nature of AI projects requires a more strategic approach 
to  financing,  one  that  can  align  with  local  and/or  regional  development  goals, 
sectoral policies, and broader national and European funding opportunities. 

This is the case of the Municipality of Noisy-Le Grand (France), which in 2023 
launched the ‘Immediate and Long-Term Energy Reduction’ (RECITAL) project, 
aiming to use AI to reduce the energy consumption and carbon impact of the city’s 
200  public  buildings  (see  Case  II.8).  The  project  has  been  primarily  funded 
through  a  combination  of  local  budget  allocations  and  national  government 
support  for  energy  transition  initiatives.  In  fact,  the  municipality  has  obtained 
financial  assistance  from  the  French  Ministry  of  Ecological  Transition  and 
Territorial  Cohesion,  which 
the  DIAT  call  for  projects  on 
‘Demonstrators  of  frugal  Artificial  Intelligence  at the  service  of  the  ecological 
transition of territories’ that covered 50% of the costs allocated to the project (i.e., 
EUR 1.1 million). 

launched 

Another successful case study is related to the Municipality of Vaasa (Finland), 
that,  between  2020  and  2022,  implemented  the  ‘Fix  the  Streets’  project,  to 
improve the conditions and safety of the city’s streets and roads through targeted, 
data-driven  solutions  (see  Case  II.3).  Vaasa  greatly  benefitted  from  a  formal 
research and innovation budget, granted annually by their national government. 
This  budget  promotes  (and  mandates)  experimentation  and  innovation  in  the 
provision of public services, with a specific focus on technologically leveraged 
improvements that can be scaled to other regions and municipalities.  

The  Municipality  of  Kortrijk  (Belgium)  is  a  key  player  in  the  digital 
transformation of local governance. In line with the region’s broader digitalisation 
strategy, Kortrijk has implemented a pioneering ‘Virtual Assistant’ project aimed 
at improving the accessibility, efficiency, and quality of public services (see Case 
II.1).  Funded  by  a  regional  budget  attached  to  a  formal  policy  initiative, 
‘Municipality  without  a  Town  Hall’,  this  initiative  is  part  of  the  region's 
commitment  to  advancing  smart  city  technologies,  digital  government,  and  e-
governance through the adoption of AI solutions, inter alia, to better serve citizens 
and businesses alike. Such a commitment has been formally incorporated by the 
region in the Flanders’ AI Policy Plan, launched in 2019 and renewed in 2024, 
aiming to enhance the existing AI knowledge base and its uptake in the region, in 
close cooperation with the Flemish industry.  

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Part 3: Foresight analysis, conclusion and policy recommendations 

The following sections are structured to provide clear progression from foresight 
analysis  to  policy  recommendations.  Based  on  an  exploration  of  trends  in  AI 
adoption, weak signals and megatrends, along with the main findings of the study, 
action-oriented recommendations are provided to support successful adoption of 
AI within LRAs. 

The  recommendations  address  the  relevant  aspects  of  AI  implementation  and 
offer practical and scientifically grounded guidance to EU institutions, Member 
States, and LRAs for an effective and coordinated action on AI adoption for the 
public sector at local level.  

3.1 Foresight analysis 

The foresight analysis approach in this study can be defined as an exploratory, 
empirical-analytical  approach  (Kreibich,  2006).  It  focuses  on  possible  future 
trends concerning AI in public administrations, megatrends that are affecting the 
society globally and underexplored developments in AI technology. In this sense, 
the  foresight  technique  is  intended  as  a  tool  for  increasing  awareness  and 
knowledge of potential future implications focused on a specific topic. 

Figure  21  -  Relationship  and  reciprocal  influence  between  trends, 
megatrends and weak signals 

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As  shown  in  Figure  21,  the  foresight  analysis  and  its  results are  based on  the 
consolidation of three elements: 

•  AI trends identified by the LRAs that participated in the survey; 
•  Horizon Scanning for the relevant weak signals detection; and 
•  Megatrend analysis. 

For  the  purpose  of  this  study,  it  is  important  to  understand  the  three  different 
timespans put in relation by these three elements. While megatrends are societal 
changes that are already ongoing and trends are topics that are emerging today, 
weak  signals  are  considered  as  technological  innovations  that  can  acquire 
importance in the future. Therefore, it is important to examine the impact these 
elements taken all together may produce. 

Figure  21  represents  how  these  elements  interact  with  each  other  and  the 
dynamics such interactions create. AI trends are depicted as emerging themes in 
the present moment, since they were identified by  the LRAs responding to the 
survey  as  topics  which  will  acquire  growing  importance  in  the  future.  The 
different  heights  of  the  columns  representing  the  trends  are  related  to  the 
frequency  of selected  keywords’ occurrence  in  the  survey  results.  Megatrends, 
which are represented as arrows, have trajectories that start in the past and point 
towards the future. In this analysis, AI trends are correlated with megatrends with 
a certain degree of semantic and logical proximity, which could accelerate the AI 
trends’ development in the future. According to the qualitative feedback received 
on  AI  trends  by  the  survey  respondents,  the  authors  performed  a  clustering 
exercise to identify a list of trends (see Figure 22). Lastly, weak signals of new 
technologies  that  may  affect  the  AI  uptake  by  public  administrations  are 
represented as horizontal bars intersecting both trends and megatrends. The weak 
signals  width  depends  on  their  degree  of  logical  connection  and  relevance  to 
trends and megatrends. Finally, the positioning of the weak signals on the time 
axis describes the point in the future where weak signals might materialise. 

In  the  following  sections,  each  element  of  the  foresight  analysis  is  described 
individually, leading to their consolidation in meaningful key insights. 

3.1.1 The AI trends identified by survey respondents 

As  technology  advances  at  an  exponential  pace,  continuous  monitoring  of 
emerging trends is essential to facilitate its adoption and, more broadly, to adapt 
digital transition processes within public administrations effectively. 

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For this purpose, the survey included a specific question on relevant trends and 
future developments for the AI implementation in public administrations9.  

To analyse the survey responses, the authors took the following steps: 

1.  Replies  were  clustered  according  to  meaningful  keywords  and  coded 

accordingly. 

2.  Keywords’ occurrences were counted to show the frequency and relevance 

of certain topics. 

3.  The survey data were correlated with information on the geographical area 

of the respondents. 

Twelve  keywords  were  identified  in  the  responses  with  various  degrees  of 
frequency (Figure 22). Moreover, Figure 22 allows for an analysis of how the 
most recurring topics are divided amongst European regions. The graph shows 
the  frequency  of  the  occurrence  of  each  keyword,  considering  that  for  each 
response more than one keyword could have been attributed based on semantic 
criteria.  As  a  criterion  for  the  geographical  subdivisions,  it  was  used  the  one 
proposed by EuroVoc, as described in Section 1. 

Figure 22 – Trends’ relevance according to the survey results  

9 i.e., Q1.33 - Concerning the future of AI and GenAI, do you see any trend for this technology or lessons learnt 
that can be used to improve the use and adoption of AI and GenAI at regional and local levels? (see Annex I) 

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Figure  22  shows  the  most  recurring  themes  are  the  ones  concerning  ‘AI 
integration in public services and efficiency’, ‘governance and guidance’, ‘ethics’ 
and ‘digital skills’ across European regions. It is important to note that there are 
more  trends  connected  with  the  streamlining  of internal  processes  than  toward 
improving the delivery of services for the population.  

AI integration in public services and efficiency. It appears clear how, from the 
public sector’s perspective, the primary focus is the life and well-being of their 
citizens (Androniceanu, 2023). Therefore, AI and GenAI are considered useful 
tools to improve the number and quality of services offered by the administration 
to  the  citizens  (van  Noordt  &  Misuraca,  2022).  The  use  of  advanced  digital 
solutions is, for most of the respondents, the key to enhance customer experience 
and optimise bureaucratic processes.  

Governance  and  guidance;  ethics.  The  analysed  data  also  indicates  a  clear 
expectation from LRAs concerning the growing need for research and guidance 
for  ethical  considerations  in  AI  use.  Transparency,  respect  of  privacy  and 
traceability of processes have been addressed, but many aspects related to other 
ethical  implications  of  AI  remain  underexplored.  Furthermore,  standardised 
frameworks and monitoring mechanisms are yet to be established. Consequently, 
ethics is still emerging as a central and increasingly prominent area of focus for 
the coming years (Siau & Wang, 2020).  

Digital skills. The impact of digitalisation remains limited if those engaging with 
it – both civil servants and citizens – lack adequate training and understanding of 
to  develop  a 
AI-driven 
comprehensive range of digital skills is essential to ensure effective utilisation and 
to mitigate the risk of misuse (van Noordt & Tangi, 2023). This is a topic that is 
perceived as pivotal to correctly manage the AI uptake and application. 

solutions.  Providing  universal  opportunities 

sharing  and 

cooperation  between 

Knowledge 
and 
regional/national governments, also through a standardised set of ‘best practices’, 
is considered to  be  of  increasing  relevance.  This  activity, often used  for better 
exploring use cases and solutions for possible obstacles, is considered crucial to 
avoid  duplicating  efforts  and  to  exchange  significant  experiences,  including 
building networks of LRAs using AI. In light of this, the knowledge sharing will 
become more and more necessary for LRAs.  

administrations 

Risk assessment. The risks associated with AI usage, introduced and adopted as 
a guiding criterion by the AI Act, represent one of the foremost concerns within 
the public sector. Many respondents expressed the need for clear frameworks for 
risks  assessment  connected  to  the  possible  development  of  advanced  digital 
solutions (Chiariello, 2021). Therefore, it is necessary to develop a comprehensive 

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set of guidelines that can help public administrations to adopt AI-driven solutions 
minimising  the  threat  for  the  citizens,  and  such  frameworks  need  to  be  easily 
updated to incorporate new risks identified as the technology evolves. 

Data quality  and interoperability.  It  extends beyond the provision of ensuring 
security and protection in case of data collection and storage from external access 
and  manipulation.  Significant  emphasis  is  placed  on  establishing  seamless 
communication  and 
interoperability  between  diverse  databases  across 
municipalities  throughout  Europe,  following  the  principle  described  by  the 
European Parliament as the ‘[…] essential link between the availability of high-
quality data and the development of AI’ (European Parliament, 2021). Facilitating 
the  sharing  of  non-sensitive  data  to  support  neighbouring  LRAs  is  critical  for 
enhancing the efficiency of AI-driven solutions, particularly those reliant on data 
exchanges  that  transcend  national  borders.  The  focus  is  also  on  the  practical 
aspects  regarding  data  interoperability.  According  to  the  INSPIRE  Directive 
(2007), EU Member States should implement ‘[…] rules laying down technical 
arrangements for the interoperability and, where practicable, harmonisation of 
spatial data sets and services […]’.  

The  concept  of  data  sharing  is  also  in  line  with  the  ‘Once  Only  Principle’, 
according to which citizens do not have to present the same data to the public 
administration more than once, ensuring that ‘data that is already known to public 
administrations is pre-filled in forms presented to the user’ (EC, 2022). 

Scalability.  Risk  management  and  data  interoperability  are  also  crucial  for 
enhancing the scalability of projects, which has been highlighted especially by 
municipalities  in  Central  and  Southern  Europe,  and  which  is  of  paramount 
importance  to  optimise  the  reuse  of  technologies  and  methodologies  that  have 
proven effective in other contexts. A clear model for the scalability of AI projects 
can also support smaller municipalities, with less financial and human resources, 
to adopt AI solutions more easily. 

Sustainability.  Much  less  discussed  is  the  topic  of  sustainability.  By  way  of 
example, AI could be used to reduce CO2  consumption and emissions (Rakha, 
2023)  and  support  achievement  of  the  European  Green  Deal.  Nonetheless,  the 
specific  ways  AI  can  support  LRAs  in  this  regard  is  still  largely  unexplored. 
Additionally, the energy usage – and relative CO2 emissions – directly related to 
AI tools themselves is generally known but not yet comprehensively measured or 
addressed. As shown in Figure 22, countries in Northern and Western Europe do 
not see AI sustainability as a trend, and this could indicate that their pluriannual 
tradition in integrating digital solutions and territorial planning with a strong view 
on  environmental  protection  is  already  part  of  their  usual  planning  process. 

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Therefore, sustainability should not be considered for them as ‘emerging’ but as 
an ‘established’ practice (Akande et al., 2019). 

Public engagement. The low incidence of responses pointing out the relevance of 
public engagement as a future trend could be related to the modest attention that 
is still given to co-creation and its benefits. If public administrations were more 
aware of the importance of mutual feedback exchange and reciprocal advice in a 
context  which  is  still  only  superficially  studied,  the  direct  involvement  of  the 
population would be a more widespread practice. Moreover, this is particularly 
relevant for the AI divide, as the level of public engagement (or lack thereof) is 
connected  to  scepticism  towards  the  technology  itself  or  to  the  perception  of 
scarce positive impact concerning the AI applications. As AI will evolve towards 
increasingly  complex  tasks,  it  will  be  crucial  to  engage  with  the  end-users  to 
explain the AI mechanisms and to address potential concerns. 

Finally,  two  trends  received  less  attention  as  expected,  namely  financial 
resources and GenAI. The first does not seem to be considered as becoming more 
relevant in the near future, even if the importance of the economical dimension is 
acknowledged by municipalities in Central and Eastern Europe. This could be also 
due  to  the  increasing  presence  of  financing  instruments  that  at  present  are 
available for AI implementation (e.g., Digital Europe, Horizon Europe, European 
Digital  Innovation  Hubs,  Testing  and  Experimentation  Facilities,  along  with 
national funding foreseen through the structural funds). Concerning GenAI, the 
low interest received is actually in line with the general sentiment towards GenAI 
observed in the interviews conducted for the scope of this study and in the survey 
results.  The  technology  is  probably  not  mature  enough  to  allow  public 
administrations to fully understand its risks, benefits and applications. 

Some  respondents  did  not  identify  any  trends.  There  are  different  possible 
explanations to justify this. This may be because many public administrations are 
simply too small, in numbers and dimensions, to have enough experience in such 
a field to be able to elaborate a future vision for AI. Finally, some respondents 
highlighted  the hype surrounding AI, suggesting that the current enthusiasm is 
largely driven by curiosity and prevailing fads, which are likely to be diminished 
in the coming years (Marx, 2024). 

3.1.2 The Horizon Scanning for relevant weak signals detection 

Weak signals are defined as ‘information about an emerging development with a 
likely future impact’ (van Veen & Ortt, 2021). Since foresight has an anticipatory 
aim, the detection and analysis of weak signals is crucial for exploring possible 
future developments within a given sector. 

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Starting from the JRC report on weak signals (JRC, 2021) and from the JRC Weak 
signals 2020 Dashboard, which is continuously updated, relevant weak signals for 
AI  development  were  selected.  The  process  of  exploration,  filtration  and 
assessment of weak signals is a technique called Horizon Scanning. According to 
the Better Regulation Toolbox of the EC, this approach needs to be integrated as 
part of a wider foresight exercise, ‘whenever there is a high degree of uncertainty 
surrounding changes to the relevant future context and to ensure that short term 
actions are grounded in the long-term objectives’ (EC, 2021a).  

For the purpose  of  this study, the AI trends identified by the LRAs have  been 
cross-referenced  with  selected  weak  signals,  retrieved  by  ‘Weak  Signals  in 
technology’ (Eulaerts et al., 2021). The main areas of potential development in 
the  field  of  AI  were  identified  and  explored,  specifically  regarding  their 
application within local public administrations in Europe. 

The analysed weak signals, chosen for their particular relevance with the topic 
addressed in these pages, are the following: 

Ethical  AI.  As  previously  highlighted,  ethical  considerations  are  crucial  when 
implementing  AI-based  solutions.  The  importance  of  an  ethical  framework  is 
recurrently mentioned in scientific literature, as well as within the AI Act, and its 
background policies and strategies. The risk-based approach adopted in the AI 
Act reflects the choice of the EU to approach potential problems proactively. The 
aim  is  to  address  the  complexities  of  AI  development,  providing  a  structured 
regulatory framework to shape its future (da Costa Alexandre & Pereira, 2023). 
In  the  ‘Draft  Report  on  Artificial  Intelligence  in  a  Digital  Age’,  published  in 
November 2021 by the Special Committee on Artificial Intelligence in a Digital 
Age (European Parliament, 2021), it is clearly stated that, 

[…] central to the EU regulatory approach is a strong attention to ethical 
considerations  in  line  with  core  human  rights  values  and  democratic 
principles. 

Considering the exponential growth that AI will likely undergo in the years to 
come,  an  early  attempt  to  foresee  every  single  possible  evolvement,  field  of 
application or possible threat derived by a misuse of the technology is a complex 
exercise. Excluding some implementation of AI as considered harmful for citizens 
is the solution adopted by the EU legislator, who acknowledges the issue of ethics 
as  being  of  paramount  relevance.  The  focus  on  this  aspect  acquires  additional 
importance if brought into the field of public administrations, which is necessarily 
bound to respect the citizen, safeguarding their privacy and safety.  

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Starting from the  provisions of  the  AI Act and national regulations,  as well as 
building on the results of the present  research, it is possible to identify aspects 
concerning  the  ethics  for  AI  that  are  worthy  of  attention,  as  they  may  acquire 
relevance in the immediate future. 

Ethical principles and values may diverge across national and regional contexts, 
as well as between individuals, reflecting the nuanced ethical landscape across 
and within the EU Member States. The data obtained from the survey underscore 
this  variation,  revealing  significant  differences  even  within  the  EU  countries. 
While  it  is  crucial  for  international  and  European  legislation  to  provide  a 
foundational ethical structure to ensure compliance with universal legal standards 
and  shared  values,  it  is  important  to  guarantee  a  certain  degree  of  freedom  of 
action to national and regional legislators. This approach allows for the tailoring 
of AI regulatory measures to better align with the specific needs, cultural values, 
and  political  sensitivities  of  individual  populations,  fostering  ethical  practices 
more suitable  for  different  social  contexts.  Furthermore,  it  is  also  important to 
consider  the  need  for  an  approach  to  ethics  in  AI  that  is  able  to  monitor  and 
address new ethics concerns as they emerge with the further development of the 
technology and with the evolving perception of the end-users. 

