Advancing AI adoption : strengths and gaps in the European digital innovation hubs network : insights from the first generation of EDIHs.
Date: 2025-10-03
Institutions: Joint Research Centre
Subjects: action programme, artificial intelligence, coaching in the workplace, data processing, digital literacy, digital single market, digital transformation, innovation, public awareness campaign, regulatory policy, report, vocational training

Joint Research Centre 

Advancing AI Adoption: 
Strengths and Gaps in the 
European Digital Innovation 
Hubs Network 

Insights from the first generation of EDIHs 

Carpentier, E., Nepelski, D., Torrecillas Jodar, J. 

2025 

 
 
 
 
 
 
 
 
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
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Contact information 
Name: Elodie Carpentier 
Address: Joint Research Centre - Edificio Expo, c/Inca Garcilaso 3, E-41092 Sevilla - España 
Email: Elodie.CARPENTIER@ec.europa.eu 

The Joint Research Centre: EU Science Hub 
https://joint-research-centre.ec.europa.eu  

JRC141305 
EUR 40348 

PDF 

ISBN 978-92-68-28468-1 

ISSN 1831-9424  

doi:10.2760/4776421   KJ-01-25-324-EN-N 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

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How to cite this report: Carpentier, E., Nepelski, D. and Torrecillas Jodar, J., Advancing AI Adoption: Strengths and Gaps in 
the European Digital Innovation Hubs Network - Insights from the first generation of EDIHs, Publications Office of the 
European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/4776421, JRC141305. 

Contents 

Abstract ...................................................................................................................................................................................................... 1 

Executive summary .............................................................................................................................................................................. 2 

1  Introduction ....................................................................................................................................................................................... 3 

2  Methodology and sample .......................................................................................................................................................... 5 

2.1  Definition of EDIHs’ AI capabilities ............................................................................................................................ 5 

2.2  Methodology ........................................................................................................................................................................... 6 

2.3  Sample of EDIHs .................................................................................................................................................................. 7 

3  AI capabilities and resources in the EDIH Network .................................................................................................. 10 

3.1  Identifying and addressing customers’ challenges ....................................................................................... 10 

3.2  Helping customers implement AI solutions ....................................................................................................... 14 

3.3  Leveraging the AI ecosystem ..................................................................................................................................... 17 

3.4  Increasing awareness of AI opportunities and training the workforce .............................................. 19 

4  Conclusion and policy recommendations ...................................................................................................................... 21 

References ............................................................................................................................................................................................. 23 

List of abbreviations and definitions ...................................................................................................................................... 24 

List of figures ....................................................................................................................................................................................... 25 

List of tables ......................................................................................................................................................................................... 26 

Annex ........................................................................................................................................................................................................ 27 

 
 
 
 
Abstract 

This report investigates the strengths and gaps in the European Digital Innovation Hubs (EDIH) Net-
work in its first stage, with regards to advancing AI adoption within Europe. As announced in the AI 
Continent Action Plan, the second generation of EDIHs will become AI experience centres and need to 
reinforce their AI-related services and capacities. From 16 extensive interviews conducted with a di-
verse sample of EDIHs and complemented with EDIH-reported data on their activities, the report of-
fers  an  in-depth  analysis  of  capabilities  of  the  first  generation  of  EDIHs,  such  as  identifying  and 
addressing customers' challenges, helping customers implement AI solutions, leveraging the AI eco-
system, and raising  awareness  and skills for  AI. In  this first phase, the EDIHs demonstrate strong 
capacity in understanding and addressing customer challenges related to AI adoption through sub-
stantial  support  like  awareness  sessions,  training  programs,  and  mentoring.  EDIHs  are  supporting 
established AI applications more commonly than cutting-edge ones, which coincides with the limited 
AI readiness of their customers, as successfully adopting AI requires a foundational level of digital 
maturity, including a solid digital business strategy, skills, processes, and infrastructure. Primary sec-
tors of AI adoption are manufacturing, health care, and energy. EDIHs are well-connected with digital 
providers, AI experts, and testing facilities, but they have fewer links with regulatory bodies affecting 
full AI implementation support. While EDIHs are active in raising AI awareness and enhancing skills, 
most show shortcomings in assessing social and environmental impacts and need improvements in 
providing regulation and ethics guidance. To address this challenge, the Apply AI Strategy will propose 
how  to  better  prepare  the  second  generation  of  the  Network  to  serve  as  first  line  help  desks  for 
regulatory compliance.  

1 

 
 
 
 
Executive summary  

This report investigates the capability of the first generation of the European Digital Innovation Hubs 
(EDIH) Network in advancing AI adoption across the private and public sectors in Europe. The EDIH 
Network  comprises  151  EDIHs  established  since  2023  and  funded  under  the  Digital  Europe  Pro-
gramme, and by September 2024, more than 80% of them have delivered AI services to their cus-
tomers.  

From  16  extensive  interviews  conducted  with  a  diverse  sample  of  EDIHs  from  various  regions  in 
Europe and complemented with EDIH-reported data on their activities, the report offers an in-depth 
analysis  of  the  following  capabilities  of  EDIHs:  identifying  and  addressing  customers’  challenges, 
helping customers implement AI solutions, leveraging the AI ecosystem, and increasing awareness of 
AI opportunities and training the workforce. 

EDIHs demonstrate capabilities in understanding and addressing customer challenges related to AI 
adoption. The main barriers that EDIH identified for their customers are the availability and quality of 
data for AI implementation, and the lack of awareness and education on AI. EDIHs provide substantial 
support through AI-tailored awareness sessions, training programs, and mentoring to improve data 
collection processes. In addition, some EDIHs have developed AI maturity assessment tools to gauge 
customer readiness and frame appropriate implementation roadmaps. 

EDIHs display a higher proficiency in supporting the implementation of AI for established applications 
such  as  machine  learning  and  data  analytics  than  for  cutting-edge  ones  like  expert  systems  and 
robotics.  The  primary  sectors  experiencing  AI  adoption  with  EDIH  support  include  manufacturing, 
health care, and energy. Most implementations rely on customers’ and public datasets rather than 
private ones and, in this first stage, EDIHs are not acting as trusted data intermediaries, i.e. collecting 
private data, anonymising it and sharing it with other companies for AI model testing. 

While EDIHs are well-connected to digital providers, AI experts, and testing and experimentation fa-
cilities, they have more limited links with regulatory frameworks and sandbox providers as well as 
standardisation bodies.  In the second generation of the EDIH Network, EDIHs will need better links to 
regulatory basis to be able to act as the first line help desks for regulatory compliance. EDIHs support 
customers in access to finance, although funding is not specific to AI. EDIHs are sometimes aware of 
other AI initiatives in their local ecosystem but they were little knowledgeable of the AI Factories, 
although this sub-component of the EuroHPC Joint Undertaking initiative was not yet completed at 
the time of interviews. At the time, only a  third of them  could perceive  the relevance of these AI 
Factories for their customers. 

Finally, EDIHs display enthusiasm in raising awareness of AI opportunities and enhancing workforce 
skills. However, EDIHs need improvement on assessing the social and environmental impacts of AI 
and providing regulation and ethics guidance. Respondents expressed a strong interest in enhancing 
their regulatory compliance capacities and service offer and identify a need to implement systematic 
procedures to check compliance with the AI Act during implementation.  

