Artificial or human intelligence?
Date: 2019-06-30
Institutions: Cedefop
Subjects: artificial intelligence, automation, digital literacy, digitisation, impact of information technology, labour market, new type of employment, online platform worker, professional qualifications, skill obsolescence, technological change

BRIEFING NOTE

ARTIFICIAL OR HUMAN INTELLIGENCE?

Digitalisation and the future  
of jobs and skills: opportunities and risks

Did you know that artificial intelligence-based meth-
ods of image recognition – such as scanning X-rays 
for  evidence  of  cancer  or  other  diseases  –  have  re-
duced the scope of error from 29% to less than 3% 
in the past seven years? Can you imagine how much 
such  predictive  capabilities  would  allow  a  physician 
to improve efficiency in his/her diagnoses? 

THE FOURTH INDUSTRIAL  
REVOLUTION
Most of us will recently have come across some man-
ifestation of AI applications, be it virtual assistants on 
our  smartphones,  speaking  to  chatbots,  translating 
foreign  languages  online,  or  unknowingly  being  tar-
geted by specific adverts and media content (1). The 
physician using the novel image recognition machine 
will recently have learned how to use it or, more pre-
cisely, how to interact with it, each playing their part 
in the diagnosis.

FIGURE 1.  POLICIES FOR DIGITAL AND OTHER KEY SKILLS IN THE  

  EU-28+

In  fact,  the  4th  Industrial  Revolution  (or  Industry 
4.0) is here, and already disrupting the world of work. 
Cedefop’s first European skills and jobs survey (ESJS) 
found that 43% of adult workers across the EU saw 
the  technologies  they  use  change  in  the  past  five 
years, while 47% saw changes in their working meth-
ods or practices. With forecasts predicting that nearly 
half of all jobs in advanced economies may potentially 
be  automated,  it  is  no  surprise  that  a  2017  Euroba-
rometer survey revealed that 72% of EU citizens fear 
that robots and AI may ‘steal people’s jobs’.

However, many scientists see fears of robots and 
machines  breeding  a  jobless  future  as  exaggerated, 
arguing  that  previous  industrial  revolutions  caused 
this kind of ‘alarmism’ too. While no prediction about 
the future can be definitive, it seems reasonable to ex-
pect that both work and learning will increasingly be 
shaped by automation and AI applications in a wide 
range  of  industries.  This  includes  education,  health 
care, transport and manufacturing.

(1)  The Oxford Dictionary defines artificial intelligence (AI) as the 
theory and development of computer systems able to perform 
tasks  normally  requiring  human  intelligence,  such  as  visual 
perception,  speech  recognition,  decision-making,  and  trans-
lation between languages (2017). AI may affect economy and 
society by being a general purpose technology, lowering the 
cost and increasing the predictive capability of decision-mak-
ers in complex, unstructured environments.

In  the  past  four  years  (2015-18),  most  EU  Member 
State  training  policy  initiatives  were  geared  towards 
providing  people  in  initial  and  continuous  VET  with 
digital  skills.  Such  policies  had  the  highest  rate  of 
full-scale implementation and government regulation. 

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DRIVERS OF CHANGE
This time is different
There are several reasons why current technological 
progress differs from the past. Innovation cycles are 
faster. Firms can engage in rapid product prototyping 
and marketing. Many organisations are less depend-
ent  on  a  core  workforce,  as  they  can  draw  on  the 
power of the crowd and online (platform) labour. The 
digital world also allows for fast upscaling of ‘digital 
innovators’ in winner-take-all markets.

New  technologies  have  usually  translated  into 
cheaper and better products, creating higher demand 
from  consumers  and  more  jobs.  However,  in  recent 
decades the link between higher productivity and la-
bour’s  income  share  has  been  severed  in  most  EU 
countries.  This  time  around,  technological  progress 
may exacerbate income inequalities. 

The demographic crunch also threatens societies’ 
ability to adapt to the changing skill demands of fu-
ture  labour  markets;  for  instance,  it  is  usually  more 
difficult for mid-career workers to upskill and change 
jobs than for their younger counterparts.