Moreover,  another  important  ethical  aspect  in  the  context  of  the  AI  uptake  by 
European public administrations, is the emergence of an AI divide. In order to 
better frame the challenge, it is possible to make a distinction between AI divide 
amongst citizens and AI divide between public administrations. 

Addressing the digital divide amongst citizens requires that public administrations 
adopt comprehensive digitalisation strategies that inclusively engage individuals 
with  limited  exposure  to  new  technologies  or  constrained  access  to  digital 
resources. Without such efforts, these populations risk being marginalised, unable 
to adapt to rapid technological advancements and potentially deprived of equal 
access to essential services. A promising approach may be to implement a dual-
access model: while advancing digital public services, they should also preserve 
traditional methods of service access, such as in-person interactions and paper-
based  forms.  This  approach  accommodates  diverse  needs  and  digital  literacy 
levels, ensuring inclusivity. 

Moreover, disparities in access to digital services may arise due to factors such as 
gender,  socioeconomic  background,  and  varying  levels  of  digital  literacy 
(Rodríguez  Ayuso,  2023).  These  factors  underscore  the  necessity  for  public 
policies  that  actively  address  and  mitigate  barriers  to  access,  fostering  a  more 
equitable digital landscape. 

82 

 
 
 
 
 
 
Regarding digitalisation  ratios between administrations,  the  divide  may  further 
increase between different regions at European level. Regions with lower levels 
of  digitalisation  risk  falling  progressively  behind  those  with  advanced  digital 
competencies and a higher degree of acceptance of AI-driven technologies. The 
prospect of an uneven development could lead to unequal access to opportunities 
for citizens from different European regions. This uneven distribution of digital 
infrastructure and literacy may inhibit equitable access to emerging opportunities 
associated  with  AI,  hindering  both  technological  innovation  and  societal 
development in less digitally mature regions.  

Finally, an ethical aspect that could be relevant in the near future is the so-called 
‘erosion of human autonomy’. As future AI technologies will be more and more 
pervasive,  the  media,  the  advertising,  and  even  the  personal  decision-making 
could  be  supported  by  the  employment  of  AI  technology.  In  this  context,  the 
overreliance on AI may affect the individual autonomy and lead to a behavioural 
subtle  manipulation  that  influence  choices  and  compromise  human  agency 
(Zuboff, 2019). 

Green AI. Environmental sustainability is a key dimension in the evolution and 
adoption of AI solutions by local public authorities. There are many examples of 
AI  used  to  reduce  energy  consumption  and  CO2  emissions  in  European  public 
administrations. Moreover, AI holds considerable potential as a strategic tool for 
both  European  and  global  governments  in  advancing  the  achievement  of  the 
Sustainable  Development  Goals  (SDGs)  by  2030.  In  particular,  it  can  be  a 
significant booster for 25 targets – equal to 93% of the total – in the goals related 
to environmental topics, i.e., SDGs number 13, 14 and 15 (Vinuesa et al., 2020). 
AI-driven solutions can also contribute to the optimisation of alternative energy 
supply sources, in particular related to renewable energies, from the optimisation 
of  photovoltaic  power  plants  by  analysing  the  sunniest  hours  and  the  most 
advantageous inclination of the panels, to the study of wind flows for wind farms 
(JRC, 2021). Other functions could be found in the monitoring of infrastructures 
for  planning  restructuring  and  efficiency  interventions,  the  optimisation  of 
logistics for reduction of emissions derived from transports, or the enhancement 
of efficacy of irrigation systems and reduction of water waste (Rakha, 2023).  

European legislators are well aware of the capital importance of data sharing for 
an  effective  implementation  of  AI-driven  solutions  in  general  and  for  the 
environmental sector. In addition to the INSPIRE Directive  – forefather of  the 
European  legislation  on  big  data  management  –  the  GreenData4All  Initiative, 
following the path set by the European Strategy for Data, aims to a comprehensive 
upgrading  of  the  communitarian  data  sharing  framework  and  the  creation  of  a 
user-centred and trustworthy ecosystem (JRC, 2023).  Although it has not been 
extensively explored, AI-driven solutions can also be a powerful tool for fulfilling 

83 

 
 
 
 
the ambitions and goals of the  European Green Deal, through the analysis and 
exploitation  of  large  volumes  of  environmental  data,  enhancing  efficiency, 
finding new methods or improving measuring and monitoring (Corrigan & Lucaj, 
2020; Ponti et al., 2024). 

On  the  opposite  side  of  the  coin,  AI  can  itself  be  the  direct  cause  of  energy 
consumption.  As  highlighted  by  the  study  commissioned  by  the  Artificial 
Intelligent in the Digital Age (AIDA) Committee in 2021, AI needs to rely on 
digital infrastructures and data centres, which are major energy consumers. The 
training phase alone of an LLM – of which ChatGPT is the best-known case – can 
consume  up  to  1,287  MWh  for  terabytes  of  data  and  more  than  175  billion 
parameters (de Vries, 2023). This energy consumption results in the production 
of 550 tonnes of CO2, equivalent to what a single person would generate travelling 
by  plane  for  4,538,050  km:  1,100  times  the  trip  between  New  York  and  San 
Francisco  (Crivellaro,  2023).  In  the  future,  it  is  esteemed  that  ICT  will  be 
responsible  for  20%  of  global  energy  consumption  by  2030,  with  a  direct 
contribution to greenhouse gas emissions estimated in the order of magnitude of 
more than one Gigaton per year (AIDA Committee, 2021).  

Although  the  scientific  literature  provides  numerous  examples  of  case  studies 
where AI systems are utilised to mitigate energy consumption and reduce CO2 
emissions,  there  is  a  relative  scarcity  of  research  that  critically  examines  the 
energy consumption directly attributed to AI technologies themselves. Amongst 
these,  Rohde  et  al.  (2024)  propose  a  set  of  guidelines  and  KPIs  to  assess  the 
sustainability of AI-driven solutions. An effort to establish a unified framework 
for  assessing  not  only  the  environmental  but  also  the  social  and  economic 
sustainability  of  AI  solutions  is  exemplified  by  the  Sustainability  Criteria  and 
Indicators  for  Artificial  Intelligence  Systems  (SCAIS)  Framework.  However, 
further  research  on  this  topic  is  required,  along  with  the  development  of 
comprehensive, institution-level guidelines. 

Smart  cities.  For  several  years  now,  the  digital  transition  of  cities  has  been 
accelerating exponentially. The introduction of AI-driven solutions allows public 
administrations  to  offer  better  and  cheaper  services  to  the  residents  (Şerban  & 
Lytras,  2020).  The  streamlining  of  services  allows  greater  availability  and 
possibility  to  allocate  the  available  budget,  directing  funds  to  other  projects to 
meet different needs.  

The  main  fields  of  AI  application  within  the  urban  context  currently  focus  on 
optimising energy consumption, enhancing urban safety, and improving public 
mobility. Examples such as the Municipality of Noisy-le-Grand (France) and 
the  Municipality  of  Sofia  (Bulgaria)  (see  Cases  II.2  and  II.8)  could  be 
considered good examples of AI solutions implemented to benefit citizens.  

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As far as the use of predictive and generative capabilities of AI are concerned, 
even though their potential to contribute to the smart cities approach is undeniable, 
applications  are  currently  more  limited,  both  in  terms  of  the  total  number  of 
solutions adopted and their scope in terms of effects and purposes. This is because 
these kinds of AI applied for public policies and urban dimension may have strong 
implications in political and legal responsibility. 

Predictive AI could allow cities to better address needs and future risks, explore 
new opportunities for urban governance and face emergencies. On the other hand, 
generative  functions may support urban planning processes  and  deepen  citizen 
engagement by facilitating the design of adaptive public spaces. 

According to what emerges from the survey results, many EU municipalities are 
today still unable to exploit these tools, either due to a lack of data, digital skills 
or a lack of political will. 

The analysis of the dynamics of smart cities and urban digitisation systems will 
become increasingly important as the population living within large metropolises 
grows and technological solutions become more advanced and accessible. One 
issue that should not be underestimated is that of the population's acceptance of 
these technologies. It is up to public administrations to ensure that citizens are 
able  to  understand  the  benefits  and  risks  of  the  proposed  solutions,  through 
transparent implementation processes, along with societal engagement.  

Edge AI. The development of AI - especially GenAI - has not followed a linear 
growth model, but rather an exponential one. The possibilities of development are 
constantly increasing. A future perspective is represented by the deployment of 
AI algorithms and computational capacity directly on local devices, such as urban 
sensors,  public  cameras  or  people's  smartphones,  therefore  acquiring  off-line 
capabilities.  This  approach  is  particularly  efficient  for  solutions  that  require 
immediate responses or greater data security in crucial sectors such as healthcare, 
manufacturing, and urban management. 

For instance, as shown by the ARGO project implemented by the Municipality 
of  Turin  (Italy), Edge AI can enable devices such as smart traffic cameras or 
pollution sensors to detect incidents, traffic, crimes or variations in air quality in 
real-time, allowing law enforcement to intervene immediately, without the need 
for data transmission to remote servers (Shi & Dustdar, 2016; Premsankar et al., 
2018). 

Edge AI can also enhance data privacy by keeping sensitive information, such as 
healthcare or social data, in compliance with  the EU data protection standards. 

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Furthermore, Edge AI could also offer the possibility for underserved rural areas, 
possibly having an impact on reducing the digital divide. 

However, Edge AI is also likely to bring new challenges. For instance, storing the 
data locally will increase the vulnerability surface for cyberattacks. 

3.1.3 Megatrends 

The process of adoption of AI systems by EU public administrations – examined 
through the answers given by respondents to the survey and the weak signals – 
can be analysed also under the lens of megatrends. The 14 Megatrends10 identified 
by the EC Competence Centre on Foresight are described as ‘long-term driving 
forces that are observable now and will continue to have a global impact in the 
years to come’.  

In the context of this study, only the megatrends identified as relevant are analysed 
in terms of their impact on AI adoption. 

Accelerating  technological  change  and  hyperconnectivity.  The  impact  of 
technology  and  digital  connectivity  on  human  life  has  grown  exponentially. 
Technology  is  taking  giant  steps  forward  and  through  it  we  are  building 
connections between reality and the digital world, to the point of creating new 
realities  in  the  digital  world.  The  adoption  of  IoT  and  AI  driven  solutions  in 
increasingly smart cities will aliment itself, with a profound impact on the life of 
people.  

Continuing  urbanisation.  The  process  of  moving  from  the  countryside  to  the 
cities in search of better opportunities - jobs, education, services – has been going 
on for centuries, constantly increasing in volume and numbers. The technological 
development of cities and the change of work will further increase the flow of 
people moving from rural to urban areas across the world. The number of people 
living in cities has more than doubled over the last 40 years and is projected to 
reach 5 billion by 2050. While it is true that technology will improve conditions 
for  many,  it  is  also  true  that  digital  divide  may  widen,  and  the  environmental 
consequences of excessive housing density may exacerbate.  

A  similar  trend  is  observed  for  what  concerns  the  increasing  demographic 
imbalances. This growth can lead to an imbalance in urban development, creating 

10  i.e.,  Accelerating  Technological  Change  and  Hyperconnectivity,  Aggravating  Resource  Scarcity,  Changing 
Nature  of  Work,  Changing  Security  Paradigm,  Climate  Change  and  Environmental  Degradation,  Continuing 
Urbanisation, Diversification of Education and Learning, Widening Inequalities, Expanding Influence of East and 
South,  Growing  Consumption,  Increasing  Demographic  Imbalances,  Increasing  Influence  of  New  Governing 
Systems, Increasing Significance of Migration, Shifting Health Challenges. 

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inequalities or divisions between neighbouring areas, increasing the gap with the 
suburbs, and distancing citizens from experiencing the city as one. 

Changing  nature  of  work.  Automation  and  technological  advancements  are 
creating unprecedented challenges and opportunities. Machines have already the 
potential to substitute humans in different fields. At the same time is increasing 
the  need  for  new  digital  and  technological  skills.  The  shift  in  occupational 
structures is leading to polarisation in employment, wages and inequalities. The 
impact of new generations entering the workforce, and the growing digitalisation 
is reshaping the world of work. 

Changing  security  paradigm.  Security  concerns  related  to  digital  evolution 
extend beyond issues of private online security or misinformation on the Internet. 
The  risks  associated with  the  malicious  use  of technology significantly  impact 
citizens' lives. Internet scams, phishing, deepfakes, and online fraud are global 
challenges,  now  commonplace  for  individuals  using  digital  technologies 
(Deloitte, 2024). Additionally, cyberattacks and ransomware targeting the digital 
infrastructures of public administrations have become an escalating issue, with 
direct consequences for citizens' daily lives (Dudziak-Gajowiak & Szleszyński, 
2023).  

Beyond  these  domestic  concerns,  the  digital  evolution  also  carries  substantial 
implications  in  the  international  arena.  The  dynamics  of  political  power  are 
shifting, and warfare is no longer confined to traditional methods or actors. The 
Westphalian paradigm, which once positioned only nation-states as the primary 
combatants in war, is increasingly challenged by non-state actors, who now pose 
military,  political,  and  cyber  threats  to  States.  Technology  has  enabled  these 
actors to expand their scope of attack, amplifying the risk of targeting civilian 
populations  through  hybrid  warfare  tactics.  Financial  systems,  smart  home 
technologies, and urban infrastructures may now become key battlegrounds in this 
evolving landscape. 

AI  can  be  a  valuable  asset  for  national  and  local  governments  to  face  such 
problems, through enhanced data analysis and threat detection. The resilience of 
digital infrastructures is critical to support the technological development of cities 
and regions, and safeguarding the proper functioning of the society.  

Climate change and environmental degradation. Climate change would persist 
even  if  all  emissions  from  human  activities  were  to  cease  abruptly.  However, 
without significantly stronger mitigation efforts, anthropogenic greenhouse gas 
emissions will continue to exacerbate global warming and alter climate patterns. 
Immediate and comprehensive environmental action is essential to mitigate the 
damage  and  avert  the  most  severe  consequences  of  climate  change  and 

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environmental degradation. AI-driven solutions have the potential to facilitate the 
adoption  of  more  efficient  methods  for  reducing  CO2  emissions  and  energy 
consumption, particularly within the context of smart cities.  

Furthermore,  environmental  changes  are  likely  to  amplify  the  increasing 
significance  of  migration,  particularly  in  relation  to  climate  and  economic 
displacement. 

Aggravating  resource  scarcity.  This  megatrend  is  particularly  important  when 
dealing with critical materials as essential for advanced technologies such as AI, 
and it poses a significant challenge to the future development and deployment of 
AI. As demand for AI-driven systems grows, the reliance on rare earth metals, 
semiconductors,  and  other  critical  materials  intensifies.  These  materials  are 
crucial for manufacturing AI hardware such as processors, sensors, and batteries, 
and their scarcity can disrupt supply chains, increase costs, and limit technological 
innovation (Binnemans et al., 2013). Moreover, geopolitical tensions surrounding 
access  to  these  materials  could  lead  to  supply  uncertainties,  hindering  the 
scalability and global reach of AI solutions.  

The growing competition for these resources may also drive environmental and 
ethical  concerns,  as  extraction  practices  often  result  in  significant  ecological 
damage and human rights issues (Rachovides  et al., 2024). In this context, the 
future of AI development could depend on both finding alternative materials and 
advancing recycling technologies, as well as fostering international cooperation 
to ensure equitable and sustainable access to these critical resources. 

Other megatrends, such as the growing consumption of resources, the widening 
of inequalities and the shifting health challenges are going to play a significant 
role in the future of society and global balances. However, the mutual influence 
between these dimensions and the impact of AI in European public administration 
settings appears less significant when compared to the others mentioned above. 

3.1.4 Results of the foresight analysis 

The  exploratory,  empirical-analytical  foresight  analysis  has  been  applied  to 
explore future AI trends and weak signals and analyse their interaction with the 
megatrends  and  their  implications  for  public  administrations.  The  analysis  is 
based on three key elements: trends, identified through the LRAs consultation, 
weak  signals  and  megatrends  as  provided  by  the  JRC.  Trends  reflect  the 
respondents’ view on topics which  are expected to have growing relevance for 
public administrations adopting AI. Of particular importance amongst these are: 
AI integration in public services and efficiency, governance, ethics, and digital 
skills.  

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In  addition  to  identifying  AI  applications  to  enhance  internal  management  of 
public administrations, which is reflected by the primary data collected, a topic 
that is becoming central for LRAs is AI governance. Without a well-defined chain 
of  accountability,  oversight  and  risk  management,  LRAs  face  significant 
constraints in fully integrating AI in their processes.  

Moreover, respondents shared  that it will become more and more important to 
have clear and shared ethical guidelines, appropriate in-house skills, knowledge 
sharing mechanisms as well as solid risk monitoring and management approaches. 
Ethical  concerns,  including  privacy  and  transparency,  are  progressively 
considered more central issues, while risk management and knowledge sharing 
between  administrations  are  seen  as  becoming  critical  for  successful  AI 
implementation. Future concerns about the limitations of AI due to the lack of 
protocols  and practices  for  data interoperability  and quality  control  and  for  AI 
projects scalability, its environmental impact, and the financial resources are also 
noted, with some respondents remaining sceptical about AI's potential benefits. 