2 

 
 
1 

Introduction  

While small and medium-sized enterprises (SMEs) and public sector organisations (PSOs) continue to 
face challenges in adopting digital solutions and introducing digital innovations, public sector inter-
ventions bear promises in easing these challenges. The European Commission has made the digital 
transformation of companies and the public sector one of its top priorities.1  

Since 2023, the European Commission established a network of European Digital Innovation Hubs 
(EDIHs)  to  support  the  digital  transformation  of  SMEs  and  PSOs.  The  151  EDIHs  are  consortia  of 
research and technology organisations, universities, and private sector companies (De Nigris, Kalpaka, 
& and Nepelski, 2023). They assess the digital maturity of their customers2 and provide a range of 
services for free or at a low cost: networking and access to innovation ecosystems, training and skills 
development,3 support to find investment, and testing technologies and solutions before investing in 
them. These  services rely on  technologies  from the most established, such as  logistics or  internet 
services and applications, to the most frontier ones, such as artificial intelligence (AI) and decision 
support or internet of things.  

By the end of 2024, more than 80% of EDIHs already delivered services involving artificial intelligence 
(Carpentier, D'Adda, Nepelski, & and Stake, 2025). Among companies approaching EDIHs, SMEs are 
less digitalised than larger firms. They exhibit also lower  levels  of adoption of  AI  and automation 
technologies (Carpentier, D'Adda, Nepelski, & and Stake, 2025).4 In the digital transformation process 
of EDIH customers, the first step is to establish a business strategy that identifies the relevant digital 
innovations for the company needs, then collecting and managing data, recruiting, training and em-
powering  employees,  and  the  last  step  is  to  implement  AI  and  automation  (Carpentier,  D'Adda, 
Nepelski, & and Stake, 2025).  

These main trends rely on the data collected systematically from EDIHs. While they offer an interest-
ing overview of the EDIH Network’s AI-related activities, they remain superficial and do not identify, 
for  instance,  the  branches  or  applications  of  AI  involved, the  regulatory  skills of  the  EDIHs  or  the 
difficulties  their  customers  face  when  adopting  these  technologies.  To  better  understand  the  AI-
related capabilities in the EDIH Network, we carried out 16 interviews with EDIH representatives. Con-
sidering the scope of EDIHs’ activities, we adopt a broad definition of relevant capabilities that en-
compasses:  

  identifying customers’ challenges in AI adoption and finding appropriate strategies to over-

come them,  

  accompanying the implementation of AI solutions in clients’ digital transformation,  

1 Its first priority, a new plan for Europe's sustainable prosperity and competitiveness, includes “boosting productivity with 
digital tech diffusion to strengthen EU's competitiveness and become a leader in AI innovation”, and “turbo charging 
investment to accelerate green, digital and social transition” (European Commission: Directorate-General for 
Communication and Leyen, U. v. d., 2024). 

2 Using the Digital Maturity Assessment Tool (DMAT), developed by the European Commission. For more information about 

the tool, see Kalpaka, Rissola, De Nigris, & Nepelski (2023).  

3 Training and skills development represent 30% of all the services that EDIHs delivered until the end of 2024, and 39% 

of them are related to artificial intelligence (see Table 1).  

4 This finding relies on almost 9,000 digital maturity assessment of EDIHs customers performed prior to delivering ser-

vices. 

3 

 
 
 
 
  leveraging the AI ecosystem to support their customers’ AI-related needs, and 

  increasing AI awareness and skills. 

In the second section, we define more in details the relevant capabilities related to AI that we aim at 
measuring, justify our choice of a qualitative approach, and describe interview participants compared 
to the rest of the EDIH Network. In the third section, we present the findings, grouped into the four 
main capabilities. In the last section, we conclude and provide a few policy recommendations.  

4 

 
 
2  Methodology and sample 

In this section, we define the assessed capabilities related to AI, describe the empirical approach to 
identify them, and characterise the sample of EDIHs that participated in the interviews compared to 
the rest of the EDIH Network.  

2.1  Definition of EDIHs’ AI capabilities 

EDIHs’ mission is to support the digitalisation of SMEs and public sector, with a focus on AI technolo-
gies. In a general way, they execute this mission through four broad categories of activities:  

  raising awareness of digital solutions and their benefits for organisations,  

  developing and testing solutions before organisations invest into them, 

  training and educating organisations and their employees on digital topics, and 

  supporting organisations in accessing financing for their digital transformation. 

Considering the wide scope of these activities, we define a set of capabilities and finer-grained func-
tions in relation with AI, as reported in Table 1.5  

Table 1. Definition of AI-relevant capabilities of EDIHs 

Capability 
Identifying customers’ challenges in adopting AI and establish-
ing a strategy to solve them, including assessing the feasibility 
and relevance of AI solutions for customers. 
Accompanying the implementation of AI solutions in clients’ dig-
ital transformation, by offering support in different branches 
and applications of AI in different sectors and sourcing and 
managing data for AI implementation. 
Leveraging the AI ecosystem to serve their customers’ needs in 
AI, through matchmaking, facilitating access to finance for AI, 
and positioning themselves in the AI landscape. 

Increasing awareness around AI opportunities and training the 
workforce on AI skills, while 
•  Accounting for the environmental and social impacts of AI 

• 

in awareness raising, training and implementation activities, 
Increasing customers’ understanding of legislation and reg-
ulations related to AI and supporting regulatory compliance 
during implementation, and 

•  Supporting other EDIHs in the EDIH Network, e.g. through 
clients’ takeover or training them in AI topics and tools. 

Source: JRC elaboration. 

Relevant function(s) 
•  AI adoption road mapping 
•  Test before invest & solu-

tion screening 

•  Solution deployment sup-

port 

•  Support to find investment 

• 

for AI 
Innovation ecosystem & 
networking 

•  Test before invest & solu-

tion screening 
•  Talent development 
•  Regulation and ethics 

guidance 

•  Capacity building for EDIHs 

5 The list of fine-grained functions is available in questions 18 and 19 in Appendix Table 3. 

5 

 
 
 
 
2.2  Methodology 

Considering the extent and nature of the AI capabilities that we aim to assess, as well as the particular 
setup in which EDIHs operate, this subsection describes the data sources available and justifies the 
selection of a qualitative approach.  

2.2.1  Data from the EDIH platform  

The first and directly available source of data, as mentioned in the introduction, are the ones that 
EDIH report in the EDIH platform6 and describing their activities and associated customers in details. 
This data was useful to produce previous reports on EDIHs characteristics, activities and customers 
(De Nigris, Kalpaka, & and Nepelski 2023; Carpentier, D'Adda, Nepelski, & and Stake, 2025). Despite 
the exhaustivity of these data, they remain superficial regarding the specific branches or applications 
of AI involved in the service delivery,7 and do not characterise, for instance, the source of data used 
in AI implementation, EDIHs’ AI regulatory expertise or their ability to act as trusted data intermedi-
aries.8  

2.2.2  Data on scientific and technological outputs of organisations 

Another, potentially more objective, source of information on EDIHs’ AI capabilities are the scientific 
publications and patent records of their participating organisations. Not only would it be limited to 
scientific and technical capabilities, overlooking entirely networking and other soft skills relevant for 
EDIHs, but it would not be suitable to the EDIHs’ setup, and other innovation intermediaries alike, for 
the following reason.  

Three main groups of actors are involved in innovation intermediaries’ activities: organisations that 
generate the AI knowledge and innovations (“AI developers”), organisations that form the EDIHs (AI 
innovation  intermediaries, or  “AI  intermediaries”  for  short),  and  organisations  that  receive  support 
from the innovation intermediaries to adopt AI (“AI users”). These groups are not entirely separate, 
for example some AI developers may belong to AI intermediaries, although this is not a requirement 
if AI intermediaries have sufficient links to AI developers. In addition, the presence of AI developers 
within the AI intermediary may increase the recognition of opportunities to commercialise an AI so-
lution  and  facilitate  its  provision.  However,  the  presence  of  developers  within  the  intermediary  is 
independent of the capability of the AI intermediary in its broader functions, such as assessing the 
full digital needs of the potential user and whether the AI solutions available “in-house” are indeed 
the relevant solution for their needs or providing legal support if the solution involves the develop-
ment  of  a  tailored  AI  solution.  In  this  context,  following  paper  trails  of  scientific  publications  and 
patent applications of organisations forming an AI intermediary is not appropriate, since it would only 
be informative of AI capabilities of the narrow subset of AI developers within the AI intermediary.  

