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POLICY PAPER SERIES

IZA Policy Paper No. 216

Artificial Intelligence and the Future of
Work: Evidence and Policy Guidelines for
Developing Economies
Pablo Egana-delSol
Luis Vargas-Faulbaum

JULY 2025

POLICY PAPER SERIES

IZA Policy Paper No. 216

Artificial Intelligence and the Future of
Work: Evidence and Policy Guidelines for
Developing Economies
Pablo Egana-delSol

Adolfo Ibanez University and IZA

Luis Vargas-Faulbaum
Adolfo Ibanez University

JULY 2025

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may
include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA
Guiding Principles of Research Integrity.
The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the
world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our
time. Our key objective is to build bridges between academic research, policymakers and society.
IZA Policy Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should
account for its provisional character. A revised version may be available directly from the author.

IZA – Institute of Labor Economics
Schaumburg-Lippe-Straße 5–9
53113 Bonn, Germany

Phone: +49-228-3894-0
Email: publications@iza.org

www.iza.org

IZA Policy Paper No. 216

JULY 2025

ABSTRACT
Artificial Intelligence and the Future of
Work: Evidence and Policy Guidelines for
Developing Economies*
This article offers a comprehensive review of Artificial Intelligence’s (AI) effects on global
labour markets, with a particular focus on developing economies. Drawing on an extensive
body of evidence, it demonstrates that AI’s disruptive potential diverges markedly
from earlier waves of automation, extending its reach into occupations once deemed
insulated—especially those demanding advanced education and complex cognitive
abilities. The analysis reveals significant heterogeneity in AI exposure across countries at
different development stages and among workers distinguished by skill sets, educational
attainment, age, and gender, underscoring its unequal distributional consequences. To
harness AI’s benefits while safeguarding vulnerable groups, we propose four strategic
policy levers: bolstering digital infrastructure, expanding vocational training and lifelong
upskilling programmes, formalising labour markets, and integrating AI tools within social
protection delivery. Collectively, these measures foster a human centred adoption of AI,
bridge the digital divide, and promote inclusive growth, thereby mitigating adverse impacts
on employment and wages.
JEL Classification:

J23, J24, J31, O1, O33

Keywords:

artificial intelligence, labour market, inequality, automation,
social protection

Corresponding author:
Pablo Egana-delSol
Universidad Adolfo Ibañez
Av. Diag. Las Torres 2640
7941169 Santiago
Chile
E-mail: pablo.egana@uai.cl

* This paper was prepared as a background paper for the Human Development Report 2025, UNDP. We are grateful
to Raul Fugellie for his assistance as a research assistant, and to Josefin Pasanen, Raiyan Arshad, and Heriberto Tapia
from the UNDP team for their invaluable feedback.

Table of Contents

I.

Introduction

2

II.

Literature review

7

III.

Methods

16

IV.

Results

19

A.

Differences between countries

19

B.

Exposed occupations to AI

22

C.

Skills of individuals exposed to Artificial Intelligence

25

D.

Education of individuals exposed to Artificial Intelligence

27

E.

Age of individuals exposed to AI

29

F.

Gender of individuals exposed to AI

30

G.

Inequality

31

H.

Others

32

V.

Policy proposals

33

A.

Workers’ Concerns

34

B.

Access to internet services

36

C.

Social protection

37

D.

Labour market formalisation

40

E.

Vocational training

42

F.

Worldwide actions and initiatives

44

VI.

Conclusions

46

VII.

Bibliography

50

1

I.

Introduction

The development of technology has led to rapid and transformative changes in our
societies. Advancements in healthcare and environmental protection, as well as the
emergence of new ways of working, have significantly impacted various aspects of daily
life. The internet, as a key driver of the creation, diversification, and dissemination of
information, has been an essential resource for the adoption of technology and,
consequently, for the development of nations. For instance, individuals with access to
high-speed internet experience a 13.2% increase in employment opportunities, while
total employment per company rises by up to 22%, and exports per company nearly
quadruple (World Bank 2024). These changes, although disruptive, have been gradually
and unevenly integrated, depending on the material conditions and level of development
of each country. This process generates varying effects and development gaps that can
persist over time, both within countries and between them.
Artificial Intelligence (AI), together with the Fourth Industrial Revolution, poses
significant challenges concerning the social changes it may induce, particularly in terms
of the future of work regarding its organisation and productivity, among other
dimensions. Much like access to the internet, the development, accessibility, and
utilisation of Generative Artificial Intelligence present the possibility that many jobs may
become simpler and quicker to perform, but also some parts of the labour market could
be replaced by this technology. These possibilities, whose magnitude remains unknown,
present challenges that need to be addressed through public policies that ensure
sustainable adoption by facilitating adaptation to it, as well as mitigating adverse
consequences. Therefore, understanding and identifying what these challenges are and
what we currently know about them becomes paramount.