Moreover, building on the concept of weak signals, the study analyses the ones 
directly impacting AI implementation at local level. Horizon Scanning is used to 
identify  and  explore  key  weak  signals  for  AI  adoption  by  local  public 
administrations across the EU. The weak signals investigated include ethical AI, 
environmental sustainability through and of AI (Green AI), the potential of AI in 
smart cities, and the application of Edge AI to improve the efficiency and reach 
of AI in areas where digital infrastructures are less developed. Ethical AI focuses 
on the increasing complexity of concerns related to an ethical AI use, while Green 
AI aims to go beyond the sole reduction of energy consumption and emissions 
and  aim  at  making  the  AI  implementation  sustainable  and  environmental 
conscious.  Furthermore,  the  development  of  smart  cities  involves  AI-driven 
solutions to predict actions for energy, safety, and mobility needs, and Edge AI to 
allow local devices to locally process data securely and efficiently, especially in 
regions with limited connectivity.  

The adoption of AI by EU public administrations is also shaped by megatrends, 
which  are  long-term  forces  with  global  impact.  Amongst  them,  ‘accelerating 
technological change and hyperconnectivity’ could enhance the integration of AI 
and IoT in smart cities, while ‘continuing urbanisation’ could increase digital and 
environmental  challenges  in  cities.  Moreover,  ‘the  changing  nature  of  work’, 
leading  towards  automation,  would  eventually  raise  digital  skills  demand  and 
cause workforce polarisation. Concerning the ‘shifting of security paradigm’, AI 
could help address cyber threats and hybrid warfare; and for the ‘climate change’, 
AI  can  support  energy  efficiency  and  sustainability  approaches  and  solutions. 
Finally, ‘aggravating resource scarcity’, particularly related to critical materials 

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for  AI  hardware  development,  could  further  exacerbate  an  equitable  and 
sustainable access to digital devices.  

In conclusion, the analysis reveals that AI adoption by EU public administrations 
is  shaped  by  a  combination  of  forces,  including  technological  advancements, 
ethical issues, and broader societal challenges. Ethical AI and Green AI will be 
crucial  to  ensuring  sustainable  and  responsible  implementation,  while  the 
development of smart cities and the integration of Edge AI can greatly improve 
local  services.  Nonetheless,  concerns  over  AI  governance,  appropriate  digital 
skills, privacy, transparency, and risks persist. Furthermore, megatrends such as 
climate change and urbanisation will drive the need for AI solutions that are both 
inclusive and sustainable. Ultimately, addressing these interconnected challenges 
will be key to maximising AI's potential in the public sector. 

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3.2.1  Overview  of  the  policy  recommendations  on  the  AI 
implementation 

This  section  provides  an  overview  of  existing  recommendations  on  AI 
implementation within the public sector, and also more broadly.  

In general, over the past seven years, European policies and strategies on AI have 
been gradually consolidated into a comprehensive framework which considers the 
ethical,  economic,  and  societal  implications  of  AI.  The  following  paragraphs 
summarise these policy developments as made by the EU. 

By way of example, the ‘Tallinn Declaration on e-Government’ (2017) called for 
EU  Member  States  to  commit  to  provide  efficient  and  secure  digital  public 
services. The Declaration emphasised the value of collaboration amongst Member 
States  to  guarantee  coherent  development,  promoting  simplified  cross-border 
digital  services  and  opening  the  door  for  the  incorporation  of  AI  in  public 
administration. 

The importance of values such as human agency, privacy, fairness, transparency, 
and  accountability,  was  stressed  in  the  ‘Ethics  Guidelines  for  Trustworthy  AI’ 
(2019a) released by the High-Level Expert Group on Artificial Intelligence (AI 
HLEG).  The  Guidelines  established  the  ethical  framework  for  AI  deployment, 
which applies to LRAs.  

The  AI  HLEG  also  issued  the  ‘Policy  and  Investment  Recommendations  for 
Trustworthy AI’ (2019) to supplement the Ethics Guidelines. They are meant to 
give policymakers direction on how to encourage AI investment that is consistent 
with the ethics guidelines.  

The abovementioned document promotes measures that are deemed to be crucial 
for  providing  digital  public  services  that  are  easy  to  use,  cross-border,  and 
interoperable. In order to support the AI uptake, it also encourages Member States 
to  develop  structures  dedicated  to  cooperation  actions,  capitalise  on  funding 
opportunities, and enhance digital infrastructure. 

In  parallel,  the  ‘Coordinated  Plan  on  Artificial  Intelligence’  (2018,  updated  in 
2021)  aimed  to  align  national  AI  strategies  and  create  synergies  in  the  EU, 
ensuring that AI's implementation across Europe is cohesive. The Plan included 
specific recommendations for the public sector, stressing the need to strengthen 
cooperation across the different governmental levels. To better operationalise the 
Plan, the 2021 update added a reference to specific actions to be undertaken by 
Member States, and also provided a specific timeline. Moreover, the 2021 review 
integrated  the  ethics  framework  introduced  by  the  Ethics  Guidelines,  and 

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highlighted  the  importance  of  enhancing  data  sharing,  developing  national  AI 
strategies, and prioritising the workforce upskilling and reskilling. It also called 
for concrete actions to implement AI solutions within public administrations. 

The ‘White Paper on Artificial Intelligence’ (2020c) proposed, for the first time, 
the  risk-based  AI  classification  which  has  become  the  basis  for  the  risk-based 
approach in the EU AI Act. Given the strong focus on ethics and citizens’ rights, 
the White Paper specifies that the public sector is particularly required to ensure 
that  AI  implementation  is  aligned  with  the  ethical  standards  established  by 
previous documents. 

Moreover, the strategy ‘Shaping Europe’s Digital Future’ (2020b), frames the EU 
vision  for  digital  transformation  –  for  which  AI  is  a  central  technology.  The 
initiative  recalls  the  importance  of  digital  skills,  in  particular  for  public  sector 
employees,  recommending  actions  for  enhancing  AI  literacy  amongst  civil 
servants, an aspect that is a key part of the ‘European Skills Agenda’ (2020a). 

Furthermore, to complement the strategic and regulatory framework, European 
funding  for  the  2021-2027  multi-annual  financial  framework  to  support  AI 
research and development is now allocated under the Horizon Europe and Digital 
Europe programmes. Specific topics related to AI are also included transversally 
in  other  funding  instruments,  such  as  the  European  Defence  Fund,  the  LIFE 
programme, InvestEU, and the digital strand of the Connecting Europe Facility, 
along with the structural funds available to the Member States. 

Finally, the  2024  adoption of the  AI Act  marked  the  establishment  of  the  first 
regulatory framework for European regulation of  AI. It is also the world's first 
regulatory  framework  that  classifies  AI  systems  according  to  risk  and  create 
regulations  based  on  these  classifications.  In  order  to  promote  a  cohesive 
implementation  throughout  the  EU,  the  Regulation  puts  forward  a  set  of 
recommendations for the Member States. 

In stressing how national policies are required to be aligned with the objectives 
and principles of the AI Act to ensure coherent AI adoption and implementation, 
the  AI  Act  also  requires  Member  States  to  nominate  or  establish  national 
regulatory  organisations  to  oversee  the  regulation  of  national  implementation. 
Furthermore,  to  support  the  successful  deployment  of  AI,  Member  States  are 
encouraged to fund capacity building initiatives to enhance the AI-related skills 
necessary to fulfil the increasing demand for expertise in the field. 

The AI Act also advocates for robust collaboration between the public and private 
sectors to improve data quality and standardise sharing protocols, thus facilitating 
a unified data ecosystem conducive to AI innovation. Furthermore, it underscores 

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the  importance  of  public  engagement,  encouraging  Member  States  to  promote 
citizen  awareness  about  both  the  benefits  and  risks  associated  with  AI 
technologies.  Finally,  the  AI  Act  recommends  establishing  a  monitoring 
mechanism  to  evaluate  its  implementation  and  track  the  broader  impact  of  AI 
across the EU. 

The following areas can be identified as summary topics addressed thus far by the 
recommendations  directed  to  the  Member  States  through  European  policies, 
strategies, and legal frameworks: 

•  Digital Public Services: The Tallinn Declaration (2017) encourages efficient, 
secure, and collaborative digital services across the EU Member States, setting 
a foundation for AI integration in public administration settings. 

•  Ethical  Framework:  The  Ethics  Guidelines  for  Trustworthy  AI  (2019) 
promotes human agency, privacy, fairness, transparency, and accountability in 
AI deployment. 

•  Policy  and 

Investment 
Investment  Guidance:  The  Policy  and 
Recommendations  (2019)  support  collaborative  AI  environments  by 
supporting  interoperable,  user-friendly  digital  services  with  robust  legal, 
security, and ethical standards. 

•  Coordinated Implementation: The Coordinated Plan on AI (2018, updated 
in 2021) calls for aligned national strategies, data sharing, skills development, 
and specific action timelines for public sector AI use. 

•  Digital  Skills  and  Literacy:  Shaping  Europe’s  Digital  Future  and  the 
European Skills Agenda (2020a, b) emphasise AI literacy and upskilling for 
civil servants. 

•  Funding  for  AI:  The  2021-2027  financial  framework  divides  funding 
between Horizon Europe, Digital Europe, and other EU funds to support AI 
R&D, with a focus on applications in the public sector. 

•  Risk-based  Regulation:  The  White  Paper  on  AI  (2020c)  and  the  AI  Act 
(2024)  introduce  the  concept  of  risk-based  AI  classification  to  ensure  AI 
applications  align  with  the  EU  fundamental  rights  and  ethical  standards, 
especially in the public sector. 

Concerning  the  regional  and  local  perspectives  on  AI  adoption  in  the  public 
sector,  the  CoR,  through  its  rapporteur,  Alberto  Cirio,  presented  an  opinion 
entitled  ‘Challenges  and  opportunities  of  artificial  intelligence  in  the  public 
sector: defining the role of regional and local authorities’ (European Committee 
of the Regions, 2024b). The opinion has been adopted by the CoR plenary on 21st 
November 2024.  

The opinion addresses ethical and inclusive requirements for AI adoption by local 
authorities, focusing on transparency, data management, public engagement, and 

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support  for  underserved  communities.  It  also  calls  for  training  civil  servants, 
creating  AI  test  beds  and  regulatory  sandboxes,  and  ethical  guidelines.  It  also 
prioritises  bridging  AI  disparities,  enhancing  cybersecurity,  and  fostering  AI 
ecosystems for local needs.  Furthermore, the  opinion recalls the importance of 
promoting  human-centred  reliable  AI,  in  line  with  the  values  promoted  at 
European  level,  specifying  that  the  most  effective  approach  is  to  identify  AI 
potential  solutions  based  on  public  consultations,  which  allow  for  a  better 
alignment with the actual needs at societal level. Finally, in terms of addressing 
disparities  at  local  level,  the  opinion  underscores  the  urgency  of  proactively 
addressing the AI divide, with particular reference to rural areas. 

3.2.2 Recommendations 

The  following  recommendations  are  based  on  the  primary  and  secondary  data 
gathered through the desk research and literature review, the survey, the experts’ 
interviews, the case studies, and the foresight analysis.  

To enhance the AI adoption by the  LRAs, there are several topics that emerge 
from the discussion of the factors for the successful implementation of AI (see 
Part 2) and from the foresight analysis (see Section 3.1). 

Governance, leadership and interaction between the political/managerial level and 
the technical level are considered to grow in importance in the future and where 
guidance  on  establishing  models  and  approaches  is  strongly  called  for  by  the 
LRAs. 

Ethics, risk management and monitoring mechanisms are indispensable activities 
to  comply  with  the  European  regulatory  framework  and  that  are  expected  to 
increase their complexity and nuances in the near future. 

Digital skills to address the AI divide and capacity building for civil servants are 
a  clear  necessity  for  the  public  administrations  willing  to  adopt  AI  and  which 
requires continuous actions to follow the technological evolution. 

Public  engagement  and  co-creation  are  fundamental  to  foster  public  trust  and 
alignments with local societal needs. 

Cooperation,  knowledge  sharing  and  scalability  are  crucial  to  ensure  cost-
effectiveness of the AI development, cross-border services and coherent solutions 
across Member States. 

Data  quality,  availability  and 
implementation and fostering cooperation. 

interoperability  are  prerequisites  for  AI 

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Sustainability and environmental impact are also underlined, including both the 
application of AI for advancing sustainability goals and the necessity to reduce 
the environmental impact due to the AI usage. 

Emerging technologies, digital sovereignty and partnerships for innovation, lay 
down  the  basis  for  following  the  innovation  in  the  sector  and  promoting  the 
European leadership in AI development at global level. 

In a lesser extent, funding and investment are also mentioned and will need to be 
increased as the relevance of AI grows. However, several financial instruments 
are already available and cover different aspect of AI implementation. 

Considering these topics and the recommendations already issued in the literature 
on  the  topic,  the  following  recommendations  are  identified  for  enhancing  the 
adoption and effectiveness of AI and GenAI at the local and regional levels within 
the EU: 

Recommendation  1.  The  EU  should  support  LRAs  to  implement  AI 
Monitoring  and  Evaluation  Frameworks  by  outlining  standardised 
frameworks and KPIs, enabling LRAs to track AI’s impact, adjust strategies, 
and align with technological advances. 

This  recommendation  stresses  the  growing  need  for  standardised  monitoring 
practices  and  methods  to  evaluate  AI's  effectiveness  and  alignment  with  local 
needs and technological evolution. While ethics and risk management are largely 
covered by previous recommendations and ongoing work at European level, the 
debate around the issuing of appropriate monitoring mechanisms is still under-
addressed. 

According to the survey results, only a small percentage of LRAs are currently 
evaluating  the  efficacy  and  efficiency  of  their  AI  applications.  As  pointed  out 
during the interviews, many respondents are facing difficulties in the definition of 
precise  KPIs,  mostly  because  the  concept  of  AI  efficiency  itself  needs  further 
clarification. 

Furthermore,  the  survey  results  highlight  the  fact  that,  although  some  LRAs 
acknowledge  the  importance  of  ongoing  assessment  to  modify  AI  projects  in 
response to evolving requirements and technological evolution, this strategy is not 
consistently implemented. The need for EU-provided KPIs is further justified by 
the fact that smaller municipalities, in particular, frequently lack the means to put 
in  place  reliable  monitoring  systems.  A  unified  approach  to  evaluation  would 

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enable more consistent, effective AI integration across diverse regions, enhancing 
transparency, accountability, and policy responsiveness at the local level. 

In  this  context,  the  EU  TSI  can  support  with  specialised  expertise  LRAs  in 
designing  monitoring  and  evaluation  frameworks  which  adapt  to  the  specific 
territorial characteristics, given that a European standardised framework is still 
needed as common bases for all European regions. In addition, Horizon Europe 
can also be employed to propose innovative tools for monitoring and evaluation 
purposes. 

Recommendation 2. To foster public trust and citizen acceptance,  LRAs 
should involve citizens in the AI development process. This should include 
guidance  on  co-creation  design  and  development  methods,  ongoing 
collection  of  feedback,  users  testing  sessions  and  the  promotion  of 
transparent  communication.  This  can  include  online  portals,  public 
workshops, seminars (both virtual and physical), public consultations, and 
explicit  ‘resident-in-the-loop’  development  practices  to  raise  inclusivity, 
promote  the  design  and  development  of  citizen-centric  and  explainable 
solutions,  demystification  of  AI  applications  and  foregrounding  of 
community and societal benefit. 

This recommendation places a particular emphasis on direct public engagement 
at local level. While previous recommendations promote citizen engagement and 
acceptance through ‘Diverse Voices in Policy’ and ‘Transparent, Ethical AI’, this 
strategy  encourages  ongoing  communication  between  people  and  public 
administrations in order to build public trust and go beyond initial participation.  

To  comprehend  particular  local  and  regional  demands,  requirements,  and 
concerns, LRAs should endeavour to participate alongside citizens in the design 
and development of activities that involve them. This may include open digital 
forums, transparent physical workshops, seminars, or complete integration into 
R&D lifecycles. 

LRAs  should  commit  to  radical  openness  and  transparency,  whether  the 
technologies  are  adopted  in  relation  to  the  public  or  not.  This  is  especially 
important in instances where resident data is utilised to improve public services, 
or to extract insights, or value for the public sector authority (e.g., public health 
or environmental data).  

The survey results highlight that citizens lack a clear understanding of AI’s role 
and potential benefits, which can create apprehension and resistance towards AI, 

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particularly  at  the  local  level.  By  engaging  the  public  directly  through 
mechanisms such as co-creation, online portals, public consultations, seminars, 
and workshops, LRAs can address common concerns and work towards building 
confidence in the technologies, and the deployers of the technologies. Moreover, 
such a process can also be useful as an educational tool for people, explaining 
data strategies and specific technological concepts, as well as the implications of 
specific technology deployments and associated choices that LRAs will choose in 
their deployment as it is quite gradual and stepwise. 

Another  interesting  aspect  emerging  from  this  study  is  the  predisposition  for 
people to be more willing to accept and support AI implementation and appreciate 
its  tangible  (and  intangible)  benefits  when  they  are  actively  included  in  the 
discussions. Additionally, feedback gathered from these interactions can allow to 
refine AI strategies, making them more tailored to local needs and values. This 
approach  does  not  only  build  trust,  but  also  encourages  a  community-centred 
perspective  on  AI,  making  local  projects  more  suitable  to  public’s  specific 
expectations and concerns. 

For  this  purpose,  the  Digital  Europe  programme  can  be  used  to  create  digital 
platforms that facilitate public engagement, such as online portals and co-creation 
forum or tools to support public feedback collection. 

Recommendation  3.  The  EU  and  Member  States  should  encourage  the 
establishment  of  regulatory  sandboxes  across  regions,  allowing  LRAs  to 
legally  compliant 
experiment  with  AI  solutions  within  controlled, 
environments.  Test  beds  and  living  labs  would  further  support  local 
governments  in  evaluating  AI  applications’  effectiveness  before  full 
deployment. 