6 The EDIH platform is managed by the Digital Transformation Accelerator (DTA). Digital Transformation Accelerator for 

the network of European Digital Innovation Hubs | European Digital Innovation Hubs Network 

7 EDIHs can indicate if an activity is related to AI using a tag (“Artificial intelligence and decision support”), and there is a 

free text field for EDIHs to describe the content of the service in more details that is rarely filled in, but there is not a 
normalised field in which EDIHs can report the particular technology or content that the service relies on. 

8 Trusted data intermediaries are entities that are expected to provide a trusted and secure environment in which compa-

nies or individuals can share data for testing and developing of AI innovations.  

6 

 
 
 
 
2.2.3  A qualitative approach 

Considering the limitations of using a large-scale quantitative approach, we decided to select a qual-
itative one, interviews, that would allow to explore the various capabilities that we are interested in, 
while facilitating a possible later survey to extend to the rest of the EDIH Network. The first 7 inter-
views belonged to a pilot group, on which the applicability and relevance of the questions were tested 
and the EDIHs were also invited to provide feedback on potentially relevant topics that we initially 
overlooked.   

2.3  Sample of EDIHs 

2.3.1  Sampling strategy 

As the aim of the interviews is to determine the AI capabilities in the EDIH Network, we deliberately 
focused our attention on the EDIHs that were most likely to be involved in providing AI services, either 
as their main technology or as one of a few main technologies.  

We aimed at covering at least 10% of the total population of EDIHs, which would be 15 out of the 
151 EDIHs in the EDIH Network.9 Policy officers at DG CNECT, who are in regular contact with the 
hubs, provided an initial list of 13 hubs that were among the most likely to be delivering AI services. 
They also included some EDIHs that are less focused on AI, to ensure some heterogeneity and verify 
the ability of the interview script to adapt to such cases. 

We contacted the EDIH coordinators and their AI experts (if available) by email, explaining the aim of 
the interview, the expected format, content and outcome for DG CNECT and for themselves. To ensure 
the relevance and accuracy of information collected during the interview, we requested that the rep-
resentatives most closely involved in the delivery of AI services participate in the interview.  

10 EDIHs responded positively and participated in the interview. As we had not yet reached the target 
of 10% of the EDIH Network, we repeated the process and collected a second list of 10 more EDIHs. 
Six of them took part in the interview. This represents an overall response rate of 70%. 

We conducted a total of 16 semi-structured interviews with EDIHs representatives in September and 
October 2024.10 The list of questions asked during interview is reported in Annex Table 2. After the 
first pilot round of 7 interviews, we identified an opportunity to improve the data collection for a few 
specific questions related to the sectors, AI applications and technologies, and functions performed. 
We introduced a short survey accordingly, reported in Annex Table 4, that we sent to respondents 
ahead of the interview so the responses could be revised together during the interview rather than 
collected entirely at that time. This modification in the protocol substantially improved the complete-
ness and accuracy of answers to these questions. 

The interviews took place online and lasted on average one hour and 10 minutes, ranging from 50 
minutes to one hour and 45 minutes. To maximise the extent and accuracy of information collected, 

9 At the time of the interviews, there were 151 EDIHs funded under the Digital Europe Programme. The other 74 Seals of 
Excellence have no reporting obligations since they are not funded by the European Commission and were therefore 
not considered in the analysis.  

10 The interview participants are AI5production, AIRE, DATA EDIH, DIGITALZH, DIVA, EDCASS, EDIH4IAE.LT, EDIH4MARCHE, 

EDIH4UrbanSAVE, EDIHLV, EDIH-SNL, H4i, HDS, OCEANOPOLIS, SustAIn.brussels, and TDIH. 

7 

 
 
 
 
we video recorded most of the interviews.11 We also have the transcript of interviews from Microsoft 
Teams’ embedded transcription service.  

2.3.2  Interviewees characteristics 

The map in Figure 1 shows in green the 16 regions of the 15 countries with EDIHs that participated 
in the interviews (there are two EDIHs in two regions of the Netherlands that were interviewed). Other 
regions with EDIHs that were not invited or declined to participate are in orange. The grey regions are 
those  where  there is  no  EDIH  coordinator  (although  there  may  be  EDIH  participants).  EDIHs  inter-
viewed  were  well  distributed  across  the  EDIH  Network,  with  a  slightly  higher  concentration  in  the 
North-Eastern countries. 

Figure 1. Geographical location of interviewed and non-interviewed EDIHs   

Note: EDIHs are located in the NUTS2 region of the coordinator. Four interviewed EDIHs located in Belgium, Hungary, Aus-

tria and Germany are difficult to distinguish on the map.  

Source: JRC elaboration. 

We further explore interviewees profile compared to the rest of the EDIH Network leveraging the data 
on services delivered as reported in the EDIH platform. The accuracy of this data was verified with 
EDIHs  at  the  beginning  of  the  interviews.  In  a  few  cases,  these  numbers  were  understating  their 
service delivery either because they did not update the latest activities or a technical issue arose. 
Table 2 reports the average number and percentage of AI services per hub for interviewees and the 
rest of the EDIH Network separately, at the time of interviews. We identify the following noteworthy 
differences between the two groups:  

11 In two cases, the analysis relies on the interviewer’s handwritten notes. In one case, the interview was relocated on a 

different platform due to authorisation issues and we could not record it, and in the other case the recording failed.  

8 

 
 
 
 
 
  First, interviewees are among the most active hubs in the EDIH Network:12 they delivered 316 

AI services per hub on average, compared to 41 in the rest of the EDIH Network.  

  Second, the interviewees are specialised on AI to a greater extent than the rest of the EDIH 
Network: on average, 47% of their services involved AI technologies compared to 28% in the 
rest of the EDIH Network.  

Third, the type of AI services delivered differs across the two groups, with interviewees being relatively 
more AI-focused in their “networking and access to innovation ecosystems” and “support to find in-
vestment” services, while the rest of the EDIH Network is more AI-focused in their “test before invest” 
and “training and skills development” services.  

Table 2. Average number and share of AI services among interviewees and in the rest of the EDIH Network 

Type of service  Average  number 

of AI services 

Average  %  of  AI 
services  

Average  number 
of AI services 

Average  %  of  AI 
services  

Interviewees 

Rest of the EDIH Network 

Networking  
Funding  
Test before invest 
Skills 
Total 

199 
23 
102 
132 
316 

64% 
47% 
40% 
44% 
47% 

20 
11 
19 
24 
41 

23% 
21% 
34% 
30% 
28% 

Notes: There are 16 EDIHs in the group of interviewees, 13 of which delivered at least one AI-related service, and 107 
reporting EDIHs in the group of the rest of the EDIH Network, of which 74 reported at least one AI-related service. In 
each sub-group, not all EDIHs have delivered AI services in each of the four types of services, so we report the average 
number of AI services rather than the raw count and the average percentage of AI services to account for these differ-
ent group sizes.  

Source: JRC elaboration. 

12 This is true both for services relying on AI and all services overall.  

9 

 
 
 
 
 
3  AI capabilities and resources in the EDIH Network 

In this section, we present the findings from the interviews, following the four categories of AI capa-
bilities as defined in subsection 2.1: Identifying and addressing customers’ challenges, helping cus-
tomers implement AI solutions, leveraging the AI ecosystem, and increasing awareness of AI oppor-
tunities and training the workforce.  