2

This research systematically reviews the existing literature on the impacts of Artificial
Intelligence on labour market outcomes and the broader socio-economic landscape
worldwide. It also identifies potential strategies to mitigate adverse effects, capitalise on
opportunities for inclusive growth, and enhance human development through peoplecentred digital technologies. In doing so, the study proposes policy measures focusing on
formalisation, social protection, and training to address the consequences of digital
transformation, aiming to expand capabilities and promote human development,
particularly among vulnerable groups. Accordingly, the research question addressed in
this paper is: How does exposure to Artificial Intelligence influence labour market
outcomes across both developed and developing economies, and what policy strategies
can effectively mitigate adverse effects while promoting inclusive growth?
To examine the future of work and the challenges arising from the adoption and
widespread use of Artificial Intelligence, we will use the recent automation process—
about which we have more extensive evidence—as a starting point. By doing so, we aim
to identify the potential differences between this new technology and recent automation
efforts, recognising that the full scope and potential of AI remain unclear. However,
emerging evidence suggests that the skill sets and occupations at risk diverge
significantly from those mostly affected by automation, as detailed in this document.
For instance, in the case of automation, evidence indicates that it has impacted and
deepened wage inequality in the United States (Acemoglu and Restrepo 2019), and has
affected skills and abilities such as knowledge of fine arts and several psychomotor
abilities, with subsequent consequences for the labour market in OECD countries
(Lassébie and Quintini 2022). Moreover, findings for developing countries and Latin
America suggest even more pronounced potential impacts (Egana-delSol and Joyce 2020;

3

Egana-delSol et al. 2022). Meanwhile, Autor (2019) argues that certain factors, such as
high levels of skill specialisation and the attainment of advanced higher education, enable
protective or adaptive measures that minimise the impact of automation on these
segments of the labour market. This result aligns with evidence from Latin America
(Egana-delSol et al. 2022; Egana-delSol, Cruz, and Micco 2022).
However, Artificial Intelligence may have an important impact in economies while
amplifying inequalities by increasing capital income and with different effects for
workers according to age and educational background (Acemoglu and Restrepo 2022).
Different results will depend on both, complementarity of occupations and exposure to
artificial intelligence and differ for advanced and emerging economies (Cazzaniga et al.
2024). Historically, part of this differentiated impact has been influenced by institutional
arrangements and power relations that mediate the transmission of benefits from
technological advancements (Acemoglu and Johnson 2024) and by the extent of relevant
public policies.
In this article, we explore how Artificial Intelligence (AI) may affect the future of work.
To this end, we first provide a brief overview of the current understanding of how this
disruptive new technology could impact existing employment and the labour force.
Although there is a significant degree of uncertainty due to the recent emergence of this
technology and the rapid pace of its development, we will demonstrate that the various
efforts to conceptualise, model, and evaluate its future impacts consistently indicate that
these effects will differ according to the country's level of development, workers' skills,
age range, gender, and the sectors in which they are employed.
Given that Artificial Intelligence (AI) and Generative AI have not yet been widely adopted
and remain largely in a pilot stage across various industries, there is still limited empirical
4

evidence of their impact on the labour market beyond specific cases (Brynjolfsson, Li, and
Raymond 2024; Brynjolfsson and Unger 2023). The stage of implementation is even more
nascent in developing economies, where analysis has often focused on estimating the
areas and jobs that will be most affected when this technology becomes widespread
(Gmyrek et al. 2024; Benitez and Parrado 2024). Indeed, to understand the potential
effects of AI on developing economies and the associated public policies required for
optimal deployment, we will first review the existing evidence related to automation
processes. Following this, we will examine studies that estimate the risks of exposure to
AI, particularly those based on task-based and automation models.
These models are particularly valuable as they allow for conceptualising AI as a
technology capable of performing an additional set of tasks beyond those potentially
addressed by automation. However, it is important to emphasise that the mere existence
of a technology's capacity to perform certain tasks is not a sufficient condition for the
replacement of labour by technology. Other factors influence this decision, including
labour conditions, wages, the cost of capital, complementary human capital for the new
technology, and other contextual considerations.
Consequently, the progress achievable through this technology will be determined by
how public policies address the mitigation of these differential effects and guide its
development towards prioritising societal sustainability. Therefore, we will also discuss
possible policies that countries could adopt, both collectively and individually, to harness
this innovation, always bearing in mind that these measures depend on their initial
conditions.
The document is organized as follows. The second section offers a review of the literature.
The third briefly describes the methodology, while the fourth section exposes our main
5

results. The fifth section proposes policy actions to tackle the main challenges that AI
poses in the labor market. The final section concludes.