A  concept  that  has  not  been  addressed  with  sufficient  attention  in  previous 
guidelines  is  the  possibility  to  adopt  regulatory  sandboxes  and  test  beds, 
contributing  to  structured  oversight  and  accountability  as  key  components  of 
robust governance frameworks. It is useful to regulate and assure compliance for 
testing  environments  for  emerging  AI  technologies.  By  enabling  real-world 
testing of AI applications, this strategy can help LRAs understand the dangers and 
limitations of the  technology, prior to its widespread implementation. National 
governments  should  enhance  sandboxing  procedures  for  data  governance  and 
processing  across  departments  and  regions.  This  could  entail  establishing 
specialised  functional  sub-departments  to  enhance  compliance  procedures, 
encourage  more  innovation  and  research  amongst  LRAs,  and  capitalise  on  the 
implicit and explicit value of data. 

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The data demonstrate that while many LRAs are interested in adopting AI, they 
face significant uncertainty regarding compliance with regulatory standards, risk 
management, and the practical effectiveness of AI applications. Smaller LRAs, in 
particular,  often  lack  the  resources  to  fully  evaluate  AI’s  impacts  or  to  ensure 
compliance with complex EU regulations without first testing these technologies 
in a low-risk setting. 

Establishing  regulatory  sandboxes  would  allow  LRAs  to  experiment  within  a 
framework  that  maintains  legal  and  ethical  standards,  fostering  innovation 
without risking non-compliance. The data also reveal that some LRAs hesitate to 
fully implement AI solutions due to concerns about unforeseen consequences and 
lack  of  real-world  data  on  AI’s  local  impact.  Test  beds  and  living  labs,  i.e., 
structured  environments  where  AI  can  be  piloted,  would  address  this  gap  by 
providing  LRAs  with  the  opportunity  to  assess  AI’s  utility,  gather  data  on  its 
outcomes,  and  refine  applications  before  broader  deployment.  In  support  of 
LRAs,  the  Horizon  Europe  programme  is  the  financial  instrument  that  the  EU 
makes available for the creation and implementation of regulatory sandboxes and 
test beds to conduct experimentations with AI. 

Moreover, such experimentation environments could support the testing of GenAI 
solutions, which are currently not employed at local level, due to its numerous 
legal implications. 

Ultimately,  this  recommendation  aims  at  mitigating  the  challenges  of  AI 
deployment within complex regulatory frameworks, and seeks to empower LRAs 
with controlled spaces to innovate responsibly and effectively, aligning with the 
EU’s goals for ethical and well-regulated AI adoption at all administrative levels. 

Recommendation  4.  To  prevent  AI  adoption  from  worsening  digital 
inequality  and  help  address  digital  divides,  LRAs  should  implement 
inclusive practices, such as online guidance, training for vulnerable groups, 
and affordable access to digital tools. Member States could facilitate these 
initiatives by offering resources and policy support. 

Although  ‘Training  and  Bridging  Digital  Divides’  was  noted  in  previous 
recommendations, this recommendation adds specificity by suggesting targeted 
inclusive practices for digitally disadvantaged groups, such as online guidance, 
affordable  access,  and  training  programmes  that  specifically  mitigate  digital 
inequality in AI adoption. 

Research results also show that many  LRAs  struggle to ensure equal access to 
digital  services  for  the  entire  population.  Different  segments  of  the  population 

98 

 
 
 
 
 
 
 
 
may  not  have  the  digital  skills  or  resources  needed  to  take  advantage  of  AI 
developments. Disparities may result from this digital divide, with some groups 
disadvantaged or excluded from emerging AI-based public services. 

In order to encourage wider public acceptance and reduce the danger of exclusion, 
LRAs should proactively address these disparities by providing tailored assistance 
to  ensure  that  the  benefits  of  AI  are  available  to  all  residents.  Furthermore,  as 
smaller LRAs may not have the funds or infrastructure to carry out these measures 
on  their  own,  it  is  crucial  that  national  governments  provide  resources  and 
political support.  

The European Social Fund Plus (ESF+) offers opportunities to finance training 
programmes to improve the digital skills of vulnerable groups. This can also be 
coupled  with  the  InvestEU  instrument  which  can  support  the  provision  of 
affordable digital technologies and infrastructure to underserved communities. 

In  line  with  the  EU's  overarching  goals  of  justice  and  equality  in  digital 
transformation, this collaborative effort would reduce the social risks associated 
with AI deployment and promote digital inclusion. 

Recommendation 5. The EU, together with Member States, should facilitate 
partnerships between public, private, and academic sectors that can support 
LRAs access advanced technologies and expertise. 

recommendations 

The  previous 
include  cross-border  ecosystems  and 
collaborative  projects  but  do not  explicitly  address  partnerships  with  academic 
institutions and private entities within regions to strengthen local capabilities. This 
recommendation fills that gap by advocating for interdisciplinary collaboration 
with academia and industry, especially to bolster under-resourced LRAs. 

According to the survey results, only about 27% of LRAs are currently adopting 
AI,  with  larger  LRAs  generally  being  more  active  in  implementation.  Smaller 
LRAs face significant hurdles, particularly in terms of limited human resources, 
technical expertise, and financial capacity, which restrict their ability to leverage 
AI  technologies  effectively.  Partnerships  with  external  entities  (private  and 
academic sectors) could help bridge these gaps by providing essential knowledge 
and resources for smaller LRAs to adopt and integrate AI. In this sense, LRAs 
should also consider the opportunities offered by the Horizon Europe programme 
that foster the creation of such partnerships for innovation.  

Beyond  addressing  internal  challenges,  these  partnerships  foster  a  dynamic 
ecosystem  where  public,  private,  and  academic  sectors  collectively  enhance 

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public  service  delivery  and  citizen  engagement.  Private  sector  expertise  in 
technological  development  and  academia’s  role  in  researching  user-centric  AI 
applications can support LRAs to align AI solutions to specific place-based needs, 
also creating opportunities for pooling resources and sharing infrastructure, which 
significantly benefits smaller LRAs with budget constraints.  

To overcome the significant skills gap identified amongst LRAs, the EU and its 
Member  States  should  prioritise  funding  for  AI  and  digital  literacy  training. 
Furthermore,  offering  incentives  for  AI  certifications  could  help  public 
administration  staff  build  relevant  skills,  particularly  in  smaller  municipalities 
where resources are often more limited. 

Given the challenges related to data interoperability and security, the EU and its 
Member  States  should  focus  on  developing  and  enforcing  standardised 
frameworks.  These  frameworks  would  facilitate  secure  data  sharing  between 
LRAs,  thus  enhancing  the  effectiveness  of  AI-driven  public  services  across 
jurisdictions. 

Finally, this kind of partnerships allows LRAs to follow the evolution of emerging 
AI  technology,  such  as  Edge  AI,  and  support  the  advancing  of  a  European 
leadership  in  the  sector  and  explore  new  solutions  for  fostering  equitable 
opportunities to benefit from the AI for all citizens in Europe. 

Recommendation 6. National governments should prioritise integration 
at the local and regional level of open standards, software, and hardware, 
which  contribute 
to  ensuring  high-quality  data  and  seamless 
interoperability.  This  should  include  investment  in  IT  infrastructure  to 
support  permissive  research  and  innovation.  This  might  include  the 
upgrading of systems, porting of existing systems, investing in modern 
alternatives with  improved  functionality, that adhere  to  open standards 
for  data  exchange  and  management.  Supporting  the  deployment  of 
harmonised  open-source  technologies  (regionally/nationally)  will  not 
only improve inter-regional scalability and compatibility but also enhance 
data  integrity,  consistency,  and  interoperability  across  administrative 
boundaries.  This  will  promote  openness,  improve  public  trust,  provide 
clear pathways for inter-regional scalability, and remove private sector 
dependencies  –  especially  those  linked  to  proprietary  software  and 
hardware, and their aforementioned licencing models.  

The case studies highlighted the benefits of developing IT infrastructure on open 
software  and  permissively  licenced  technologies.  This  is  especially  true  in  the 

100 

 
 
 
 
 
 
 
case of technologies that support the integration of AI into the wider public sector 
administration.  Removing  long-term  dependencies  on  proprietary  technology 
should  be  a  specific  focus  of  governments  (both  nationally  and  regionally),  to 
increase  national  and  European  technological  sovereignty,  as  well  as  to  aid 
interoperability, security, and European harmonisation.  

Integrating modern IT infrastructure, while carrying a predominant capital cost, 
will work towards  future proofing public administration  for  the  deployment of 
specific AI technologies. This might include technologies such as Edge AI, smart 
city integration, the integration of confidential computing, federated learning, or 
the  deployment  of  specific  technologies  to  aid  security  such  as  homomorphic 
encryption or quantum-proofing.  

Focus on integrating open and permissive AI technologies should also include the 
promotion and deployment of European focused foundational, and/or language 
AI  models,  such  as  those  within  projects  and  consortiums  such  as  Euro-LLM 
(Euro-LLM, 2024), OpenGPT-X (OpenGPT-X, 2024), and SiloGEN (SiloGEN, 
2024). This effort would also support the ongoing foregrounding of inclusivity, 
as Member State language support would be integrated. An improved focus on 
moving  away  from  proprietary  (and  mostly  US  big-tech  owned  and  operated) 
models will also promote European digital sovereignty. This effort will support 
research,  innovation  and  societal  development  whilst  maintaining  European 
ethical  principles,  respecting  European  fundamental  rights,  and  promoting  the 
notion  of  European  leadership  and  strategic  autonomy  in  the  digital  field 
(Madiega, 2020). 

Recommendation  7.  LRAs  should  invest  in  creating  stronger  ties  across 
various internal departments, especially between management and technical 
IT  teams,  also  finding  more  effective  way  of  interaction  between  the 
technical and managerial/political levels. This will foster AI adoption and 
improve  cooperation,  collaboration,  flexibility,  and  resilience  in  design, 
development, and deployment.  

The  case  studies  highlighted  the  need  for  LRAs  to  develop  appropriate 
governance procedures for both inter-departmental and inter-regional data sharing 
–  especially  when  that  processing  is  beyond  the  normal  remit  of  one  or  more 
departments  or  entities,  or,  more  importantly,  traversing  across  various 
stakeholder  grouping  (e.g.,  public,  private,  academia,  etc.).  While  expertise  is 
developing at the local level on this data governance, it should be supported by 
improved  efforts  directed  at  creating  cohesive  relationships  across  regions  and 
departments, as appropriate. This may include cross-departmental and/or national 
workshops, seminars, or curated events that support network creation, relationship 

101 

 
 
 
 
 
 
building,  or  expose  representatives  to  success  stories  (or  open  challenges)  that 
inspire or direct the creation of new, improved, processes. This effort can be tied 
into the development of regulatory sandboxes (see Recommendation 3).  

Recommendation  8.  The  EU  and  Member  States  should  further  explore 
how AI could support the local level to contribute to the European Green 
Deal and to the United Nations’ environmental-focused SDGs. In parallel, 
more  research  is  needed  to  minimise  the  environmental  impact  of  AI  by 
fostering AI systems that are designed and deployed sustainably. 

Concerning the application of AI to pursue international environmental goals, as 
local  authorities  can  meaningfully  contribute  to  the  implementation  of 
sustainability  initiatives  directly  impacting  communities,  ecosystems,  and 
regional development, the establishment of environmentally-conscious practices 
is especially important (Rolnick et al., 2019). This will also ensure that LRAs, 
independently  by  their  size  and  financial  resources,  can  optimise  efforts  for 
enhancing their environmental resilience. 

Moreover,  according  to  the  results  of  the  foresight  analysis,  given  the  rapid 
development that AI technology will undergo in the next future, it will be equally 
important  to  advance  the  research  on  how  it  is  possible  to  reduce  the 
environmental footprint of AI. In fact, AI systems are moving towards large-scale 
models that will likely pose significant challenges to the energy consumption and 
environmental  protection  (Strubell,  Ganesh,  and  McCallum,  2019).  This  is  an 
aspect  that  is  still  underexplored  in  the  ongoing  policy  debate  around  AI  at 
European level. 

On this matter, it is possible to exploit the clear synergy with the aims of the LIFE 
programme,  which  is  dedicated  to  funding  environmental  and  climate-related 
initiatives. 

3.3 Conclusions of the study 

This  study  provides an overview of  the opportunities and challenges of the AI 
adoption by local and regional administrations. Starting from a literature review 
and a presentation of the state of play of AI adoption by LRAs across the EU, the 
study investigates critical drivers and best practices for the successful uptake of 
AI in the public sector at local level.  

102 

 
 
 
 
 
 
 
 
 
AI  adoption  rates  are  found  to  be  on  the  rise  amongst  LRAs,  particularly  its 
application for improving the efficiency of internal management. However, the 
study  findings  highlight  a  potential  correlation  between  the  size  of  the 
administrations and the availability of skills and financial resources which lead to 
unequal  degrees  of  integration  of  AI  in  the  organisations’  processes  across 
Europe. 

Related  to  this  finding,  the  topic  of  capacity  building  for  AI  in  the  public 
administrations is of paramount importance to effectively manage and deploy AI 
technologies,  ensuring  that  they  can  adapt  to  evolving  digital  landscapes  and 
address emerging challenges. 

In  this  sense,  a  model  that  can  contribute  to  addressing  the  skills  gap,  whilst 
fostering innovation and scalability, is the creation of partnerships between local 
authorities, academia, and the private sector. 

A critical point of the research is the need, from a local perspective, to enhance 
the alignment between AI development and the specific local priorities, stressing 
the necessity to focus on societal engagement through co-creation practices and 
citizens’ feedback collection. 

Similarly, more efforts are required to tackle the AI divide and spread inclusive 
approaches  to  AI,  which,  if  not  addressed  proactively,  may  lead  to  significant 
disparities across the EU LRAs. 

Furthermore,  ethic,  legal  and  governance  concerns  are  perceived  as  a  major 
obstacle to the full exploration and exploitation of the AI technology. Therefore, 
more guidance at European and national level is strongly advocated by LRAs.  

Finally,  it  is  crucial  to  foster  the  debate  on  the  impact  of  AI  in  enhancing  the 
environmental  resilience  at  the  local  and  regional  level,  both  in  terms  of  its 
application in energy optimisation, waste collection and climate monitoring and 
also to reduce the energy-intensive processes of training and deploying large AI 
models. 

To further support these findings, it may be interesting to compare these results 
with  a  publication  of  the  JRC  dated  to  November  2024  entitled  ‘What  factors 
influence perceived artificial intelligence adoption by public managers?’  (JRC, 
2024). This report investigates the state of play of the AI adoption by the public 
sector,  focusing on  identifying factors  that  promote or  hinder  the AI  adoption, 
found to be relevant both at national and local levels. 

103 

 
 
 
 
 
 
 
 
 
The JRC publication shares several findings with the present study. First of all, 
the research used the TOE framework complemented with individual  attitudes, 
which is used to understand the technology adoption dynamics through these three 
elements.  According  to  the  research  results,  which  are  based  on  a  survey 
conducted in seven EU Member States, it is found that the internal factors are the 
most crucial for the AI adoption, and in particular leadership support, innovative 
culture,  and  in-house  expertise.  These  conclusions  reflect  this  study  findings, 
which  highlight  similar  drivers  for  the  AI  adoption  such  as  the  centrality  of 
individual  managers  as  initiator  of  the  AI  projects  (see  Section  1.2.2)  and  the 
importance of enhancing an effective communication in between the technical and 
the managerial/political levels (see Section 2.3). 

Moreover, the JRC study largely stresses citizens’ role in AI adoption. According 
to  its  results,  public  administrations,  when  developing  AI  projects,  try  to 
anticipate citizens’ needs and potential demand, as they are considered as the main 
stakeholders of the technology. However, consultation and co-creation activities 
are  not  particularly  spread  as  practices.  This  conclusion  aligns  with  this  study 
findings, pointing to the citizens engagement and acceptance as one of the main 
drivers for the success of the AI adoption in the public sector (see Section 2.4). 

To  conclude,  it  is  important  to  note  that  both  studies  recommend  the 
implementation  of  context-sensitive  strategies  to  bridge  gaps  in  expertise, 
governance, and public acceptance. 

104 

 
 
 
 
Annex I – The online survey  

The questionnaire is structured into three sections, aimed at collecting evidence 
on the following: 

•  General data on the respondents; 
•  Data on the implementation of AI/GenAI initiatives by LRAs to identify 
drivers, barriers, opinions and best practices on the AI adoption by LRAs; 
•  Views  on  the  future  of  AI  for  public  administrations  to  support 

policymaking. 

The design of the questionnaire considered the research questions to which the 
study aims to respond, as well as the gaps the research group deems relevant to 
fill to come up with a final evaluation of the implementation of the AI at the local 
and  regional  level  and  its  potential  improvement.  The  respondents  have  been 
invited via e-mail to participate in the questionnaire. The questionnaire ran for six 
weeks. The tool used for the technical implementation of the online questionnaire 
is EUSurvey (https://ec.europa.eu/eusurvey/home/welcome).  

Introduction to the survey 

The European  Committee  of  the  Regions (CoR),  representing  local  and 
regional authorities in the European Union, has tasked FORMIT and Trilateral 
Research to conduct a questionnaire and a study to investigate the state of play 
and the key factors for the successful AI and GenAI adoption at local and regional 
level. 

The  survey  is  structured  into  four  sections.  It  takes  around  15  minutes  to  be 
completed. The survey is available online in English, French, German, Italian, 
Polish  and  Spanish.  It  will  remain  open  until  15  October  2024.  For  more 
information, please contact ricercaeinnovazione@formit.org. 

Individual contributions provided through the open questions of the survey may 
be eventually used in the study but in an anonymised form. Fondazione FORMIT 
standard privacy statement applies. 