These capabilities were closely related to a set of 8 AI-related functions. To get an overview of the 
functions offered in the EDIH Network, Figure 2 shows the most to least common functions provided 
by respondents. EDIHs widely offer test before invest and solution screening (94%) as well as inno-
vation  ecosystem  and  networking  services  (94%).  Talent  development,  which  covers  training  and 
mentoring, is offered by 81% of respondents. The remaining respondents do not offer it because it is 
already  widely  available  in  their ecosystem  at  a  low  cost.  81%  also  offer  AI  solution deployment 
support, in which they accompany their customers in deploying an AI solution in their business oper-
ations. Relatively fewer offer support to find investment for AI, although many mentioned that they 
provide the service in a broader way without the funding being specific to an AI technology. Signifi-
cantly fewer provide regulation and ethics guidance (62%) or capacity building for EDIHs, i.e. training 
other EDIHs in the Network (44%).  

Figure 2. Percentage of respondents supporting their clients in 8 AI-related functions 

Note: This figure displays the percentage of EDIHs which responded “yes” to providing the different AI-related functions. It 

corresponds to question 18 in Annex Table 3.  

Source: JRC calculations based on interviews. 

Broadly speaking, the AI expertise available within the EDIH respondents mostly lies in the universities 
and research centres, while 25% of respondents have a partner with AI expertise from the private 
sector.  

3.1  Identifying and addressing customers’ challenges 

The first capability that we assess is to identify customers’ challenges in adopting AI and establishing 
a strategy to solve them, including by using or developing a dedicated AI maturity assessment tool.  

3.1.1  Barrier to AI uptake among European SMEs and the public sector 

Respondents indicate several barriers that their customers are facing in adopting AI. Figure 3 reports 
the different barriers mentioned by respondent by order of frequency.  

10 

 
 
 
Figure 3. Percentage of respondents citing common barriers their clients face when adopting AI 

Note: This figure displays the percentage of EDIHs that mentioned the referenced category as a barrier to AI uptake among 
their customers. Each respondent could mention one or multiple barriers. It corresponds to question 12 in Annex Table 
3.  

Source: JRC calculations based on interviews. 

A first set of barriers relates to resource constraints:   

  60%  of  respondents indicate  a  barrier to  AI  implementation  the  lack  of  availability  and 
quality of data that their clients are collecting, if any, to use as an input in implementing AI 
solutions. 

  27% of respondents further mention the lack of skills or talent in the organisation or its 
local talent pool. Some companies may have standard developers, but they cannot develop 
advanced AI solutions, so they need EDIH support for that. Other companies indicate that the 
local talent pool sometimes prefers to work for multinationals or leaves the country for better 
opportunities in Northern and Eastern European countries, leaving the local companies with 
an under skilled workforce.  

  13% of respondents mention the lack of time. Adopting AI is a lengthy, generally multi-step, 
process. Companies often don’t have time to enter it. Taken in combination with other barriers 
(lack of good quality data, lack of skills, unclear benefits), it discourages AI adoption among 
EDIH clients.  

  Similarly, 13% of respondent point to the lack of financial resources to invest in AI solu-

tions or dedicate human resources to the adoption of AI, especially for SMEs. 

Another important set of barriers relates to a limited AI literacy:   

  The lack of AI literacy itself, i.e. awareness and education about artificial intelligence, is 
also  mentioned  by  60%  of  respondents.  Many  customers  do  not  understand, or  at  a  very 
superficial level, what AI is, what it  can  and cannot do,  how  it  can be implemented in  the 
organisation processes or even how to get started. 

“Sometimes there is a huge confusion between deep learning, learning in general, and artificial 
intelligence in general, which includes for example, knowledge representation and reasoning. 
And we need to explain that.” TDIH, Romania 

“[The main barrier is] unawareness of how AI is linked to business processes. […] Let's make 
an example. We have a production company. It has its client research management system. It 

11 

 
 
 
has its production management system, quality control, logistics and so on. So [we] need to 
go vertically in the business process and understand how to use AI. It's where are the bottle-
necks, what doesn't work. And if we could make it work with AI and where is the reduction? 
Where is the increase in productivity and that is a bottleneck. We understand the technologies, 
but we don't understand how the technologies could work in terms of our business processes.” 
AIRE, Estonia 

  The unclear benefits of AI adoption for customers are mentioned by 27% of respondents. 
Customers and EDIHs find it difficult to estimate the expected return on investment for AI 
adoption, especially for technologies or sectors where there exists too few use cases or suc-
cess stories to build upon.   

To a lesser extent, 13% of respondents mention the other following barriers:  

  Fear of cybersecurity threats AI technologies may pose. Clients are concerned about the 
AI  system  learning  from  their  activity  and  how  their  data  gets  out  of  their  control.  Public 
sector clients are particularly concerned about sharing citizen’s data with AI solution providers 
in the market. 

  Intergenerational prejudice against AI. Although the recent hype around ChatGPT and gen-
erative AI strongly reduced this prejudice, a respondent refers to a particular age group that 
remains resistant to change:  

“If we get a little bigger [company] […], we have a specific age rate of management persons 
that are really afraid of AI. So, we can build a prototype, but if it comes to: how can we bring 
that  into  the  company?  Do  you  want  to  use  that?  Then  they  [refuse  and]  bring  up  a  lot  of 
arguments that are not really rational.” EDIH4UrbanSAVE, Germany 

All these barriers are quite consistent with those identified in other investigations. In an earlier and 
independent task, the JRC established a focus group on AI13 and surveyed 98 companies on their use, 
barriers and impact of adopting AI. To the question of what constitutes a barrier to AI adoption, the 
three main answers were access to data, on which 94% of respondents (strongly) agree, access to 
finance (88%), and access to relevant skills (82%). Even though the point of view is different between 
the two exercises, with EDIHs’ perception in the 16 interviews compared to the SMEs themselves in 
the focus group survey, the main barrier identified is consistent between the two studies, i.e. access 
and  quality  of  data  to implement  AI  solutions.  Additionally,  the  access  to  relevant  skills  was  also 
mentioned by a 27% of interview respondents, relatively less frequently than among SMEs from the 
focus group, while access to finance was seldom mentioned by EDIHs.  

13 in collaboration with the Digital SME Alliance: https://www.digitalsme.eu/european-focus-group-on-artificial-intelli-

gence/ 

12 

 
 
 
 
 
3.1.2  Current approaches to overcome barriers to AI uptake 

During the interviews, EDIHs sometimes mentioned ways in which they address some of the barriers 
that their clients face in adopting AI, from creating or improving the data collection process to provid-
ing knowledge on the technologies, convincing and reassuring clients with rational arguments, training 
their staff and convincing higher management with AI prototypes.   

3.1.2.1  Resource constraints 

When data is not available in the clients’ organisation or the quality is not sufficient to implement an 
AI solution, EDIHs would take a step back: 

  Either help the client review if and how it collects its data and proceed to support them in 
implementing a proper data collection process. In some cases, EDIHs have the robots gather-
ing data directly to train and calibrate their underlying AI models.  

  Or connect the client with a service provider who would support the company in starting or 

improving their data collection process.  

Any of these strategies delays the AI implementation, of course, but it enables it later.  

To address the lack of skills, EDIHs offer trainings on AI tools,14 and would help companies perform 
the necessary research and development to tailor an AI solution to the customers’ need beyond what 
standard developers can do.  

EDIHs also provide support to find investment when the required AI solution is too expensive for the 
client. 