And while past technological breakthroughs tend-
ed  to  replace  low-skilled,  routine  work,  today  many 
high-skilled  tasks,  including  in  the  health,  legal,  fi-
nance  and  education  industries,  can  be  performed 
faster and better by machines than humans.

How technology affects labour markets
Despite  these  differences  from  previous  industrial 
transformations,  evidence  to  date  on  the  impact  of 
robots on employment is mixed. Some studies point 
to a positive (or neutral) net employment balance as-
sociated with technology (especially research and de-
velopment and product innovation) and some forms 
of automation. While US research has found a signifi-
cant negative effect of robot adoption, evidence from 
Germany  and  other  advanced  economies  points  to 
positive job creation spillovers across industries, al-
beit with distributional consequences for wages and 
work hours for workers of different age and skill level.
Of  most  concern,  however,  is  that  job  creation 
through innovation seems to have run out of steam: 
instead of creating new jobs or tasks, commercial in-
novations using AI, such as industrial robots, are in-
creasingly displacing labour.

IMPACT OF AUTOMATION 
Obsolete jobs and skills, changing tasks
Building on ESJS data, recent research suggests that 
only 14% of EU jobs face a high risk of automation, 
with most related tasks substituted by machine learn-
ing  algorithms  (Figure  2).  This  includes  assemblers, 
operators of stationary plant and machines, electrical 

and  electronic  trades  workers,  but  also  drivers  and 
mobile plant operators. For approximately 18 million 
EU workers (8% of jobs), according to ESJS data, the 
risk is severe, as their employers fail to provide com-
pensatory training, further exacerbating their vulner-
ability.

FIGURE 2.  SHARE OF EU JOBS AT RISK OF AUTOMATION

NB: Automation risk classes defined as: High risk = 70%; Transformation = 
between 50-70%; Some adjustment = 30-50%; Little change = automation 
risk < 30%
Source:  Pouliakas (2018). 

However,  AI  and  automation  do  not  necessarily 
destroy  jobs,  but  transform  them:  for  four  in  10  EU 
jobs some tasks will be automated, demanding new 
skill  needs  to  complement  AI  technologies.  ESJS 
analysis shows that the jobs most likely to be trans-
formed by automation typically rely on routine tasks. 
Jobs  demanding  worker  autonomy,  planning,  team-
work, communication and customer-service skills are 
expected to resist automation better. 

FIGURE 3. JOB SKILLS/TASKS PRONE TO AUTOMATION

NB: 4Cs: communication, cooperation, creativity, critical thinking.
Source: Pouliakas (2018).

Polarisation and automation
Workers in jobs with high automation probability suf-
fer significant negative labour market consequences. 
Many fear they will lose their jobs (job separation); this 
impacts on their job satisfaction. Employees in high-
ly  automatable  jobs,  predominantly  lower-educated 
workers, also receive about 3.5% lower hourly wages 
than those with similar sets of skills in lower-risk jobs. 

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The impact of automation is generally felt unequal-
ly in different segments of society. People with gaps 
in digital and transversal skills, working in medium- or 
low-skill occupations, are prone to a higher automa-
tion risk than their better educated peers. The same 
holds for workers in elementary occupations, crafts/
trades, and plant and machine operators. In contrast, 
this risk is lower for managers and professionals and 
those employed in social and personal services, edu-
cation, health and cultural industries (Figure 4). 

FIGURE 4.  PERCENTAGE OF EU WORKERS AT HIGH RISK OF  

  AUTOMATION, BY INDUSTRY

NEW FORMS OF WORK – NEW 
WAYS TO LEARN
Technological  change  and  digitalisation  not  only  af-
fect what work new jobs entail and what people will 
have to learn, but also how people will work and learn, 
both at their workplace and outside. 