The  follow-up  to  your  contribution  requires  that  your  personal  data  (name, 
contact details, etc.) be processed in a file. Participation in the consultation is 
voluntary. Should you require any further information or wish to exercise your 
rights  under Regulation  (EU) 2016/679  (e.g.,  to access, rectify,  or  delete  your 
data),  please  contact  the  data  controller  (CoR; studies@cor.europa.eu)  or  the 
FORMIT; 
data 

(Fondazione 

processor 

survey 

this 

of 

105 

 
 
 
 
 
 
 
 
 
or 

Trilateral 

Research; 
ricercaeinnovazione@formit.org 
dpo@trilateralresearch.com).  You  have  the  right  of  recourse  to  the  European 
Data Protection Supervisor at any time (www.edps.europa.eu). For any further 
information on data protection policy and the use of your contributions, please 
consult the following legal notice: CoR Data Protection Notice (full version) and 
FORMIT  Privacy  Statement  (full  version).  Please  note  that  the  answers  you 
provide can be used, in an anonymised form for drafting a report. That report 
could be transmitted to CoR rapporteurs and other EU institutions and  used in 
CoR studies and publications. If you do not wish so, please inform us accordingly. 

* 
☐ I hereby consent to the transfer to the European Committee of the Regions (‘the 
Committee’)  of  the  personal  data  (first  name;  last  name;  organisation; 
role/position;  e-mail)  that  I  submit  in  the  present  survey,  to  be  used  by  the 
Committee for canvassing purposes. The CoR will retain data collected on the 
occasion of the study for a maximum of two years after the prospection. However, 
the personal data of survey respondents having refused to be contacted around 
survey follow-up activities will be deleted two months after the completion of the 
study.  For  any  further  information  on  the  Committee  data  protection  policy, 
please consult the following legal notice: Data Protection Notice (full version). 

Section 1 – Details about the respondent  
(Questions marked with * are mandatory) 

Q1.1 – Information about the respondent 
First name: [Open answer]______________________ 
Last name: [Open answer]______________________ 
Email*: [Open answer]______________________ 
Role/position*: [Open answer]______________________ 
Organisation*: [Open answer]______________________ 
Country where the organisation is based*: [drop down menu with the EU27 
Member States] 
Region where the organisation is based*: [drop down menu with the names of 
NUTS2 of the selected Member State] 
City where the organisation is based*: [Open answer] ______________________ 

Q1.2 – Your organisation is:* [only one answer is allowed]  
•  A local public authority. 
•  A regional public authority. 
•  A local / regional agency working for a public authority. 
•  Other. [please specify] 

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Q1.3  -  Does  your organisation use  any AI  and/or GenAI  solution?*  [only one 
answer is allowed] 
•  Yes. 
•  No. [go to Q1.30] 
•  I don’t know. [go to Q1.30] 

Section 2 – Implementation of the AI in the public administrations  

Q1.4 - In your organisation, what is AI and GenAI mainly used for?* [multiple 
answers are allowed] 
•  To enhance decision-making processes and outcomes in policymaking (e.g., 
to detect social problems faster than using traditional techniques, to monitor 
the implementation of policy or to enhance citizen participation, to identify 
trends and patterns that could be used for policy and predicting trends). 

•  To improve the delivery of public services and citizen/government interaction, 
by  providing  more  efficient  and  effective,  tailor-made  or  altogether  new 
citizens’ services, increasing trust and participation by citizens in public sector 
activities. 

•  To  optimise  internal  management  (e.g.,  by  better  allocating  human  and 

financial resources or improving the security of public administrations). 

•  I do not know. 
•  Other. [please specify] 

Q1.5  -  For  which  sector  of  your  organisation  is  the  AI  and/or  GenAI  used?* 
[multiple answers are allowed] 
•  Municipal public services (e.g., road maintenance, waste management, green 

spaces management, streets lighting). 

•  Energy and sustainability. 
•  Health. 
•  Public order and safety (e.g., crime control, emergency management). 
•  Social protection. 
•  Environmental protection. 
•  Education. 
•  Mobility and transport. 
•  AI-based services for the public administration. 
•  AI-based services for citizens. 
•  Other. [please specify] 

Q1.6 - Who was the initiator of the AI/GenAI adoption in your organisation?* 
[only one answer is allowed] 
•  An individual expert in the field. 
•  A manager. 

107 

 
 
 
 
 
•  An in-house agency. 
•  A partnership. 
•  I do not know.  
•  Other, including external subject/actor. [please specify] 

Q1.7 - In your organisation, is there any dedicated office/person in charge for the 
AI development/adoption/integration/monitoring?* [only one answer is allowed] 
•  Yes. [go to Q1.8] 
•  No. [go to Q1.9] 
•  I do not know. [go to Q1.9] 

Q1.8  -  Please  indicate  which  office  and/or  role(s)  is  involved  in  AI 
development/adoption/integration/monitoring.* [open question] 

Q1.9  -  Are  the  following  recommendations  being  adopted  and  implemented  in 
practice by your organisation?* [multiple answers are allowed] 
•  Promote the adoption of ethical principles (e.g., human-centric and trustworthy 
AI) and identify mitigating measures to minimise the risks of deployment of 
AI by the public sector. 

•  Promote  co-creation  approaches  for  AI  development  with  citizens  and 

businesses. 

•  Promote awareness on potential positive impact of emerging technologies in 

the public sector. 

•  Promote capacity-building programmes for public sector innovators aiming to 

develop and/or adopt AI for the public services.  

•  Promote the use of regulatory sandboxes for public administrations, allowing 

experimentation of AI-enabled solutions. 

•  Optimise funding in support of AI in the public sector. 
•  Support  multidisciplinary  research  with  universities  and  Research  and 

Development institutions around AI for the public sector. 

•  Reinforce and advance existing initiatives on open data and interoperability.  
•  Develop and apply impact assessment frameworks based on key influencing 

factors to measure the use and impact of AI in the public sector. 
•  Develop sustainable AI in compliance with environmental principles. 
•  None of the above. 
•  I do not know. 

Q1.10 - Did your organisation organise any of the following actions in the process 
of developing the AI?* [multiple answers are allowed] 
•  Regulatory sandboxes. 
•  Test beds. 
•  Living labs. 

108 

 
 
 
 
 
•  Best practices exchange. 
•  I do not know. [go to Q1.12] 
•  Other. [please specify] 
•  No. [go to Q1.12] 

Q1.11 - Please provide us with the name and weblink of the preliminary initiative 
for the AI adoption most relevant to you. If there is no weblink, please describe in 
two lines the initiative.* [open question] 

Q1.12 – In your opinion, what are the drivers of the AI and GenAI adoption by 
public services at subnational level?* [scoring on Likert scale] 
•  Improved  Public  Service  (e.g.,  responsive,  efficient  and  cost-effective 
services; increase quality of information and services; public citizen-centred 
services; personalised services; new services or channels). 

•  Improved  Administrative  Efficiency  (e.g.,  improved  management  of  public 
resources;  increased  quality  of  processes  and  systems;  responsiveness  of 
government  operation;  cost  reduction;  better  collaboration  and  better 
communication; 
transparency,  participation;  enabling  greater 
increase 
fairness, honesty, equality). 

•  Open  Government  Capabilities  (e.g.,  increased  transparency  of  public 
operations; increased participation in government; improved public control on 
government). 

•  Other. [please specify] 

Q1.13  -  In  your  organisation,  which  of  the  following  AI-related  skills  and 
competences are present?* [multiple answers are allowed] 
•  Technical qualification (e.g., data science). 
•  Skills on ethical and transparency requirements. 
•  Basic knowledge on AI. 
•  Management of procurement activities. 
•  I do not know. 
•  Other. [please specify] 

Q1.14 - What can be done to fill potential AI-related gaps in the personnel of your 
organisation?* [scoring on Likert scale] 
•  Attracting/Hiring people with the right skill sets. 
•  Internal training staff provided by external contractor.  
•  Creation of an in-house agency. 
•  National initiative for AI skills in public administration. 
•  Work  together  with  university  to  develop  IT  specialist  courses  for  civil 

servants. 

•  Public funding for civil servants to pursue private certified AI training courses. 

109 

 
 
 
 
 
Q1.15 - Are you outsourcing any AI development?* [only one answer is allowed] 
•  Yes. [go to Q1.16] 
•  No. [go to Q1.17] 
•  I do not know. [go to Q1.17] 

Q1.16  -  How  do  you  keep  a  balance  between  internal  resources  and 
outsourcing?* [only one answer is allowed] 
•  By developing the project entirely internally, with a minor contribution from 

external suppliers. 

•  By building a system on top of the AI system offered by a private supplier in 

order to adjust the results to the need of the organisation. 

•  By developing AI in collaboration with the academia. 
•  Other. [please specify] 

Q1.17  -  How  did  you  keep  the  AI development  inhouse?*  [only  one  answer  is 
allowed] 
•  We already had the right skillset inhouse. 
•  We have an in-house agency. 
•  We hired new people with the right skills.  
•  Other. [please specify] 

Q1.18 - Is your organisation working on data interoperability and security for 
enabling the AI to work properly?* [only one answer is allowed] 
•  Yes. [go to Q1.19] 
•  No. [go to Q1.20] 
•  I do not know. [go to Q1.20] 

Q1.19  -  Which  actions  did  your  organisation  put  in  place  for  this  purpose?* 
[multiple answers are allowed] 

•  Agreements with other public organisations for data interoperability. 
•  Agreements with other private organisations for data interoperability. 
•  Alignment with regulatory authorities. 
•  Overcoming  ontology 

fragmentation  and  consolidating 

semantic 

interoperability. 
the 

•  Addressing 

legal  consistency  amongst  different  national  and 

international systems. 
•  Other. [please specify] 

Q1.20  -  Since  the  adoption  of  the  AI  can  increase  the  digital  divide,  which 
measures does your organisation put in place to enable AI to include digitally 
disadvantaged groups?* [multiple answers are allowed] 

110 

 
 
 
 
 
 
•  Specific training to vulnerable groups on AI in public services. 
•  Online guidance and assistance to their interaction with AI. 
•  Develop  Explainable  AI  (XAI)  systems  that  provide  laymen  users’ 

explanations for AI-driven decisions. 

•  Support the affordable access to AI tools and technologies to help tackle the 

digital divide. 

•  Awareness raising campaigns to spread the ability to distinguish AI generated 

content and debunking fake news on AI. 

•  Other. [please specify] 
•  None of the abovementioned. 

Q1.21 - According to your organisation’s experience, can you mention any best 
practices for managing and scaling AI and GenAI projects within LRAs? Please 
provide us with the name and weblink of the initiative most relevant to you. If 
there is no weblink, please describe the initiative in two lines. [open question] 

Q1.22  -  Does  your  organisation  have  partnerships  with  the  private  sector,  
academia, and other stakeholders?* [only one answer is allowed] 
•  Yes. [go to Q1.23] 
•  No. [go to Q1.24] 
•  I do not know. [go to Q1.24] 

Q1.23  -  Please provide  us  with  the  name and  weblink  of  the  partnership  most 
relevant  to  you.  If  there  is  no  weblink,  please  describe  in  two  lines  the 
partnership.* [open question] 

Q1.24  -  How  are  ethical  considerations  or  guidelines  for  the  AI  and  GenAI 
implementation  processes  integrated  in  AI  development  and  implementation?* 
[multiple answers are allowed] 
•  Ethics by design. 
•  Ethical data sourcing (i.e., obtaining data in a way that respects individuals’ 

privacy, consent, and applicable data rights). 

•  Proper  data  management  for  AI  tools  involves  secure  storage,  controlled 

access, and regulated deletion practices. 

•  Establish guidance documents in line with European criteria. 
•  Establish an accountability framework, so there are clear lines of responsibility 

if the AI system fails or causes harm. 

•  AI ethics review. 
•  Training on ethics principles. 
•  Gather feedback on possible biases from the users. 
•  Continuous monitoring of ethics. 

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•  Other. [please specify] 
•  There is no ethics integration. 

Q1.25  -  According  to  your  opinion/experience,  which  are  the  most  effective 
funding mechanisms that support AI and GenAI initiatives at local and regional 
level?* [multiple answers are allowed] 
•  EU Recovery and Resilience Facility (RRF).  
•  European  Digital  Innovation  Hubs  (funded  under  the  Digital  Europe 

Programme). 

•  Other calls under the Digital Europe Programme. 
•  Horizon Europe Programme. 
•  Other European structural and investment funds (e.g., ERDF, ESF+).  
•  Connecting Europe Facility for digital infrastructure. 
•  Other. [please specify] 
•  I do not know. 

Q1.26 - Does your organisation assess the effectiveness and efficiency of AI and 
GenAI applications currently in use?* [only one answer is allowed] 
•  Yes. [go to Q1.27] 
•  No. [go to Q1.28] 
•  I do not know. [go to Q1.28] 

Q1.27 - Can you explain how? [open question] 

Q1.28 - Does your organisation foresee a continuous monitoring and evaluation 
of AI and GenAI projects to adapt to evolving needs and technologies?* [only one 
answer is allowed] 
•  Yes.  
•  No. 
•  I do not know. 

Q1.29  -  Does  your  organisation  foresee  the  gathering  of  public  feedback  for 
shaping the AI and GenAI strategies of LRAs?* [only one answer is allowed] 
•  Yes.  
•  No. 
•  I do not know. 

Section 3 – AI for public administrations perspectives  

Q1.30  –  According  to  your  opinion,  which  uses  of  AI  and  GenAI  should  be 
prioritised or avoided in the future in order to maximise the benefits of AI use?* 
[scoring on Likert scale from prioritised to avoided] 

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•  To enhance decision-making processes and outcomes in policymaking (e.g., 
to detect social problems faster than using traditional techniques, to monitor 
the implementation of policy or to enhance citizen participation, to identify 
trends and patterns that could be used for policy and predicting trends). 

•  To improve the delivery of public services and citizen/government interaction, 
by  providing  more  efficient  and  effective,  tailor-made  or  altogether  new 
citizens’ services, increasing trust and participation by citizens in public sector 
activities. 

•  To  optimise  internal  management  (e.g.,  by  better  allocating  human  and 

financial resources or improving the security of public administrations). 

•  Other. [please specify] 

Q1.31 - According to your opinion, what are the main challenges and barriers 
faced by your organisation in the adoption and implementation of AI and GenAI 
technologies?* [scoring on Likert scale] 
•  Lack of skills and expertise. 
•  Lack of data interoperability. 
•  Complexity. 
•  System integration issues. 
•  Compliance with laws and regulations. 
•  Budget constraints. 
•  Quality of data. 
•  Lack of awareness/knowledge on standardisation processes. 
•  Lack  of  engagement  with  stakeholders  (e.g.,  private  actors,  regulatory 

authorities, academia). 

•  Lack of political interest in the AI adoption. 
•  I do not know. 
•  Other. [please specify] 

Q1.32  -  According  to  your  opinion,  how  can  political  leaders  at  local  level 
support successful AI and GenAI adoption at the local level?* [open question] 

Q1.33  -  Concerning the future  of AI and GenAI, do you see any trend for this 
technology or lessons learnt that can be used to improve the use and adoption of 
AI and GenAI at regional and local levels?* [open question] 

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Annex II – Eight case studies 

This Annex reports the eight case studies on the use of AI by LRAs. 

1.  De  Virtuele  Assistent  (‘The  Virtual  Assistant’)  (Municipality  of  Kortrijk, 

Belgium, 2022-2025, AI-based services for citizens). 

2.  Green  public  transport  on  demand  (Municipality  of  Sofia,  Bulgaria,  2020-

2023, mobility). 

3.  Vaasan  kadut  kuntoon  (‘Fix  the  Streets’)  (Municipality  of  Vaasa,  Finland, 

2020-2022, municipal public services). 

4.   Satellites  for  Wilderness  Inspection  and  Forest  Threat  Tracking  (SWIFTT) 

(Municipality of Riga, Latvia, 2022-2025, environmental protection). 

5.  Vadindekset (‘The wet index’) (Municipality of Jammerbugt, Denmark, 2021-

2023, public order and safety). 

6.  Policies  for  Holistic  Urban  Mobility  and  Accessibility  (Municipality  of 

Nicosia, Cyprus, 2021-2024, mobility). 

7.  The AI-based LisNav application dedicated to transforming the lives of the 
visually  impaired  (Municipality  of  Lisbon,  Portugal,  2022-2023,  social 
protection/mobility). 

8.  Immediate  and  Long-Term  Energy  Reduction  (RECITAL)  (Municipality  of 

Noisy-le-Grand, France, 2023-2025, energy). 

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Case II.1. De Virtuele Assistent (‘The Virtual Assistant’) (Belgium) 

Broel Towers and Broel Bridge in the City of Kortijk. 
Photo credit: Marc Ryckaert (MJJR). 

Initiator: City of Kortijk (BE). 

Project partners: City of Kortrijk, Leiedal. 

External suppliers: Cronos Public Services. 

Sector of intervention: AI-based services for citizens. 

Funding: EUR 538,488.68, out of which: 
80% from ‘Municipality without a Town Hall’ (EUR 426,660.46), 20% from the City of 
Kortrijk (EUR 111,828.22). 

Timing: 01/06/2022 - 31/05/2025. 

Expected impact: Implementation of the Virtual Assistant to official Kortrijk municipality 
websites; improving access to information, availability of services, search functionality, 
and integration across public services and administration functions (e.g., public transport 
information, government services information and booking, public amenities information 
and services). 

Description of the case study 

‘De  Virtuele  Assistent’  (The  Virtual  Assistant)  is  funded  as  part  of  wider 
digitalisation  plan  for  Flanders  Region  in  Belgium,  implemented  and  part-
funded by the City of Kortrijk. The strategic plan for AI was created in 2018, 
for a 6-year period11. The Flanders region, through the Digital Ministry of the 
Interior, allocated  EUR 33 million to the ‘Project Municipality without a Town 

11 https://www.flandersai.be/en/beleidsplan-artificiele-intelligentie.  

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Hall’ initiative, with the Virtual Assistant receiving  EUR 426,660.46 of this 
funding. The remaining 20% of funding for this particular project was allocated 
by the City of Kortrijk itself. The Flanders Region received the funding through 
the Flemish Government's recovery programme, entitled ‘Flemish Resilience’, 
which was financed by the EU through NextGenerationEU.  