3.1.2.2  Limited AI literacy 

Respondents are very active on raising awareness and increase the level of understanding of AI and 
its practical functioning. They organise trainings, seminars, webinars, professional development days 
to explain how AI works in practical ways, and what it can do: 

“[We explain] how AI works in practice in the specific process of companies like AI in marketing, AI in 
sales, AI in production, very business-oriented functions.” EDIHLV, Latvia 

“We  need  to  be  there  and  help  them  [the  clients]  sort  of  decrypt  the  AI  and  how  to  get  started.” 
OCEANOPOLIS, Norway 

EDIHs further help convincing their customers to adopt AI solutions by computing the return on in-
vestment early in the process. However, it can be difficult depending on the sector under considera-
tion. The health sector was mentioned as easier compared to any other. While more and more success 
stories are being published, respondents  are hopeful  that these can help  them in convincing their 
customers and allow customers to identify with the cases described in the stories so that EDIHs can 
estimate more precisely a potential return on investment. 

14 Respondents did not specify extensively the trainings offered. This topic will be thematic focus of a forthcoming report 
on activities in the EDIH Network, which will emphasise the role and activities of EDIHs in improving AI skills among 
European firms and public sector organisations.   

13 

 
 
 
 
“We are reading the [industry] reports. We are reading about these big multinational corporations that 
come out there. We read about jobs being replaced at sort of aggregate numbers, but what does it 
mean for  my organisation? Are we  speaking about jobs, or should we be speaking about  tasks and 
processes? Our EDIH must help SMEs to get a grip on that one, making people understand, and where 
to start and do the right things to prepare the ground for AI.” OCEANOPOLIS, Norway 

“We did tailor the service called “AI suitability analysis”. [We] help the company vertically in their busi-
ness processes to understand where the reduction [in cost/bottleneck] comes if we use that technology 
and there's no sense of implementing an AI-enabled robot if, for example, there is no bottleneck or 
there is not enough clients or not enough production volumes, then it's cheaper that the human con-
tinues to do the work.” AIRE, Estonia 

3.1.2.3  Other barriers 

To address cybersecurity concerns, EDIHs discuss the topic in workshops. When facing intergenera-
tional prejudice and fear of AI, EDIH4UrbanSave mentions that they developed a service that is called 
“AI for leaders”, in which they develop a prototype, just to show their clients how AI works and reassure 
them in that “it's not a robot that is taking over the world leadership”. 

Beyond all the previously mentioned client-oriented solutions, a few EDIHs also collaborate among 
themselves, sharing knowledge and experiences, such as use cases, facilitating access to AI infra-
structures or addressing the needs of clients from other EDIHs with whom they established a part-
nership to further remove barriers to AI adoption.   

3.1.3  A tool dedicated to assessing the AI maturity of customers  

Although Figure 2 previously showed that 88% of respondent offer AI adoption road mapping ser-
vices, only 19% of them have developed and are using a dedicated tool, in the form of a survey, to 
delve  specifically  in  the  challenges proper  to  AI  adoption  and  identify  the  relevant  AI  solutions  to 
address them. All other respondents are using the more generic Digital Maturity Assessment Tool 
(DMAT) provided by the European Commission (Kalpaka, Rissola, De Nigris, & Nepelski, 2023). 38% 
of them have considered developing or acquiring an additional AI-dedicated tool, but most of them 
consider it unnecessary because the DMAT already includes a section dedicated to the AI maturity of 
customers. 

3.2  Helping customers implement AI solutions 

The second capability that we assess is accompanying the implementation of AI solutions in clients’ 
digital transformation, by offering support in different branches and applications of AI, in different 
sectors, and sourcing and managing data for AI implementation.  

3.2.1  Sectors addressed  

Consistent with the sample selection, 81% of respondents indicate that AI is either the main technol-
ogy (50%) or one of the main technologies (31%) in their service delivery. Figure 4 shows that most 
respondents deliver AI services in manufacturing and processing (75%), health care (56%) and energy 
(50%). About a quarter of EDIHs also deliver AI services in public administration, agricultural biotech-
nology and food biotechnology and automotive. The other sectors (not displayed in Figure 4) are less 
common and gather fewer than 20% of respondents.  

14 

 
 
 
Figure 4. Top 6 sectors in which respondents have delivered AI services 

Note: This figure displays the 6 most frequent sectors in which respondents deliver AI services, corresponding to question 3 
in Annex Table 3. There are 39 sectors in total, using the EDIH nomenclature. For 9 respondents, they checked the boxes 
in the pre-interview survey, 3 others filled it after the interview, and for the others their reply is inferred from the 
content of the remaining of the interview.  

Source: JRC calculations based on interviews. 

3.2.2  Branches and applications of AI 

Figure 5 indicates the share of respondents that have delivered services in eight branches of AI. All 
respondents have delivered services involving machine learning, which includes generative AI. Other 
common branches across the EDIH Network are computer vision, robotics, and natural language pro-
cessing (50 to 75% of EDIHs). Expert systems and human-computer interaction are less common (25 
and 31% of respondents respectively) and none of the respondents have delivered services involving 
fuzzy logic.  

Figure 5. Percentage of respondents that used different branches of AI technologies in services delivered

Note: This figure displays all the branches of AI in which respondents deliver AI services, corresponding to question 4 in 
Annex Table 3. For 9 respondents, they checked the boxes in the pre-interview survey, 3 others filled it after the inter-
view, and for the others their reply is inferred from the content of the remaining of the interview.  

Source: JRC calculations based on interviews. 

Taking a closer look at the specific AI applications that respondents provided, Figure 6 indicates that 
more than 80% of EDIHs are proficient in established applications such as data analytics, predictive 
analytics and image recognition and classification. Other common applications include text genera-
tion, LLMs and chat bots and virtual assistants (50 to 62% of respondents). EDIHs are less frequently 
reporting sentiment analysis (12%), audio generation (6%) or video generation (0%) as part of their 
AI service delivery.     

15 

Figure 6. Percentage of respondents currently using different AI applications to support clients’ AI adoption 

Note: This figure displays all AI-related applications involved in the service delivery of respondents, corresponding to ques-
tion 5 in Annex Table 3. For 9 respondents, they checked the boxes in the pre-interview survey, 3 others filled it after 
the interview, and the others were asked to freely answer the question.  

Source: JRC calculations based on interviews. 

3.2.3  Data sources and management 

Figure  7  indicates  the  primary  source  of  data  that  respondents  used  in  AI  applications  with  their 
clients. Setting aside one respondent that is not developing nor implementing AI applications with 
customers and therefore to whom the question was  not applicable, all respondents indicated that 
they primarily work with the data provided by the client, although this often involved an additional 
preliminary step of ensuring a proper data collection on clients’ side prior to the AI application devel-
opment  and/or  implementation.  69%  of  respondents  combine  client-provided  data  with  public  or 
open-access  datasets.  It  is  less  common  to  use  EDIH  partners’  datasets  (25%  of  respondents)  or 
private datasets beyond the one provided by the client (19%). 

Figure 7. Percentage of respondents using different sources of data in AI applications with their clients 

Note: This figure displays the different sources of data used by EDIHs in developing or implementing AI applications with 

their customers. It corresponds to the open-ended question 6 in Annex Table 3.  

Source: JRC calculations based on interviews. 

16 

 
 
 
 
 
Despite working mainly with client-provided datasets, some EDIH respondents did not see themselves 
as playing a  leading role as trusted data intermediaries.  Such a role implies collecting customers’ 
data, anonymising it, and making it available to other companies for AI model testing. Figure 8 shows 
most respondents indicate proficiency in collecting and anonymising the data, and less so to share it 
for AI model testing. Respondents generally referred to a reticence from private sector customers to 
share any data and constraining EDIHs’ actions through data sharing agreements.  

Figure 8. Percentage of respondents with some ability to perform different functions of a trusted data inter-
mediary 

Note: This figure displays the percentage of respondents that indicated some ability and experience in performing the cor-

responding task. It corresponds to the open-ended question 7 in Annex Table 3.  