Cedefop’s CrowdLearn study: sneak  
preview
To gather evidence on a lesser known aspect of dig-
italisation  –  the  growing  number  of  people  earning 
some  or  all  of  their  income  from  work  mediated  by 
online labour platforms – Cedefop has been looking 
at the skill needs and learning practices of so-called 
crowd-workers.  While  crowd-work  currently  repre-
sents only a small part of the overall European labour 
market (with estimates ranging from 2% to 11% of EU 
adults), it is one of the most debated (global) labour 
market trends. 

BOX 1.   CEDEFOP’S CROWDLEARN STUDY 

Source: Pouliakas (2018).

TECHNOLOGICAL SKILLS  
OBSOLESCENCE AND TRAINING
For lower-skilled workers the introduction of automat-
ing technologies at work may require a long period of 
hardship and adaptation until they acquire new skills 
to cope with new tasks or find a new job.

But technological change, and the skills obsoles-
cence it brings, affects all workers; on average 16% of 
EU workers – 28% in Estonia – fear that digitalisation 
will render their skills outdated. Higher-qualified work-
ers faced with technological skills obsolescence often 
express job dissatisfaction and fear of job insecurity. 
However,  analysis  based  on  ESJS  data  reveals,that 
they are more likely to undergo upskilling and on-the-
job learning to cope with new tasks and skills com-
plexity than workers whose jobs have not been affect-
ed by changing technologies. There is little evidence 
that  technological  advances  inevitably  cause  adult 
worker deskilling. In any case, policy-makers should 
be wary of the fact that advancing technologies rein-
force higher skill demand and training, widening the 
digital  divide  and  putting  low-skilled  workers  at  fur-
ther disadvantage.

the 

into 

following 

Cedefop’s  CrowdLearn  study,  carried  out  be-
tween  January  2018  and  December  2019, 
research  questions: 
looks 
What skills do crowd-workers develop through their 
work  and  with  what  learning  processes,  individual 
and social? Are there differences in learning and skill 
development practices between crowd-workers? Do 
platform markets promote effective skill development 
and utilisation of crowd-workers’ skills? What about 
recognition/validation and portability of crowd-work-
ers’ skills and credentials? What policies can improve 
skill  development  and  matching  of  crowd-workers? 
Final results of the study will be published in early 2020. 

Source:  www.cedefop.europa.eu/en/events-and-projects/projects/

digitalisation-and-future-work 

Cedefop’s  CrowdLearn  study  addresses  these 
questions via a combination of qualitative and quan-
titative research. Although still in progress, the study 
has  revealed  a  first  typology  of  the  most  typical 
skills  developed  by  individuals  through  crowd-work  
(Table 1).

The  findings  show  that  individuals’  skill  forma-
tion  in  crowd-working  is  biased  towards  soft  and 
entrepreneurial  skills.  Crowd-workers  tend  to  adopt 
self-regulated  learning  strategies,  mostly  using  fast- 
paced  and  short  online  learning  modules,  with  little 
support  provided  by  platforms  or  platform  clients. 
Skills matching is also different: compared to stand-
ard labour markets, where publicly regulated qualifi-
cation  systems  play  an  important  role,  in  the  online 
labour market, platforms’ proprietary data and match-
ing algorithms dominate.

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TABLE 1.  SKILLS DEVELOPED THROUGH PLATFORM WORK:   

  EVIDENCE FROM INTERVIEWS WITH CROWD-WORKERS

Technical skills
computer programming, 
marketing, search engine 
optimisation, geographic 
information systems

Language skills
English, German, Spanish

Setting up as freelancer
obtaining business permits, taxes, 
visas

Organisational skills
Project and/or time management, 
being organised

Obtaining platform work
applying/marketing/pricing 
own work using the  
platform, pitching, who to 
trust, self-presentation

Communication skills
communication, handling cultural  
differences, handling customers,  
community-building offline,  
teamwork

Learning to learn

Personal dispositions/attributes
confidence, independence,  
resilience, punctuality, risk  
tolerance, discipline, working 
alone, creativity, empathy, flexibility

Source: Cedefop’s CrowdLearn project (interim report).