Within the City of Kortrijk, a Digital Transformation team is responsible for the 
ideation,  management,  and  deployment  of  digitalisation  projects.  This  team 
works across administration departments internally and at an inter-municipality 
level.  The  Virtual  Assistant  will  be  rolled  out  across  municipalities  in  the 
Flanders  region  (and  beyond),  as  part  of  the  Grant  Agreement  requirements. 
Internally,  the  City  of  Kortrijk  also  works  with  a  number  of  complementary 
initiatives in the broad domain of digitalisation, with responsibilities delegated 
appropriately.  By  way  of  example,  the  ALL  DIGITAL  project  focuses  on 
building up citizen skills on digital topics, including the provision of ‘digihelp’ 
from  ‘digipoints’  (i.e.,  physical  information  points/desks  in  the  city).  For 
internal  employees,  sessions  are  provided through their internal  management 
system,  entitled  Copilot.  Some  of  these  are  focused  directly  on  AI  and  its 
deployment within public administration. Kortrijk also works alongside ‘The 
Digital Arts and Entertainment (HoWest)’ programme, recognised a number of 
times for best game education in the world, and the SHIFT-Project, which is 
focused  on  increasing  digital  maturity  within  staff  across  all  functions  at  an 
inter-municipal level.  

The Virtual Assistant has integrated aspects of inclusion into its roadmap, with 
functionality  to  accept  queries  in  multiple  languages.  The  project  team  also 
work directly on aspects of e-inclusion, contributing to an inclusive, equitable 
and  innovative  society.  Throughout the  design  and development  process,  the 
Digital Transformation team are committed to ensuring the 'e-inclusion reflex' 
is foregrounded, with focus on user panels, e-inclusion advisory groups and user 
research. 

Discussion of the results 

The  Virtual  Assistant  project  is  due  to  end  in  May  2025.  The  Digital 
Transformation team will monitor results, in conjunction with the funding body. 
The expected and achieved results are monitored on an ongoing basis, according 
to the project workplan, and reported on in a transparent manner - to aid trust 
and transparency in public spending12.   

12 https://www.vlaanderen.be/lokaal-bestuur/digitale-transformatie/gemeente-zonder-
gemeentehuis/goedgekeurde-projecten/de-virtuele-assistent.  

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The  project  has  a  distinct  focus  on  scalability  and  interoperability  – 
predominantly as it is expected to be rolled out to other Municipalities within 
the  Flanders  region.  The  focus  is  on  developing  a  modular  system,  that  will 
integrate with any Drupal content management system (used for City of Kortrijk 
website  management),  but  also  with  similar  Wordpress  content  management 
systems. Currently, a custom version of  ChatGPT is used for the system, but 
Microsoft services also allow implementation of open-source alternatives to be 
deployed, if required. If this was completed, the Virtual Assistant could reduce 
its  dependency  on  technology  providers.  While  the  Virtual  Assistant  will  be 
rolled out across a number of municipalities, it will be done so on a Software as 
a  Service  model  (SaaS),  with  licencing  maintained  by  the  external  supplier, 
Cronos Public Services. There are no plans to open source the code developed 
for the tool.  

Currently, the Virtual Assistant is able to receive queries in multiple languages, 
with translation completed by the integrated language model. However, national 
legislation obliges the Assistant respond in the national language – which in this 
instance  is  Flemish.  This  hinders  the  inclusivity  of  the  service,  and  poses  a 
barrier  to  wider  adoption,  effectiveness,  and  usefulness  –  especially  in  the 
increasingly  multi-cultural  society.  The  Digital  Transformation  team  have 
requested an exemption from the applicable national law, but it was refused. 
Great  harmonisation  of  relevant  laws  that  affect  digitalisation,  or  greater 
tolerance  on  application  of  laws  would  provide  additional  support  for  this 
project.  

As the project is still in the design and development phase, there has been no 
wide user-testing. However, this is planned within the timeline of the project. 
User-testing will validate the Virtual Assistant before it is rolled out to multiple 
municipalities.  Currently,  design  and  development  testing  is  completed 
internally  by  the  Digital  Transformation  team  in  conjunction  with  the 
technology provider, Cronos Public Services. 

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Case II.2. Green public transport on demand (Bulgaria) 

Rear View of The Alexander Nevsky Cathedral in Sofia, Bulgaria. 
Photo credit: Sergio Formoso (gettyimages). 

Initiator: Municipality of Sofia (BG). 

Project  partners:  Municipality  of  Sofia,  Sofia  University,  Plovdiv  University,  Sofia 
Development  Association,  Sofia  Urban  Mobility  Centre,  The  National  Association  of 
Municipalities in the Republic of Bulgaria, Modeshift Europe Ltd., The National Institute of 
Meteorology and Hydrology. 

External suppliers: n/a. 

Sector of intervention: Mobility. 

Funding: EUR 3,712,553.00 from the European Regional Development Fund, through the 
Urban Innovative Actions initiative, now known as the European Urban Initiative. 

Timing: 01/07/2020 – 30/06/2023. 

Expected  impact:  The  project  deploys  a  novel  public  transport  solution  in  the  Sofia 
neighbourhoods  of  ‘Manastirski  Livadi’  and  ‘Baxton’.  The  solutions  provide  citizens  an 
alternative  to  car  travel,  with  the  goal  of  reducing  car  traffic,  improving  air  quality,  and 
increasing  mobilisation.  The  project  targets  key  segments  of  the  population  to  promote 
cultural and behavioural change towards greener and more environmentally friendly modes 
of transport. The solution is entitled ‘On-demand green public transportation’, and is formed 
through a combination of machine learning and advanced analytic capabilities for optimal 
route calculation for a mini-fleet of electric busses. 

Description of the case study 

The INNOAIR project was funded through the European Regional Development 
fund,  through  a  Urban  Innovative  Actions  initiative.  The  project  has  multiple 
objectives, predominantly to improve mobility in the city of Sofia through a series 
of  actions  to  design,  develop,  and  deploy  greener  forms  of  public  transport  to 
residents, improving health and reducing air pollution. The project received EUR 

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3.7million from the funding body. The consortium comprises eight organisations: 
a  mix  of  public  authorities,  non-government  agencies,  universities,  and  a 
technology provider.  

The project is coordinated by the Municipality of Sofia (INNOAIR, 2023a), with 
project management duties delegated to a distinct organisation, entitled the Sofia 
Development  Association  (SDA)  (SDA,  2024).  The  SDA  is  ‘responsible  for 
testing  innovations  in  the  field  of  culture  and  digital  transformation,  research, 
analysis  and  educational  programmes  that  create  conditions  for  permanent 
dialogue  between  civil  society,  creative  communities,  business,  academic 
institutions  and  the  Sofia  Municipality’  (SDA,  2024).  The  Association  was 
established  in 2010  and  is  registered  as  an  independent  entity with  a  ‘Not-for-
Profit’  legal  status.  The  SDA  has  a  number  of  overarching  objectives:  to 
popularise  and  assist  in  urban  development  policies;  to  aid  sustainable 
development;  to  implement  innovative  development  actions;  and  to  foster 
coherent relationships between stakeholders within the wider Sofia region for the 
benefit of citizens and residents in the city.  

The  INNOAIR  project  has  a  work  plan,  associated  milestones  and  timeline 
detailed in the funding agreement. The project deployment is monitored within 
the  consortium through a reporting process. The project itself has articulated a 
number of positive outcomes, including the deployment of five electric buses, six 
charging  stations,  and  an  application  that  rewards  active  resident  transport.  In 
terms  of  green  objectives,  the  project  has  resulted  in  a  number  of  emissions 
reductions in the environment (21,645 tons of particle matter, 92 tons of NO2, and 
4,710 tons of CO2) (INNOAIR, 2023a). 

The  on-demand  electric  buses  are  the  flagship  of  the  project  and  have  been 
successfully  deployed  in  a  test  region  (‘Manastirski  livadi’),  with  work  being 
conducted  to  scale  up  the  service  into  three  other  suburbs  (‘Malinova  dolina’, 
‘Vitosha’  and  ‘Gorna  Banya’).  The  buses  couple  big  data  and  AI  to  replace 
traditional  bus  routes,  and  timetables,  with  flexible  routes  and  on-demand 
services.  Routes  are  decided  by  a  mobile  application,  developed  within  the 
project, as part of the On-demand Public Transportation Platform (ODPTP). The 
platform  provides  the  ability  to  request  e-buses  and  create  optimised  routes 
depending on real-time data such as citizen demand, traffic, predefined service 
zones,  buses  working  time  and  shifts  (considering  regulatory  driver  breaks), 
roadblocks, etc. 

The project provides for vulnerable groups, including the elderly and those not 
less  digitally  native.  The  bus  routes  are  integrated  with  physical  stops,  and 
‘request  centres’  –  where  someone  without  the  ODPTP  application  can  use  a 
publicly accessible digital terminal to request a bus to come and get them, or hop 

119 

 
 
 
 
 
on a bus as it passes. These request centres are placed in strategic places within 
the area (near public services, shopping areas, recreation areas, etc.). The hybrid 
nature of the system improves accessibility and inclusion, while altering residents’ 
perceptions  of  what  a  public  transport  system  might  look  like.  The  service  is 
continuing  to  develop,  with  a  number  of  features  planned  to  be  integrated 
including payment modules, and improved ticketing schemes. 

Discussion of the results 

The  INNOAIR  project  has  consistently  worked  with  citizens  and  citizen 
groups/representatives to understand needs and foster dialogue. This is an explicit 
way of working for the SDA and has been integrated into the way in which the 
ODPTP  has  been  developed.  The  user  testing  and  user  feedback  will  continue 
throughout the development cycle and into the scaling up phase, to ensure that the 
service continues to meet resident needs.  

Key  concerns  for  the  project  has  been  data  governance,  data  protection 
compliance, and information security. This is found to be a common issue in many 
data-driven  research  and  innovation  actions  for  the  SDA.  Many  government 
departments  of  Sofia,  it  was  communicated,  do  not  have  the  experience  or 
expertise in-house to obtain value from the data they have access to, coupled with 
the fact data is not commonly shared or processed for analytics purposes by inter-
dependent (or related) governmental departments.  

In  the  INNOAIR  project,  datasets  were  utilised  from  transport,  planning,  and 
housing departments, taking time, patience, and effort to ensure compliance and 
security obligations. Most of this was overseen by the relevant departments, with 
support from the technology provider and the research centres (i.e., universities), 
who had more experience with data analytics functions. Cybersecurity and data 
protection  concerns  were  consistently  foregrounded,  and  often  led  to  complex 
discussions around ownership, control, liability, risk, and potential (and deployed) 
mitigation measures. The EU AI Act was also a concern for the INNOAIR project, 
but compliance was led by the technology provider - who had better insight into 
specific obligations that were (and would be) required as the  AI Act came into 
force. This compliance effort was also supported by the relevant sub-departments 
in the governmental organisations.  

The  ODPTP  was  always  focused  on  rolling  out  to  a  number of  suburbs  in  the 
greater Sofia area – so the project’s goal from ideation stage was scalability. The 
suitability  of  areas  depended  on  access  to  the  required  data  (to  inform  the 
algorithms for route optimisation), but also on the demographics of the area, and 
the  available  infrastructure.  The  project  itself  is  interested  also  in  sharing 
learnings outside of Sofia, potentially across Bulgaria, but also wider throughout 

120 

 
 
 
 
 
 
other European Member States. They see the success of the trials in Sofia and 
understand the potential for wider rollout across Europe, considering the learnings 
made from the project.  

There were communicated externalities that caused concerns for the project, such 
as  the  COVID-19  pandemic  and  the  outbreak  of  war  in  Ukraine.  These  were 
disruptive, but did not hinder the successful uptake of the system. The ODPTP 
was supported by a number of complementary objectives, deployed into the Sofia 
region, including Bulgaria’s first low-emission zone. The zone had some issues 
with enforcement but has opened the conversation to changing resident behaviour 
in the city and its suburbs.   

The ODPTP has not been without some challenges, including aspects related to 
the ticketing system. Local regulations govern the way in which ticketing can be 
offered to the public (specific ticketing systems, protocols, prices, etc.), and this 
has hindered how ticketing could work in the on-demand service. However, this 
is  viewed  as  a  surmountable  challenge,  with  work  being  completed  at  the 
municipality level to ensure the ODPTP can be implemented successfully.   

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Case II.3. Vaasan kadut kuntoon (‘Fix the Streets’) (Finland) 

Vaasa Church, Finland. 
Photo credit: Harry P. 

Initiator: Municipal Engineering (Vaasa City) (FI). 

Project partners: n/a. 

External suppliers: Crowdchupa (now known as Crowdsorsa). 

Sector of intervention: Municipal public services. 

Funding: EUR 39,150.00. 

Timing: 01/11/2020 - 31/08/2022. 

Expected impact:  Through the deployed app,  developed by Crowdchupa (now known as 
Crowdsorsa), the Vaasa City will reward citizens with money for sourcing of information on 
the health of the streets and roads across the city. The information will be used to inform the 
Municipal  Engineering  department’s  (responsible  for  road  maintenance)  infrastructure 
database,  which  is  then  used  to  direct  resources  into  the  road  maintenance  programme. 
Through the application, streets are sprinkled with coins and other objects of monetary value 
which users collect by recording a video image. Data collectors are paid a fee of around two 
euros per kilometre, usually within the same day. The collected video data, which is checked 
by  AI,  is  used  in  the  planning  of  street  network  repairs.  The  method  enables  mass  data 
collection for any road user with a smartphone (Vaasa, 2024a). 

Description of the case study 

‘Vaasan kadut kuntoon’ (‘Fix the Streets’) is funded through the Vaasa City, as 
part of the ‘Innovation activity Ecosystem Agreement 2021-2027’ between Vaasa 
city region and the Government of Finland. This is managed through the Ministry 

122 

 
 
 
 
 
 
 
 
 
 
 
 
 
of Economic Affairs and Employment (Vaasa, 2024b). The ecosystem agreements 
harness the region’s ability to develop and deploy digital technologies and carbon 
neutral  activities,  helping  them  to  meet  Twin  Transition  goals.  The  ecosystem 
agreement promotes the development of an innovation ecosystem in the region 
and closer cooperation with international research, development, and innovation 
(RDI)  networks.  The  ecosystem  is  built  on  a  cooperation  model,  including 
financiers,  and  other  RDI 
companies,  universities,  research 
stakeholders, to increase research and development activities in the market. The 
core goal is to improve resident well-being (Vaasa, 2024c).  

institutes, 

In Finland, it is the responsibility of the  State and the municipalities to ensure 
proper  regional  development.  Vaasa  City  participates  in  national  urban  policy 
networks, the most important one of which is the Urban Policy Committee. The 
committee’s main task is to implement urban policy decisions. Vaasa City also 
participates in the Association of Finnish Municipalities’ Urban Policy Working 
Group.  The  Working  Group’s  tasks  include  highlighting  specific  issues 
concerning the largest cities and acts as an arena for exchanging ideas13. The ‘Fix 
the Streets’ initiative is a pilot action, concerned with improving the way in which 
the city manages and maintains its road network.  

Vaasa City has a development budget every year, which allows it to experiment 
with projects, research, and pilot actions. This budget is allocated every year and 
is  provided  as  part  of  the  regional  and  urban  development  policy  and  wider 
Finnish commitment to digitalisation and research and innovation. The ‘Fix the 
Streets’ initiative was allocated EUR 37,150.00 from this budget. 

The  initiative  is  managed  by  the  Municipal  Engineering  department  of  Vaasa 
City’s public administration. It was deployed with support from a private sector 
technology  provider,  Crowdsorsa  (formally  Crowdchupa).  Crowdsorsa  is  a 
company  based  in  Tampere,  Finland.  They  have  developed  technologies  for 
public administrations to delegate various tasks to citizens, paying them for their 
time and effort. The technology itself has a number of functional use cases, from 
road health mapping and analysis (e.g., Fix the Streets) to mapping of invasive 
species and accessibility aspects and concerns within cities. At a very high level, 
the  tool  is  a  mobile  phone-based  application  that  allows  residents  to  provide 
(typically) geo-located information to the public authority, including text, photos, 
and video (as required). This then allows the public authority to better understand 
their city infrastructure and to make decisions based on the improved monitoring 
data set. 

13 https://www.vaasa.fi/en/regional-development-and-urban-policy/  

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The ‘Fix the Streets’ pilot initiative was implemented over a two-year time frame 
(including  planning,  design,  and  deployment)  to  help  the  Vaasa  City’s  road 
infrastructure department build a multi-format knowledge database (text, image, 
video) of the city’s streets and roads – especially in the context of their current 
health  (potholes,  road  cracks,  incorrect/damaged  signage,  incorrect/damaged 
curbing, road markings, etc.). The application ingests user collected data, and then 
conducts  AI-leveraged  analysis  to  determine  specific  features  of  the  road,  or 
street, as well as identify damage, cracks, potholes, opaque road markings, etc.  

The application complements a digital signage system in the city, where every 
street  or  road  sign  had  been  assigned  a  unique  and  persistent  identifier.  This 
identifier  system  was  integrated  to  provide  geo-location  data  for  the  media 
uploaded  by  residents.  This  was  chosen  over  GPS  systems  (or  similar) 
predominantly for data minimisation purposes, but also because the digital sign 
system was a notable feature of the city’s infrastructure, so it was of benefit to 
utilise it.  