Source: JRC calculations based on interviews. 

3.3  Leveraging the AI ecosystem 

The third capability that we assess is to leverage the AI ecosystem to serve customers’ needs in AI, 
through  matchmaking,  facilitating  access  to  financing  for  AI  and  positioning  themselves  in  the  AI 
landscape. 

3.3.1  Extent of EDIHs’ network to support customers’ needs   

While most respondents are well-connected to AI experts, digital transformation partners and AI test-
ing and experimenting facilities in their ecosystem, fewer of them (62%) are connected to regulatory 
sandbox providers, 50% to AI framework providers and only 19% to AI standardisation bodies (Figure 
9). A few respondents did not know what regulatory sandboxes, AI frameworks and/or AI standardi-
sation bodies are. 

17 

 
 
 
Figure 9. Percentage of respondents who know different types of actors in their ecosystem 

Note: This figure displays the percentage of EDIHs which responded “yes” to knowing, in their ecosystem, an organisation 

providing the different categories of AI-related services. It corresponds to question 14 in Annex Table 3.  

Source: JRC calculations based on interviews. 

3.3.2  Facilitating access to AI funding 

In Figure 2, we noted that 75% of respondents indicate that they provide support to find investment 
for AI. However, during the interviews, many respondents clarified that this is rather a generic service 
since there are no funding streams dedicated specifically to AI.  

3.3.3  Awareness of complementary or competing AI initiatives and strategic po-

sitioning 

We asked interviewees if they were aware of other AI initiatives in their local ecosystem.15 The specific 
scope was undefined, so respondents considered either their region (in bigger countries) or their coun-
try (for smaller ones). Two-third of respondents could identify AI initiatives around them, in Estonia, 
Hungary, Latvia, the Netherlands, Poland and Sweden. The other respondents in Italy, Lithuania and 
Slovakia were not aware of any such initiatives.  

One initiative that is expected to be particularly relevant for EDIHs is the ongoing establishment of AI 
Factories under the umbrella of the EuroHPC JU initiative.16 These AI Factories, that were not estab-
lished yet at the time of interviews, are developing cutting-edge generative AI models by operating 
AI-dedicated supercomputers. Nearly all respondents are aware of the broader EuroHPC JU (15 out 
of 16), and about 40% also knew about the AI Factories initiative.17 However, only a third of respond-
ents consider that they have clients that might need such infrastructures, while 37% consider them 
irrelevant to their SMEs customers that have small datasets and data processing needs and are at 
lower levels of digitalisation. Some also do not consider it critical to their own activities, referring to 
the existence of other EDIHs in their country focusing more on model or product development com-
pared to themselves. Nevertheless, more than half of them indicate that, if need  arises, they  can 

15 The question was introduced after the pilot group and therefore asked to 9 hubs only.  
16 The European High-Performance Computing Joint Undertaking (EuroHPC JU) was launched in November 2018 and con-
sists of pooling HPC capacities are the European level. AI factories are recently launched sub-component of this un-
dertaking.  

17 It is worth noting that the question statement was long and led respondents to confuse the Euro HPC initiative with the 

specific AI factories subcomponent. 

18 

 
 
 
 
 
connect the customer with an HPC facility (and by extension with an AI Factory once established). One 
respondent indicated that they applied to the call for AI Factories that was just published at the time 
of the interview. 

3.4  Increasing awareness of AI opportunities and training the workforce 

The fourth and last capability that we assess in to increase awareness and understanding of AI, as 
well as training the workforce on AI skills, while considering the environmental and social impacts of 
AI and improving customers’ understanding of AI-related regulations and compliance during imple-
mentation. 

In Figure 2, we noted that  81%  of  respondents provide support in AI-specific talent development, 
which sometimes covers the topics of environmental and social impacts of AI. However, a smaller 
proportion of respondents indicated supporting customers in regulation and ethics guidance (62%) 
and supporting other EDIHs build AI capacities (e.g. through “train-the-trainer” sessions; 44%), making 
these two functions the weakest ones in the EDIH Network.  

3.4.1  Sustainability Assessment  

Respondents were generally little knowledgeable about the social and environmental implications of 
AI.18 Those with some knowledge mentioned discussing these topics at the margin during their train-
ing and awareness raising activities. During AI implementation, only 20% of respondents reported to 
support their customers in assessing either the environmental or the social impacts of AI adoption.19 
The support offered by the two EDIHs which replied positively is very early-stage, focusing on meas-
uring patient engagement in the health sector in one instance and the energy cost and waste reduc-
tion to prioritise the most efficient solutions with regards to the environment in the other. They use 
basic tools or methodologies to facilitate this assessment. 

3.4.2  Regulatory expertise and compliance capacity 

In terms of legal and regulatory expertise to support AI adoption among their customers, all but one 
respondent report having some expertise in the matter. As indicated on Figure 10, the legal expertise 
is most often available from one of the EDIH partners (75% of respondents), which can be  

  A professional service or department dealing with practical cases, for instance a technology 
transfer office from the university or an incubator, or a single lawyer (62% of respondents).  

  And/or legal scholars from an academic partner (44% of respondents).  

56% of respondents also or alternatively recur to external legal services (consulting), among which 
19% rely exclusively on this option.  

18 The social and environmental implications of AI are multifaceted and far-reaching. On the social front, AI raises signifi-

cant concerns, including job displacement, perpetuation of biases and discrimination, and erosion of privacy. Mean-
while, the environmental footprint of AI is also a growing concern, with issues such as energy consumption, electronic 
waste, and resource extraction contributing to a complex and interconnected set of challenges that require careful 
consideration and mitigation. 

19 Please note the question 11 was asked to only 9 hubs. 7 of them replied they do not assess the environmental and so-

cial impacts of AI adoption.  

19 

 
 
 
 
Figure 10. Percentage of respondent with legal and regulatory expertise, in-house or through consulting, to 
support AI adoption 

Note: This figure displays the different modalities of legal support that respondents offer to their customers for AI adoption. 
It corresponds to the open-ended question 9 in Annex Table 3. The two main non-exclusive modalities mentioned by 
respondents are in-house legal support and external consulting services. Among in-house services available, two non-
exclusive forms are reported: a professional service dedicated to legal matters, and a department of research scholars 
in a university/research institute.  

Source: JRC calculations based on interviews. 

It is worth noting that if nearly all respondents have access to some legal expertise, only 44% of 
them are offering this expertise as a service to their customers (Figure 2).  

Focusing more specifically on EDIHs’ knowledge of the AI Act,20 about a third of respondents admit 
having a rather basic level of understanding of the legislation, along with another third with an inter-
mediate level, and altogether do not provide any guidance to their customers in developing AI-enabled 
products and services on how to make them compliant with the provisions the AI Act, or in occasional 
instances would provide some guidance but only on clients’ request. The remaining third of respond-
ents is more advanced and generally provides guidance to their clients to comply with the legislation, 
most often through information sessions (webinars, events), but they have not developed and imple-
mented a formal process or checklist to systematise this clients’ guidance. It is also worth noting that 
the AI Act was adopted only a few months prior to the interviews and had not applied yet. Some EDIHs 
mentioned that, at the time of interviews, they were still waiting for clearer compliance procedures 
and rules from governments and at the EU level.21  

In the meantime, the European Commission has launched initiatives such as the Code of Practice on 
General-Purpose AI,22 guidelines and the launch of the AI Act Service Desk23 that deliver clarity re-
garding  the  applicable  rules  of  the  AI  Act  and  support  for  their  application.  The  Commission  also 
developed an AI innovation training package for EDIHs that helps them have a better understanding 
of the AI Act and the innovation ecosystem.  

20 The AI Act was adopted on June 13, 2024, a few months before the interviews, entered into force 20 days later and will 
be applied two years later. See Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 
2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). 