BOX 2.   CEDEFOP’S WORK ON THE FUTURE OF JOBS AND SKILLS

To  understand  better  the  risks  and  opportunities  of 
current technological advances, Cedefop has recent-
ly set up its new Digitalisation and future of work ac-
tivity. A key output so far is the recent launch of the 
Second  European  skills  and  jobs  survey,  which  will 
collect new data and provide unique insights into the 
impact of digitalisation and automation on EU work-
ers’  jobs  and  skills.  Cedefop  is  also  exploring  how 
AI  technologies  may  enable  better  and  faster  skills 
anticipation to inform VET policies: examples are EU 
anticipatory  policies  for  matching  skills  and  Cede-
fop’s  real-time  skill  needs  analysis  based  on  online 
job adverts. 

DOES VET OFFER ANSWERS?
To allow for successful integration of new digital pro-
cesses,  people,  businesses  and  labour  markets  will 
have to adapt and fundamentally change the way they 
work.  To  cooperate  with  machines,  workers  across 
the  board  –  from  doctors  to  construction  workers  – 
will need to acquire new skills. Education and training 
will have to respond to people’s upskilling and reskill-
ing needs; it falls to policy-makers to frame this vast 
ongoing transformation to ensure nobody is left be-
hind. 

New learning patterns and environments in digital 
labour markets also caution that  the  standard Euro-
pean skills matching and upskilling policy toolkit – in-
cluding formal skills validation processes – will have 
to be responsive to emerging skill needs, for instance 
by integrating and safeguarding quality assurance of 
new digital and sector-specific credentials.

It is of critical importance that countries invest in 
‘robot-proof’ lifelong learning for all. At the same time, 

individuals  need  to  manage  their  learning:  how  and 
when to renew their skillsets with up-to-date skills. To 
do so, they need strong foundations from initial edu-
cation or training and companies promoting continu-
ing experiential learning.

Sound  digital  and  technological  literacy  is  key  to 
social and economic participation. More than 80% of 
adult workers in the EU today need a certain level of 
digital competence to perform their jobs (2). However, 
43%  of  them  lack  basic  digital  skills  (3)  and  around 
one third are at risk from digital skills gaps (4). To help 
close  these  gaps,  VET  programmes  across  Europe 
offer both occupational digital skills (such as software 
programmer)  and  digital  skills  as  a  key  competence 
forming part of a wider programme (5).
Robot-compatible  education 

is  not  primarily 
about digital skills but about blending an array of key 
competences  (entrepreneurship,  digital,  STEM,  lan-
guages, learning to learn) into curricula and learning 
methods,  within  comprehensive  VET  programmes 
and  policy  actions.  As  Cedefop’s  analysis  of  online 
job vacancies revealed, the skill most sought after by 
European employers is adaptability to change, men-
tioned in three out of four vacancy notices in a sample 
of over 30 million vacancies (6). Being able to embrace 
change is paramount to thriving in a world where hu-
mans and robots will interact ever more closely.

Briefing note – 9140 EN
Cat. No: TI-BB-19-005-EN-N
ISBN 978-92-896-2798-6, doi:10.2801/862703
Copyright © European Centre for the Development of Vocational Training 
(Cedefop), 2019.
Reproduction is authorised provided the source is acknowledged.

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(2)  Cedefop (2018). Insights into skill shortages and skill mismatch: 
learning  from  Cedefop’s  European  skills  and  jobs  survey,  
Cedefop reference series; No 106.

(3)  European Commission (2018). Human captial: digital inclusion 

and skills. In Digital economy and society index report.

(4)  Cedefop (2016). The great divide: Digitalisation and digital skill 
gaps in the EU workforce, #ESJsurvey Insights No 9, Thessa-
loniki, Greece.

(5)  Preliminary results of a Cedefop study reveal that almost half 
(47%) of initial VET programmes integrate digital skills in sub-
jects rather than offering them as stand-alone subjects.

(6)  See Cedefop briefing note The skills employers want!

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