The crowdsourcing initiative allows Vaasa’s Municipal Engineering department 
to implement a more reliable road maintenance schedule, plan, and approximate 
costings.  It also  allows  them to  better understand  areas of the  city that  require 
more attention, or further analysis by a ‘field crew’ team. The data they gather 
also  supplements  a  separate,  internal,  road  management  and  maintenance 
application. This application allows the public authority to understand whether 
any road or street requires full re-paving (as opposed to fixing the potholes only), 
and what steps are required to repave the road considering other factors such as 
water pipe depth, condition, age, etc. The algorithmic component of this internal 
system  analyses  aspects  of  the  media  uploaded  by  the  residents  and  maps  the 
information to the City’s database. It includes analysis such as depth, width, and 
length of potholes, lengths of cracks across roads, and aspects related to poorly 
marked road markings, damaged signs, overall asphalt quality, current drainage 
capabilities, etc.  

The  ‘Fix  the  Streets’  application,  as  developed  by  Crowdsorsa,  has  been 
implemented  in  a  number  of  regions  throughout  Finland  and  Vaasa  City  has 
shared their own implementation experience with other public authorities across 
the country. Vaasa City believe the project was a success, with informal feedback 
received from residents coupled with positive media coverage of the initiative. 
They intend to  repeat  this  crowdsourcing drive  every  three  or  four  years,  as it 
allows them to gather a complete update of their database over a short time period. 
This update will then inform the next years of road infrastructure maintenance and 
repair. They have indicated that the reliability of the data they now have has been 
greatly  improved,  and  has  provided  a  much  better  evidential  base  for  road 
maintenance planning, improvements, costing, and scheduling.  

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Discussion of the results 

The  ‘Fix  the  Streets’  initiative  is  deemed  a  success  by  Vaasa  City  and  was 
described by the representative from Vaasa’s Municipal Engineering department 
as  a  considerable  boost  to  the  manner  in  which  the  department  gathered 
information about the city’s roads and streets. It also allows the  department to 
merge two road maintenance systems, as they were able to take the data gathered 
through the  Crowdsorsa  application  and  use it to  inform  their primary  IT  road 
engineering tool. 

In terms of accessibility and inclusivity, Vaasa’s Municipal Engineering deployed 
the application alongside normal information gathering methods. This meant that 
users were able (if they desired) to continue to use email or telephone as methods 
of communication to log incidents concerning road health in the city. These logs 
would then be investigated. This method was not as automated and did not provide 
the rich multi-modal (text, image, video, and associated analysis) data sets that 
the application did, but it allowed residents to be involved in the process. These 
alternative methods of communication still exist.  

The Crowdsorsa application has been developed for many other use cases. There 
have  been extensive testing  periods where  end-user feedback has been sought, 
received, and integrated into iterative versions of the software. This feedback was 
not sought directly for the ‘Fix the Streets’ campaign. 

The  Crowdsorsa  application  is  owned  by  the  Crowdsorsa  company,  and  it  is 
licenced on a SaaS basis. This allows the system to be implemented in a wide 
array of use cases across Finland (and more broadly, if required). This scalability 
is welcomed, as it provides the basis for a cohesive business relationship between 
public  authorities  and  technology  providers.  However,  the  licence  model  also 
creates strong dependency on the service provider, and limits how other public 
authorities might integrate the application with their own systems. It is possible 
that integration costs would be prohibitive for some public authorities, given the 
scale and complexity of changes required from the application. As it stands, there 
are no elements of the application that are open-sourced or provided on permissive 
licences.  

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Case  II.4.  Satellites  for  Wilderness  Inspection  and  Forest  Threat 
Tracking (SWIFTT) (Latvia) 

Sigulda, Latvia. 
Photo credit: mharris97. 

Initiator: AXA Climate (Project Coordinator) (LV). 

Project partners: SIA “Rīgas meži, Wildsense, Space Research Institute of the National 
Academy of Sciences of Ukraine, University of Bari Aldo Moro (Department of Computer 
Science), Leibniz Universität Hannover (TNT), Timbtrack, Fürstliches Forstamt (Donatus 
Prinz zu Schaumburg-Lippe), Groupe Coopération Forestière, Da Vinci Labs. 

External suppliers: n/a. 

Sector of intervention: Environmental protection. 

Funding:  EUR  3,680,231.25  from  the  EC  Horizon  Europe  programme.  The  budget 
allocated to SIA Rigas Mezi is of EUR 201,225.00, of which EUR 140,857.50 is funded 
through the SWIFFT project (SWIFFT, 2024), and EUR 60,367.50 is funded internally. 

Timing: 01/11/2022 – 31/10/2025. 

Expected  impact:  Forests  are  essential  to  life  on  Earth.  They  provide  habitats  for 
thousands of creatures and combat climate change through carbon sequestration. However, 
our  forests  are  threatened  by  insect  outbreaks,  fires,  windthrow  and  droughts.  Notably, 
insect outbreaks are one of the leading causes of forest loss globally, destroying 85 million 
hectares (ha) of forest worth EUR 15 billion annually. SWIFTT will provide a scientifically 
sound and technically feasible way to help monitor and manage forest risks: windthrow, 
insect outbreaks, and forest fires by enabling forest managers with affordable, simple and 
effective  remote  sensing  tools  backed  up  by  powerful  machine  learning  models.  The 
solution will offer a monthly health monitoring service using Copernicus satellite imagery 
to detect and map the various risks to which forests are exposed. Early threat detection aids 
timely intervention. The solution anticipates monitoring and protecting up to 40 million 

126 

 
 
 
 
 
 
 
 
 
 
hectares of global forests by 2030, saving foresters over EUR 468 million in monitoring 
costs, and creating over 50 direct jobs (SWIFFT, 2024). 

Description of the case study 

The  SWIFTT  project  is  funded  through  the  Horizon  Europe  programme 
(HORIZON.2.4.10 - Space, including Earth Observation) (EC, 2024a) under the 
topic  entitled:  HORIZON-EUSPA-2021-SPACE-02-51 
-  EGNSS  and 
Copernicus  applications  fostering  the  European  Green  Deal.  The  topic  has  a 
number of overarching objectives and themes, with the SWIFTT project focusing 
on the sub-topic of ‘Preserving and restoring ecosystems and biodiversity’ (EC, 
2024b). The project is comprised of a consortium of nine organisations, which 
are a mix of technology providers, research institutes, and regional authorities, 
including SIA Rīgas meži (Rīgas meži, 2024a), from Latvia. SIA Rīgas meži is 
the government agency responsible for management of forestry and parks (green 
infrastructure) within the regions of Riga and Pieriga in Latvia.   

SIA Rīgas meži is a municipal company founded in 2008. It is responsible for 
approximately  63  thousand  hectares  (ha)  of  forest,  453  ha  of  Riga's  gardens, 
parks and greenery, and for 367 ha of cultural and recreational parks, also known 
as ‘Mežaparks’. It also has responsibility for sustainable development, biological 
diversity and accessibility of these territories to the public (Rīgas meži, 2024b). 
They  are  a  private,  for-profit  entity,  with  direct  links  to  the  municipality,  the 
public,  and  stakeholders  in  the  region.  They  are  committed  to  governance, 
transparency,  and  excellence  in  efforts  towards  sustainability  (Rīgas  meži, 
2024c). 

SIA Rīgas meži is involved in a number of research and innovation projects, both 
nationally and at European level. The main objectives of their projects are: to use 
satellite data and AI in the monitoring of forest areas; biodiversity conservation 
and climate change mitigation; to use IT in improving forest care; and to improve 
the  quality  and  protection  of  tree  seedlings  (Rīgas  meži,  2024d).  Project 
monitoring is handled according to internal processes, as well as at the project 
level through monitoring to funding bodies. There is also annual reporting to the 
Riga Municipality regarding involvement in research projects, implementation 
phases, challenges, as well as budgets, results, and impacts.  

SIA  Rīgas  meži  have  a  number  of  ongoing  R&I  projects,  and  work  with 
integrating technology on a daily basis. The organisation place digital literacy 
high  on  their  agenda  and  have  been  skilling  their  ‘field  crew’  on  related 
technology such as field tablets, monitoring programmes, and drone monitoring 
technologies  for  a  number  of  years.  The  company  also  value  working  with 
technology  providers  at  a  local,  national,  and  European  level  to  improve  the 

127 

 
 
 
 
 
 
 
efficiency  and  effectives  of  their  forestry  and  green  area  management.  This 
includes  integration  with  Latvian  smart  city  solutions  and  overarching 
digitalisation  services,  based  on  use  of  the  Copernicus  satellite  infrastructure. 
Currently there are no specific AI skilling initiatives.   

As an end-user in the SWIFTT project, SIA Rīgas meži were involved at an early 
stage  in  the  development  of  the  research  plan  –  shaping  the  overview  of  the 
proposed  technology  to  their  distinct  needs  and  requirements  for  forestry 
management. Now the project is underway, it is managed internally by a project 
deployment team with a distinct research lead, supported by a number of staff in 
administration roles, as well as ‘field crew’, who are involved in the management 
and monitoring process of the green areas. The project is about halfway through 
its projected timeline, with the first iterations of the technology being provided 
to  end-users  within  the  project.  They  are  not  yet  at  an  operational  level,  but 
continued refinements are being integrated into new versions.     

Discussion of the results 

SIA Rīgas meži have been able to support the development of user requirements 
for  the  tool  –  to  ensure  that  the  end-product  meets  their  distinct  needs.  This 
process  was  shared  with  other  end-users  in  the  project,  drawn  from  across 
Europe. SIA Rīgas meži found this process challenging, as distinct needs from 
various  regions  across  Europe  were  being  accommodated  by  the  technical 
developers. The accommodation of varied requirements, however, should lead 
to greater scalability and interoperability of the tool. 

The  tool  being  developed  is  for  forestry  management;  it  will  support  the 
gathering  of  data,  field  analysis,  and  monitoring  of  tree  health.  There  are, 
however, aspects of European regulatory requirements (including the European 
deforestation regulation14) which are not being built into the tool. These legally 
binding requirements stem from the European Communication on ‘Stepping up 
EU action to protect and restore the world’s forests’ (EC, 2019). Whether or not 
the tool can incorporate some of this functionality is still unknown. 

As mentioned, the tool is still in early design iterations, so as it stands there has 
been  little  work  on  integrating  specific  inclusivity  and  accessibility  features. 
Efforts  have  been  taken  by  the  consortium  to  ensure  that  ‘field  crew’  are 
provided with the knowledge and skills to use the software. From a compliance 
perspective, the project has not yet focused on EU AI Act compliance but given 
the perceived low-risk of the tool, it is not seen as a major obstacle.  

14 For more information, please see: https://environment.ec.europa.eu/topics/forests/deforestation_en.  

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One of the stated primary objectives of the funding topic was that technologies 
should have clearly defined commercial potential and paths to market – including 
a clear valorisation strategy and identified market need (EC, 2024b). The SWIFTT 
software is intended to be offered on a licence basis to end-users, post project, as 
a  SaaS  offering.  SIA  Rīgas  meži  have  acknowledged  this  as  being  a  potential 
hurdle to adoption, as the licence model must consider relevant economic factors 
across Member States, and relevant budgets that forestry agencies may have. The 
SaaS  model  may  hinder  pan-European  uptake,  and  in  turn  hinder  a  number  of 
interoperability goals with regards to open data markets for forestry and green 
space management. As it stands, there have been no communicated plans to make 
components of the software open-source, or open-access.  

In  separate  projects  and  initiatives,  SIA  Rīgas  meži  are  also  working  with  a 
number  of  related  tools  that  incorporate  algorithmic  or  AI  components.  This 
includes tools for monitoring sapling health within their tree nurseries, and tools 
for monitoring social media for the gathering of input into urban tree health and 
park maintenance and management. These technologies have been welcomed by 
the organisation as they can improve delivery of its responsibilities to the public, 
and  other  stakeholders.  The  organisation  also  mentioned  how  the  public 
procurement  process  is  changing  –  with  AI  components  being  requested  and 
offered  more  frequently.  This  has  demanded  a  corresponding  increase  in  AI 
fluency amongst both procurement teams, and tenderers. 

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Case II.5. Vadindekset (‘The wet index’) project (Denmark) 

Municipality of Jammerbugt coastline. 
Photo credit: C. TORP. 

Initiator: Jammerbugt Municipality (DK). 

Project partners: Jammerbugt Municipality, DTU Sustain, the Alexandra Institute, 
KMD. 

External suppliers: n/a.  

Sector of intervention: Public order and safety. 

Funding: DKK 5.0 million  from  the investment  fund  for new technologies (Danish 
Agency for Digitalisation). 

Timing: 2021-2023. 

Expected impact: The ‘Vadindekset’ project, developed by the Jammerbugt Municipality 
in Denmark, represents an innovative application of AI to tackle the growing challenges 
posed by climate change, particularly in terms of managing and forecasting floods, through 
the  implementation  of  a  ‘wet  index’.  The  primary  impact  of  this  tool  is  its  potential  to 
significantly improve flood forecasting. The ability to foresee and proactively respond to 
flooding events is expected to (1) reduce flood damage by enabling local authorities to take 
early action; (2) enhance public safety by providing timely warnings to residents, allowing 
them  to  take  necessary  precautions;  (3)  increase  emergency  preparedness  by  informing 
local government response teams about flood risks well in advance, reducing the impact 
on public services. Although the tool is still in its development phase, the expected impact 
of the project is significant. It could serve as a model for other municipalities facing similar 
challenges, contributing to more resilient communities and better management of public 
resources against natural hazards due to climate change. 

130 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Description of the case study 

In 2023, Denmark has been hit by precipitation far above the national average 
of  the  last  ten  years,  and  future  climate  in  the  country  is  expected  to  be 
significantly wetter. The dangers of flooding are greater in coastal areas. Indeed, 
intense rainfall caused damage to the community of Jammerbugt and increased 
the municipality's emergency preparedness and management costs. Within the 
‘Vadindekset’ project, the Jammerbugt Municipality designed an early warning 
tool based on AI in response to the consequences for citizens and landowners 
of the massive rainfall that affect the area. Hence the usefulness of a tool that 
through AI, supports flood forecasting and contributes to decision-making in 
monitoring and local planning for urban and rural areas. The development of 
the tool, the so-called ‘wet index’, was facilitated by the large availability of 
geo and climate data and the solution is particularly helpful in a coastal city, 
prone  to  intense  rainfall  and  with  high  level  of  groundwater.  The  project 
development was thus facilitated by the abundance of free data which increases 
the scalability of the project. 

The  initiative  is  part  of  the  national  effort  from  the  Danish  Agency  for 
Digitalisation which works on the testing and development of new applications 
based  on  AI.  In  more  detail,  the  ‘Vadindekset’  project  is  one  of  the 
‘Signaturprojekter’ (‘Signature projects’) that benefitted from the investment 
fund set up by the Danish government, the Local Government Denmark (KL) 
and  Danish  regions.  The  investment  fund  was  arranged  in  occasion  of  the 
agreement  on  the  municipalities’  and  regions’  finances  for  2020  and  has 
supported a total of 40 ‘signature projects’ between 2020 and 2022 that explored 
the use of AI in the public sector. 

The government, KL and Danish Regions selected the projects to be financed 
based  on  their  concrete  use  of  AI  in  the  areas  of  welfare,  climate  and 
administration in support of the public sector. In this process, priority was given 
to  initiatives  that  delve  into  yet  unexplored  uses  of  AI  that  can  improve  the 
quality of public service by scaling the technology. 

The  Jammerbugt  Municipality  is  located  in  the  North  Jutland  region  of 
Denmark, an area that is not fully digitally mature and makes this case stand out 
even more. Despite the absence of specific AI skills, the local government relies 
on the IT unit as dedicated office for digitalisation, and one of the key success 
factors of this initiative is the creation of a balanced partnership. Indeed, in the 
project design and development processes, the municipality collaborated with 
private  suppliers  (KMD)  and  research  institutes  (DTU  Sustain  and  the 
Alexandra Institute) and relied on the expertise of the Technical University of 

131 

 
  
 
 
 
 
Denmark. Regarding the technical specification, the model makes use of AI and 
historical  data  (e.g.,  satellite  images,  rainfall  data  and  data  on  terrestrial 
groundwater) to train an algorithm to predict potential floods in the municipality 
48 hours in advance. To increase the precision of the forecast, the researchers 
integrated  an  understanding  of  the  movement,  distribution  and  interaction  of 
water with its surroundings. The project workplan included a development and 
a testing period.  

Discussion of the results 

In  line  with  the  priorities  followed  in  the  investment  fund  allocation,  the 
‘Vadindekset’ project devoted particular attention to its scalability, especially 
through the selection of data that are available also at national scale, such as 
those from the hydrological information and forecasting system (HIP). Indeed, 
in case of successful implementation of the project, it is expected to be extended 
nationwide.  

Despite to date the initiative is suspended due to lack of funds, at the current 
stage of development, the model demonstrates good forecasting ability, but it 
still needs to be improved and matured to achieve greater accuracy. 

The ‘wet index’ was tested by the Jammerbugt Municipality in 2023. The results 
demonstrated  that  forecasts  are  better  than  expected  for  the  wet  months  of 
spring. However, the accuracy of the forecasts is also assessed in the ability to 
prevent  ‘false  alarms’.  In  this  regard,  the  instrument  proved  to  perform 
incorrectly during the summer period, and the reason was found to be that the 
instrument had been trained with insufficient data for the summer months, thus 
demonstrating a limit and at the same time an area for improvement.  

The  aspect  of  reliability of  forecasts  is  particularly  important  with  a  view  to 
acceptance  by  the  citizens,  which  will  then  be  amongst  the  users  and  final 
beneficiaries of the tool. As proof of this, a small group of citizens was selected 
in the test phase who could check the functioning of the system on a regular 
basis. The ambition of the project designers is to make the ‘wet index’ available 
on the website of the Jammerbugt Municipality as an overview map that allows 
inhabitants to monitor and be notified of flood risk. 

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Case  II.6.  Policies  for  Holistic  Urban  Mobility  and  Accessibility 
(Cyprus)  

Eleftheria Square, Nicosia (Cyprus). 
Photo credit: Athina Vrikki. 

Initiator: Nicosia Municipality (CY). 