21 Question 10 in Annex Table 3.   
22 https://digital-strategy.ec.europa.eu/en/policies/contents-code-gpai 
23 https://ai-act-service-desk.ec.europa.eu 

20 

 
 
 
 
 
4  Conclusion and policy recommendations 

In this report,  we  conducted 16  interviews with EDIHs, complemented with EDIH-reported  data on 
their activities, to understand the capabilities that exist in the EDIH Network with regards to supporting 
AI uptake across Europe. While most AI-related functions are available, the EDIH Network shows a 
relative weakness in AI regulation and ethics’ guidance and capacity building for other EDIHs.  

EDIHs identify and address a number of challenges that their customers face in adopting AI. The main 
barriers that EDIH customers face are resource constraints and the lack of AI literacy. EDIHs actively 
support  them  with  awareness  raising  sessions  and  trainings  and  mentoring  to  improve their  data 
collection processes or brokering to outside providers, while a few EDIHs also developed a dedicated 
AI maturity assessment tool to assess customers’ readiness for AI implementation and establish a 
suitable roadmap.  

The EDIHs interviewed displayed a greater heterogeneity in helping customers implement AI solutions. 
Established branches and applications are more frequently offered among respondents, such as ma-
chine learning and data analytics, than frontier ones, such as expert systems and articulated robotics, 
for instance. They support implementation mainly in the manufacturing and processing, health care 
and energy sectors, and rely rarely on their own or private datasets for AI model training, while not 
perceiving themselves are playing a leading role as trusted data intermediaries.  

EDIHs leverage the AI ecosystem to serve their customers’ needs, although to different extents de-
pending on the type of ecosystem actor. They are well connected to digital providers, AI experts and 
testing and experimentation facilities, but less well connected to AI regulatory sandbox and AI frame-
work providers, or AI standardisation bodies. They support customers in access to finance, although 
the funding is generally not specific to AI. EDIHs are sometimes aware of other AI initiatives in their 
local ecosystem but they were little knowledgeable of the AI Factories component of the EuroHPC JU 
at  the  time  of  the  interviews.  However,  only  a  third  of  them  could  perceived  the  relevance  of  AI 
Factories for their customers. 

Finally, EDIHs actively work towards increasing awareness of AI opportunities and training the work-
force in AI knowledge and tools, with some caveats when it comes to assessing the environmental 
and  social  impacts  of  AI  or  providing  regulation  and  ethics’  guidance  to  customers.  They  show  a 
genuine interest in developing further this competency soon and many indicated a need to implement 
systematic procedures to check compliance with the AI Act during implementation. 

Looking forward, EDIHs are generally expecting AI to play an increasing role in their service delivery. 
56%  of  them  indicated  intentions  to  increase  their  regulation  and  ethics’  guidance  offer  through 
training their lawyers and providing services of legal counselling on how to be compliant with the AI 
Act. 44% also intend to offer more workshops and training on AI, and 31% are considering acquiring 
an AI assessment tool to better guide their clients (see Annex Figure 11).  

AI is deemed to play an increasing role in EDIHs’ activities, as they were already expecting at the time 
of interviews in view of the evolution of the technology and demands from their customers, but also 
with the priority set by the Commission. Indeed, the second generation of the EDIH Network will have 

21 

 
 
a reinforced focus on AI,24 and serving as ambassadors of European AI solutions’ providers and priv-
ileged access point to the AI innovation ecosystem.25 In that sense, some actions beyond those al-
ready planned and undertaken, as mentioned in the previous section, can be suggested to support 
EDIHs in their mission to increase AI uptake in Europe:  

  Encourage the EDIH Network to continue sharing use cases, as showcasing can build SME’s 

confidence in the benefit of AI and drive engagement.   

  Use  the  EDIH  platform  to  provide  more  AI-dedicated  information  and  training  sessions  to 

EDIHs on relevant initiatives and how to leverage them effectively.  

  Establish a centralised helpdesk to strengthen legal expertise within the EDIH Network and 

help ensure compliance with regulations such as the AI Act. 

  Encourage EDIHs to federate SMEs, possibly taking a sector-based approach, to build up a 
critical mass of data and computational needs before turning to an AI Factory. This would 
enable EDIHs to better understand the relevance of AI Factories / Gigafactories for their cus-
tomers, and they may be more willing to take on the role of trusted data intermediaries.  

  Finally, create a centralised AI resources directory for the Network considering the limited use 
of data beyond public and client ones, mapping in particular data available for AI model train-
ing, along with tools, frameworks, and standards, so EDIHs can access them more efficiently. 

24 EU Funding & Tenders Portal | EU Funding & Tenders Portal 
25 Apply AI Strategy, European Commission, 2025. 

22 

 
 
 
 
 
 
References 

Carpentier, E., D'adda, D., Nepelski, D., & and Stake, J. (2025). European Digital Innovation Hubs 
Network's activities and customers: State of play report 2024. Luxembourg: Publications 
Office of the European Union. doi:https://data.europa.eu/doi/10.2760/7784020 

De Nigris, S., Kalpaka, A., & and Nepelski, D. (2023). Characteristics and regional coverage of the 

European digital innovation hubs network. Publications Office of the European Union. 
doi:https://data.europa.eu/doi/10.2760/590526 

European Commission: Directorate-General for Communication and Leyen, U. v. d. (2024). Europe’s 

choice – Political guidelines for the next European Commission 2024−2029. Publications 
Office of the European Union. doi:https://data.europa.eu/doi/10.2775/260104 

Kalpaka, A., Rissola, G., De Nigris, S., & Nepelski, D. (2023). Digital Maturity Assessment (DMA) 

Framework & Questionnaires for SMEs/PSOs A guidance document for EDIHs. Seville, Spain: 
European Commission, JRC133234. 

23 

 
 
 
 
 
List of abbreviations and definitions 

Abbreviations 

Definitions 

AI 

DG CNECT 

DMAT 

EDIH 

Artificial Intelligence 

Directorate-General for Communications Networks, Content 
and Technology of the European Commission 

Digital Maturity Assessment tool 

European Digital Innovation Hubs 

EuroHPC JU 

European High-Performance Computing Joint Undertaking 

HPC 

High-Performance Computing  

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures  

Figure 1. Geographical location of interviewed and non-interviewed EDIHs ..................................................... 8 

Figure 2. Percentage of respondents supporting their clients in 8 AI-related functions ......................... 10 

Figure 3. Percentage of respondents citing common barriers their clients face when adopting AI .. 11 

Figure 4. Top 6 sectors in which respondents have delivered AI services ....................................................... 15 

Figure 5. Percentage of respondents that used different branches of AI technologies in services 
delivered ................................................................................................................................................................................................... 15 

Figure 6. Percentage of respondents currently using different AI applications to support clients’ AI 
adoption ................................................................................................................................................................................................... 16 

Figure 7. Percentage of respondents using different sources of data in AI applications with their 
clients......................................................................................................................................................................................................... 16 

Figure 8. Percentage of respondents with some ability to perform different functions of a trusted 
data intermediary ............................................................................................................................................................................... 17 

Figure 9. Percentage of respondents who know different types of actors in their ecosystem ........... 18 

Figure 10. Percentage of respondent with legal and regulatory expertise, in-house or through 
consulting, to support AI adoption ............................................................................................................................................. 20 

Figure 11. Percentage of respondents aiming at developing or improving different AI-related 
functions in the near future .......................................................................................................................................................... 30 

25 

 
 
 
List of tables 

Table 1. Definition of AI-relevant capabilities of EDIHs ................................................................................................. 5 

Table 2. Average number and share of AI services among interviewees and in the rest of the EDIH 
Network ....................................................................................................................................................................................................... 9 

Table 3. List of questions asked during the interview ................................................................................................. 27 

Table 4. List of questions in the pre-interview survey................................................................................................. 29 

26 

 
 
 
Annex  

Table 3. List of questions asked during the interview 

Question 1. Would you like to describe briefly a few use cases that best illustrate your customer support in 
AI?  