Project  partners:  Nicosia  Municipality,  GFT  Italia,  EGI  Foundation,  Netcompany-
Intasoft,  Novoville,  Unparallel  Innovation  LDA,  ViLabs  LTD,  Arthur’s  Legal  BV, 
Universidad Politecnica de Madrid, City of Athens IT Company, City of Genoa, Lisboa E-
Nova, Reportbrain, Burgas Municipality, EKSO SRL, Tecnè Italia. 

External suppliers: CYENS Centre of Excellence.  

Sector of intervention: Mobility. 

Funding: EC funding through Horizon 2020: EUR 3,999,988.25. 

Timing: 1/03/2021- 30/04/2024. 

Expected impact: Within the AI4PublicPolicy EU funded project, the expected impact of 
the  Pilot  ‘Policies  for  Holistic  Urban  Mobility  and  Accessibility’  led  by  the  Nicosia 
Municipality  covers  three  main  areas:  (1)  Urban  transformation:  the  initiative  aims  to 
support  the  creation  of  a  more  equitable,  accessible  and  liveable  city  for  all  population 
segments;  (2)  Foster  inclusivity  and  community  empowerment:  the  Pilot  intends  to 
promote social inclusion and equity, also fostering community cohesion; (3) Community 
empowerment:  the  involvement  of  stakeholders  is  instrumental  to  align  policies  to  the 
needs  of  the  community,  thus  empowering  citizens  to  participate  in  the  policy-making 
process. 

Description of the case study 

The initiative ‘Policies for Holistic Urban Mobility and Accessibility’ led by 
Nicosia  Municipality  is  part  of  the  broader  project  ‘AI4PublicPolicy’,  which 

133 

 
 
 
 
 
 
 
 
 
 
 
 
 
that 

the  contribution  of  AI, 

exploits technological advancement to support a field in which it is less common 
to  see 
is  policy  development.  The 
‘AI4PublicPolicy’project is funded by the EU Horizon 2020 programme and 
represents  a  collaborative  initiative  between  policymakers  and  Cloud/AI 
specialists aimed at unlocking the potential of AI for the development of public 
policies. Objective of the initiative is to develop and promote an open cloud 
platform (the AI4PublicPolicy Platform) that uses AI technologies for policy 
management.   

The ‘AI4PublicPolicy’ project includes the launch of five pilots led by public 
authorities,  the  third  of  which  is  the  Municipality  of  Nicosia.  As  per  the 
objective of  the entire  initiative, the case of Nicosia Municipality is oriented 
towards the development of a citizen-centric solution and its specific aim is to 
develop  evidence-driven  policies  that  enhance  citizens'  mobility  and  reduce 
travel time. In more detail, the project aims to support inclusive mobility and 
using comprehensive traffic and public transport data is targeted to the elderly 
and individuals with disabilities. 

Within the broader goal to improve urban mobility, the Pilot specifically focuses 
on  two  policies  aiming  at  (1)  improving  the  accessibility  for  people  with 
disabilities  and  at  (2)  optimising  parking  accessibility  for  individuals  with 
disabilities. The development of the first policy implies the combined use of AI 
algorithms to analyse traffic data, the whitelist of the Smart City platform and 
License  Plate  Recognition  (LPR)  cameras  to  improve  access  for  individuals 
with disabilities to streets with limited  vehicle entry during periods of traffic 
congestion.  The  aim  is  to  address  the  unique  challenges  in  navigating  high-
traffic areas by providing an accessible and effective solution to these barriers. 
The  second  pilot  policy,  ‘Smart  parking  management  for  people  with 
disabilities’, harnesses the predictive power of AI and in particular to forecast 
congested  areas  and  enhance  parking  accessibility  in  specific  support  of 
individuals with disabilities.  

In the development of the Pilot, the composition of the partnership was crucial, 
which  saw  the  collaboration  of  local  universities  and  international  institutes. 
Particularly decisive was the involvement of the CYENS Centre of Excellence, 
which  is  active  in  cutting-edge  research  and  the  cooperative  development  of 
innovation and high-impact projects. 

Discussion of the results 

The aim of the pilot ‘Policies for Holistic Urban Mobility and Accessibility’ of 
Nicosia Municipality is to contribute to the development of evidence-based and 
data-driven policies on inclusive mobility. The implementation of the policies 

134 

 
 
 
 
 
 
is planned at the conclusion of the Pilot and is not yet finalised; however, the 
results of the initiative are considered to be positive to date.  

Analysing the success factors and criticalities of the Nicosia Municipality case, 
what emerges clearly is the crucial importance of an ecosystem external to the 
local public administration that is collaborative and innovation oriented. In the 
development  of  the  technological  solution,  external  cooperation  was  in  fact 
fundamental for the procurement of the specific technical competencies absent 
within 
the  purposes  of  successful 
implementation, it was useful to investigate and monitor the level of acceptance 
amongst target groups through the involvement of organisations working with 
people with disabilities.  

the  municipality.  Likewise, 

for 

A decisive factor that enabled the feasibility of the Pilot was the prior presence 
of a smart city strategy. On the one hand, this strategy and its alignment with 
the work of the CYENS Centre of Excellence encouraged the actual initiation 
of the case, and, on the other hand, it resulted in the availability of tools used 
for traffic monitoring and management such as the city sensors to monitor traffic 
congestion and the Smart City platform providing the list of individuals with 
disabilities.  

On the opposite side, a challenge that the pilot case faced was the delay in the 
availability of useful data, due to the actions being launched at a premature stage 
compared to the implementation of the smart city strategy. Furthermore, while 
the collaboration with stakeholders and the partnership with the CYENS Centre 
of  Excellence  were  particularly  positive,  it  was  challenging  to  obtain  the 
approval  of  the  project  idea  and  implementation  modality  within  the 
organisation and  from  citizenship. This underlines  the  importance of  gaining 
trust and consensus on the introduction of AI based innovation not only from 
the  direct  beneficiaries  but  from  the  general  public  that  can  influence  the 
perception and actual success of the initiative. 

Although the pilot has not yet reached the policy implementation phase and at 
the present stage the results are not applicable to the entire city, the case of the 
Municipality of Nicosia is an excellent precedent for the use of AI to support 
policy makers in the development of evidence-based policies. The scalability of 
the initiative in the municipality is facilitated by the existence of a pre-existing 
infrastructural  base  related  to  the  smart  city  strategy  and  other  cities  in  the 
country and abroad can benefit from the lessons learnt from the partial results 
achieved so far in terms of infrastructural challenges, compliance with ethical 
procedures and stakeholder engagement priorities. 

135 

 
 
 
 
 
  
 
 
Case  II.7      The  AI-based  LisNav  application  dedicated  to 
transforming the lives of the visually impaired (Portugal) 

Praça do Comércio, Lisbon, (Portugal) 
Photo credit: Claudio Schwarz 

Initiator: 3FINERY LTD startup, Municipality of Lisbon (PT). 

Project partners: Municipality of Lisbon, Camara Municipal de Aveiro, 3FINERY LTD 
startup. 

External suppliers: n/a. 

Sector of intervention: Social protection; Mobility. 

Funding: Camara Municipal de Lisboa, Camara Municipal de Aveiro and the European 
Regional Development Fund. 

Timing: 2022-2023. 

Expected impact: The LisNav project is aimed at having a transformative impact on the 
lives of people with visual impairments, improving their accessibility to both urban and 
transportation environments through innovative technologies. By leveraging AR and AI, 
this project is designed to enhance mobility, independence, and safety for individuals with 
visual disabilities in both indoor and outdoor spaces. 

Description of the case study 

An  exemplary  case  of  successful  public-private  collaboration,  the  LisNav 
project, aimed to use AR and AI to enhance the quality of life of people with 
visual impairment. Within the framework of the Urban Innovative Action (UIA) 
promoted  by  the  EC,  the  project  was  co-financed  by  Camara  Municipal  de 
Lisboa, Camara Municipal de Aveiro and the European Regional Development 
Fund.  In  addition,  in  the  context  of  the  Better  Mobility  Accelerator  (BMA) 

136 

 
 
 
 
 
 
 
 
 
 
 
 
programme, the project was supported by the European Institution of Innovation 
& Technology (EIT). The initiative was in fact aligned closely with the main 
area of interest of the  BMA programme that is ‘Mobility for all’, intended to 
improve  the  physical  and  geographical  accessibility  of  underserved  and 
vulnerable travellers. 

The technological solution of the LisNav project was conceived and devised by 
the startup 3FINERY LTD, specialised in the use of AR to create new forms of 
experiencing reality in an interactive, game-focused and inclusive way. 

LisNav was an app specifically dedicated to people with visual impairment that 
improved the perception and interaction of users with reality through real-time 
audio  descriptions  that  augment  accessibility  in  urban  outdoor  and  indoor 
environments. The application made use of a combination of data from publicly 
available datasets and allowed users to incorporate information such as those on 
local points of interest. The LisNav app had the distinction of supporting people 
in using public transport by providing information on bus stops and the bus stop 
network.  The  use  of  intelligent  algorithms  combined  with  real-time  audio 
descriptions  was  thus  exploited  to  facilitate  the  accessibility  of  public 
transportation  and  to  increase  the  independence  of  users  even  in  unknown 
surroundings.  Furthermore,  users  had  the  possibility  to  both  access  and  input 
real-time updates. This feature was specifically designed to make travel safer 
through real-time detection of warnings, hazards and emergencies. 

The LisNav project not only benefitted from support at the European level but is 
also  currently  part  of  the  VoxPop  initiative  led  by  the  Lisbon  City  Council 
(CML). This project aims to promote the digital transition of Lisbon’s mobility 
sector  and  is  characterised  by  its  cooperative  approach  as  evidenced  by  the 
involvement of public and private representatives of the mobility sector, aiming 
to ensure the active participation of citizens. 

Discussion of the results 

The LisNav project started in 2022 and lasted 12 months. Nevertheless, the app 
designed  by  the  startup  3FINERY  LTD  is  still  downloadable  by  users.  The 
download  is  free  while  some  additional  features  are  available  for  a  fee.  The 
initiative  of  the  Municipality  of  Lisbon  has  attracted  more  interest  than  its 
creators  anticipated,  especially  from  the  point  of  view  of  its  commercial 
implication.  To  date,  more  than  a  thousand  downloads  of  the  app  have  been 
recorded,  a  number  considered  substantial  considering  the  specific  target 
audience to which it is dedicated.  

137 

 
 
 
 
 
 
 
The success of the project is based on several factors. These include the large 
availability of various categories of publicly accessible data (e.g., historical data, 
real-time connections, databases) and the contribution of programmes at local 
and international level supporting the technological advancement at the service 
of the inclusive mobility sector. These programmes provided not only financial 
support  but  also  consultancy  and  technical  support  to  test  and  initiate  the 
innovative proposal.  

From the point of view of public acceptance, the solution proposed by the LisNav 
project  reported  a  consensus  from  both  the  political  level  and  the  public, 
presumably related to the evident benefit to users and in particular to people with 
disabilities.  To  ensure  user  acceptance,  the  app  also  includes  a  monitoring 
mechanism  for  collecting  feedback.  Furthermore,  the  use  of  data  and  the 
deployment  of  applications  such  as  machine  learning  models  and  Optical 
Character  Recognition  (OCR),  which  converts  text  into  machine-readable 
formats, was carried out in compliance with security procedures and taking into 
account the potential risks of using AI, and in particular in its use for guiding 
user mobility, as it would soon be regulated by the EU AI Act. 

The  LisNav  initiative  can  be  considered  a  good  practice  of  public-private 
collaboration,  bringing  together  stakeholders  from  municipal  governments, 
European programmes and a private start-up. The app’s data-driven approach 
can also inform future urban planning and transportation policies, enabling cities 
to become more inclusive, accessible, and adaptable to the needs of all citizens. 

138 

 
 
 
 
 
Case  II.8.  Immediate  and  Long-Term  Energy  Reduction 
(RECITAL) (France) 

Next to the Arènes de Picasso: Victor Jara Avenue, Noisy-le-Grand.  
Photo credit: Zairon (Source). 

Initiator: Municipality of Noisy-le-Grand (FR). 

Project partners: n/a. 

External suppliers: Citégestion, Datanumia, Efficacity, Eridanis. 

Sector of intervention: Energy. 

Funding: EUR 2.2 million, out of which 50% from the ‘National Strategy for Artificial 
Intelligence’ (EUR 1.1 million), 50% from the Municipality of Noisy-le-Grand (EUR 1.1 
million). 

Timing: 01/09/2023 - 2025. 

Expected impact: To reduce energy consumption and carbon impact of the city's public 
buildings. 20% reduction within the first two years, 50% cut by 2030, while optimising 
renovation expenditure. The project will also be useful for analysing possible scenarios for 
refurbishment and renovation of buildings, optimising costs and limiting CO2 emissions. 

Description of the case study 

Noisy-le-Grand  has  been  implementing  a  smart  strategy  based  on  technology 
and data driven by an innovative approach, in which ‘Immediate and Long-Term 
Energy  Reduction’  (RECITAL)  fits.  Launched  in  summer  2023,  the  project 
entered its operational phase. In the words of Philippe Sajhau, Director of the 
Intelligent City, Innovation and Data:  

139 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
‘For  several  months  now,  this  ‘energy  hypervisor’  has  been  collecting 
consumption data from our buildings, with a ten-year history. This will make it 
possible  to track  consumption on a  daily basis and  identify  episodes  of over-
consumption by means of alerts’15. 

The project will also be useful for analysing possible scenarios for refurbishment 
and renovation of buildings, optimising costs and limiting CO2 emissions. The 
city  administered  questionnaires  to  sports  clubs  and  associations  sites  within 
public buildings to evaluate their energy needs and optimise building usage times 
(particularly for schools and gymnasiums) in order to limit heating times, which 
will further reduce consumption by around 5%. A considerable percentage of the 
available  funds  will  be  earmarked  for  renovation  work  (double  insulation  of 
walls and floors, renovation of ventilation and aeration systems, replacement of 
boilers, window frames, etc.), which is essential to ensure energy efficiency and 
optimise consumption.  

To finance RECITAL, the municipality has obtained financial assistance as part 
of the ‘DIAT’ call for projects (Demonstrators of frugal artificial intelligence at 
the service of the ecological transition of territories), launched by the Ministry 
of  Ecological  Transition  and  Territorial  Cohesion,  endowed  with  EUR  40 
million over five years, as part of the second phase of the ‘National Strategy for 
Artificial Intelligence’ (Snia). The programme, combining two key acceleration 
strategies such as ‘Sustainable Cities and Innovative Buildings’ and ‘Artificial 
Intelligence’, covered 50% of the RECITAL project (equal to EUR 1.1 million). 
Noisy-le-Grand is one of the first four French regions to be recognised by the 
Government  for  proposing  a  solution  based  on  frugal  AI  to  accelerate  the 
ecological transition. Noisy-le-Grand participated alongside Eridanis, Efficacity, 
Datanumia  et  Citegestion  as  partners  of  the  project.  State  funding  was  also 
granted in view of a possible - as well as desired - replicability of the project in 
other municipal contexts throughout France. 

The RECITAL project follows the guidelines laid down by the ‘France Nation 
Verte’ ecological planning programme, which aims to highlight the potential of 
data,  and  more  specifically  AI,  to  respond  to  the  five  main  challenges  of  the 
transition  (i.e.,  consumption  of  resources,  preservation  of 
ecological 
biodiversity, reduction of pollution affecting health, mitigation of and adaptation 
to global warming). Published in September 2021, the first version of the AI and 
Ecological  Transition  Roadmap  of  the  Ministerial  Centre  for  Ecological 

15 “Depuis quelques mois, cet “hyperviseur d’énergie” récupère les données de consommation de nos bâtiments, 
avec un historique sur dix ans. Cela permettra de connaître les consommations à la journée et de repérer des 
épisodes 
version). 
à 
https://www.noisylegrand.fr/information-transversale/actualites/recital-reduire-de-50-les-consommations-
energetiques. 

surconsommation 

alertes.” 

original 

(French 

grâce 

des 

de 

140 

 
 
 
 
 
Transition, Land and Sea covers the period 2021-2023. The project answers also 
to the objectives imposed to French municipalities by  ‘Décret tertiaire’. Also 
known as the Tertiary Eco-Energy Scheme (DEET), this measure – designed to 
promote the energy transition – has been introduced in 2019 as part of the ‘Law 
on changes in housing, land management and digital technology’ (ELAN) and 
requires a progressive reduction of energy consumption in buildings and parts of 
buildings of 1,000 m² and above in tertiary sector to tackle climate change. 

Discussion of the results 

The aim of this project is to use AI to reduce the energy consumption and carbon 
impact of the city's 200 public buildings. The goal is to achieve a 20% reduction 
within  the  first  two  years  and  reach  a  50%  cut  by  2030,  while  optimising 
renovation expenditure.  

The project started in September 2023 with 40 buildings and, after analysing the 
initial performance in the first 18 months, it will be extended to a further 160 
buildings.  

RECITAL also focuses on the installation of smart metres and sensors in each 
building to measure energy consumption, exploiting IoT as a tool to make the 
process of decarbonisation more efficient. The aim is to be able to exploit AI to 
bring more and better services to the population with less money, improving the 
job  of  people  working  for  the  city  and  the  citizens.  By  the  end  of  2024,  an 
algorithm  will  be  deployed  to  study  and  propose  the  most  appropriate 
programmes  of  works  over  six  years,  starting  with  the  forty  buildings  that 
consume the  most energy. The first works suggested by  RECITAL  algorithm 
should take place in 2025.  

Noisy-le-Grand  thus  becomes  a  place  dedicated  to  technological  innovation, 
where  environmental  protection  and  care  for  citizens  and  the  territory  meet, 
providing a virtuous example of green and digital transition. 

141 

 
 
 
 
 
 
 
 
 
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ISBN 978-92-895-3679-0 
DOI:10.2863/6007868

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