Question 2. What role does AI play in your current service delivery, and how do you envision it evolving? 

Question 3. In which main sectors have you delivered AI services? 

Question 4. Could you please elaborate on the branches of AI technologies that you have used in the ser-
vices reported in the system? (e.g., machine learning, natural language processing, computer vision) 

Question 5. What AI applications and related technologies do you currently use to support AI adoption by 
your clients? (e.g. Facial recognition, predictive analytics) 

Question 6. What are the primary sources of data used in AI applications with your clients? (e.g., client-pro-
vided data, EDIH-collected data, public datasets, private datasets, etc.) 

Question 7. Trusted data intermediaries are expected to provide a trusted and secure environment in which 
companies or individuals can share data for testing and developing of AI innovations. What is the level of 
your EDIH capability to collect industrial data from companies, anonymise it and then make it available to 
other companies for AI model testing? 

Question 8. Can you name and describe which members of your EDIH you can rely on to address an AI-
related request? Is any of these partners led by a woman?  

Question 9. What legal and regulatory expertise do you have in-house or through consulting to support AI 
adoption, and how do you stay updated on relevant regulations? (e.g., advice on AI Act, data protection, in-
tellectual property, contracts)  

Question 10. How do they assess your knowledge of the AI Act? Do you actively guide your clients develop-
ing AI-enabled products and services on how to make them compliant with the provisions of the AI Act? 

Question 11. Do you offer support to your customers in assessing the potential environmental and social 
impacts of AI adoption, and if so, what tools or methodologies do you use to facilitate this assessment? 

Question 12. What are the most common barriers your clients face when adopting AI, and how do you ad-
dress them?  

Question 13. Do you have an AI maturity assessment tool to identify the AI readiness of your customer and 
opportunities for growth? If not, have you considered developing or acquiring one?  

Question 14. Do you know in your ecosystem who to address for: 

• 
• 
• 
• 
• 
• 

Companies that could take over the client in their digital journey 
AI experts to rely on 
AI frameworks 
A regulatory sandbox for AI 
AI testing & exp. facility  
AI standardisation 

Please give some example(s) of organisations for each category.  

Question 15. Are you aware of any other initiatives around AI in your ecosystem? If yes, please describe 
them briefly and explain how they differ from and interact with the work of your EDIH. 

27 

 
 
Question 16. Capitalising on the supercomputing capacity of the EuroHPC Joint Undertaking, AI Factories 
will develop cutting-edge generative AI models by operating AI-dedicated supercomputers.  

•  Are you familiar with AI Factories?  
•  Would you have clients needing access to such infrastructure to develop and test fast machine 

learning and training of large General Purpose AI (GPAI) models?  

•  Could your EDIH already provide such services? 

Question 17. What motivates your interest in delivering AI services, and what benefits do you hope to 
achieve for your clients? 

Question 18. How does your EDIH support clients in the following AI functions:  

•  AI adoption road mapping,  

• 

• 

• 

• 

• 

• 

• 

solution deployment support,  

support to find investment for AI,  

innovation ecosystem & networking,  

test before invest & solution screening,  

talent development,  

regulation and ethics guidance, and  

capacity building for EDIHs?  

How do your services align with your clients' needs in each of these areas? 

Question 19. Which of the previous functions do you aim to develop or improve in the near future, and why 
are they important to your clients?  

Question 20. What specific step do you plan to take to strengthen your AI capabilities and support your cli-
ents’ AI adoption?  

Question 21. Are you interested in becoming an AI EDIH and supporting others EDIHs on AI-related tasks? If 
so, what specific contribution do you envision making to the network? 

Note: Questions 1, 3, 11 and 15 were introduced later in the data collection process so only 9 hubs answered them.  

Source: JRC elaboration. 

28 

 
 
 
 
Table 4. List of questions in the pre-interview survey 

3. In which main sectors have you delivered AI services? 

☐ Aeronautics 
☐ Agricultural biotechnology and food biotechnology 
☐ Automotive 
☐ Community-Led Local Development 
☐ Construction & Assembly 
☐ Consumer products 
☐ Cultural and creative economy 
☐ Defence 
☐ Education 
☐ Electricity 
☐ Energy 
☐ Energy, fuels and petroleum engineering 
☐ Environment 
☐ Financial 
☐ Fishery 
☐ Food and beverages 
☐ Health care 
☐ Leather 
☐ Life sciences 
☐ Manufacturing and processing 

☐ Maritime 
☐ Metal working and industrial production 
☐ Mining and extraction 
☐ NMP Non-Metallic Materials & basic pro-
cesses 
☐ Nuclear 
☐ Paper and wood 
☐ Personal services 
☐ Polymers and plastics 
☐ Public administration 
☐ Real estate 
☐ Retail, wholesale or distribution 
☐ Security 
☐ Space 
☐ Telecommunications 
☐ Textiles 
☐ Tobacco 
☐ Transport & Mobility 
☐ Transport sector 
☐ Travel and tourism 

4. Could you please elaborate on the branches of AI technologies that you have used in the services re-
ported in the system? 

☐ Machine learning (ML), including generative AI 
☐ Expert systems 
☐ Robotics 
☐ Natural language processing (NLP) 

☐ Computer vision 
☐ Knowledge representation  
☐ Fuzzy logic 
☐ Human-Computer Interaction (HCI) 

5. What AI applications and related technologies do you currently use to support AI adoption by your cli-
ents?  

☐ Image recognition and classification 
☐ Image segmentation 
☐ Content based image retrieval 
☐ Object detection and/or tracking 
☐ Facial recognition 
☐ Text generation 
☐ Image generation 
☐ Speech recognition 
☐ Audio generation 
☐ Video generation 
☐ Large language models (LLMs) 
☐ Chat bots and virtual assistants  
☐ Text classification  
☐ Sentiment analysis 
☐ Data curation 

☐ Predictive analytics 
☐ Recommendation systems 
☐ Data analytics 
☐ Automated stack trading 
☐ Autonomous vehicles 
☐ Articulated robotics 
☐ Collaborative robotics 
☐ Humanoid robotics 
☐ Autonomous robotics 
☐ Fraud detection 
☐ Predictive policing 
☐ Edge AI 
☐ Cloud AI 
☐ Data centres 

29 

 
 
 
  
  
14. In your ecosystem, do you know an organization that provides the following AI-related services? If yes, 
please provide specific examples. 

Service 
Digital transformation partners 
AI expertise 
AI framework providers 
Regulatory sandbox providers 
AI testing and experimentation facilities 
AI standardization bodies 

Answer 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 

Example(s) 

18. Does your EDIH provide support to clients in the following AI functions? If yes, please provide specific 
examples.  
Function 
AI adoption road mapping 
Solution deployment support 
Support to find investment for AI 
Innovation ecosystem & networking 
Test before invest & solution screening 
Talent development 
Regulation and ethics guidance 
Capacity building for EDIHs 

Needed by clients? 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 

Answer 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 
☐ Yes    ☐  No 

Example(s) 

Note: This survey was introduced later in the data collection process and only 9 hubs received it prior to the interview. The 

other interviewees received it after the interview.   

Source: JRC elaboration. 

Figure 11. Percentage of respondents aiming at developing or improving different AI-related functions in the 
near future 

Note: This figure displays the percentage of EDIHs which mentioned intentions to develop further each of the AI-related 

function. The near future refers to the upcoming three years. It corresponds to question 19 of Annex Table 3.  

Source: JRC calculations based on interviews. 

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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