6

II.

Literature review

In 1950, approximately 55% of Latin America's economically active population was
employed in agriculture. However, forty years later, by 1990, this proportion had
decreased to 26% (Infante and Klein 1991). By 2020, it had further declined to 14%
(CEPAL 2024). The advent and widespread adoption of industrial machinery significantly
reduced the demand for labour in the agricultural sector, prompting a shift of workers
towards other productive sectors, such as services. The automation of many agricultural
tasks resulted in the displacement of a substantial number of workers, yet simultaneously
increased the productivity of those who remained, achieving higher output per hour.
Workers who possessed or acquired the skills and abilities to utilise these machines
benefited from their integration, whereas those who did not—primarily less-educated
and older workers—were disproportionately affected by this technological progress.
Similarly, in the late 20th and early 21st centuries, the integration of computer services
and robots brought about significant changes to routine jobs, both cognitive and physical,
through automation. Acemoglu and Restrepo (2020) point that between 50% and 70% of
the shifts in the wage structure in the United States from 1980 to 2016 can be attributed
to relative wage declines among groups of workers specialising in routine tasks in
industries undergoing rapid automation. In the U.S., they also show that the introduction
of one additional robot per thousand workers reduces the employment-to-population
ratio by 0.2 percentage points and wages by 0.42%. Finally, in Europe, the penetration of
robotics lowered both the relative wages and employment rates of the most exposed
workers between 2006 and 2018 (Doorley et al. 2023). On the one hand, this may have

7

resulted in less exposure of workers to dangerous routines, but it also presented a
challenge for the labour market, where other areas had to absorb part of this unemployed
mass.
While automation has replaced a significant portion of the workforce and affected their
incomes, it also complements certain types of workers, increasing productivity in ways
that lead to higher demand for labour and altering the types of jobs available (Autor
2015). In Germany, the adoption of frontier technologies by firms resulted in a decline in
the proportion of routine jobs, a substantial increase in non-routine cognitive jobs, and a
slight rise in non-routine manual jobs (Arntz et al. 2024).
Continuing with automation, in general, in occupations intensive in routine tasks, the
adoption of ICT, the use of computers and the automation of processes through robotics
has led to the replacement of low-skilled workers. Conversely, this shift has also resulted
in a complementarity between technology and tasks that are abstract, creative, involve
problem-solving, and require coordination, typically performed by highly skilled
workers. As a result, low-skilled workers have reallocated their labour supply to service
occupations, which are difficult to automate due to their heavy reliance on physical
dexterity, flexible interpersonal communication, and direct physical proximity (David H
Autor and Dorn 2013).
The process of automation has produced both winners and losers, leading to labour
market "polarisation" in countries like the United States. In such cases, wage gains have
disproportionately benefited individuals at the top and bottom of the income and skill
distributions, while those in the middle have seen little benefit (David H. Autor 2015).
However, the effects of automation on inequality remain inconclusive. For instance,

8

Doorley and others (2023) found that the impact of automation on income inequality was
limited in many European countries, largely due to fiscal and welfare policies.
While these findings are relatively consistent across developed countries, the situation is
different in developing economies. Molina and Maloney (2016) show that labour market
polarisation has not occurred in developing countries, although early signs of this trend
can be observed in certain nations, such as China. Similarly, Hjort and Poulsen (2019)
found that the introduction of high-speed internet in Africa led to increased employment,
particularly in occupations requiring higher skills, while the impact on less-educated
workers was smaller. In contrast, automation appears to affect informal workers
disproportionately in developing economies. For example, Egana-del Sol, Cruz, and Micco
(2022) demonstrated that the impact of automation on informal employees was three
times greater than on formal employees in Chile after the pandemic. This finding is
particularly significant for countries with high levels of informality, where the unequal
effects of automation may exacerbate existing labour market vulnerabilities.
Finally, regarding gender differences in the levels of impact related to automation,
findings diverge. Unlike Muro et.al (2019) who identify men as being more exposed,
Egana-delSol et al (2022) report that the proportion of women at high risk is 21%,
compared to 19% for men across four Latin American countries.
Despite extensive analyses on automation, various experts agree that the development of
generative Artificial Intelligence (AI) exhibits significant differences from the processes
observed in recent years. Generally, the features distinguishing AI from other automation
technologies are its capacity to substantially expand the range of tasks that can be
automated—extending beyond routine and non-cognitive tasks—and its potential to
impact all occupational sectors, owing to its general-purpose nature (OECD 2023).
9

There is evidence of just incipient use of AI even in developed countries. For instance,
research shows that in the US fewer than 6% of firms used any of the AI-related
technologies (namely automated-guided vehicles, machine learning, machine vision,
natural language processing, and voice recognition) though most very large firms
reported at least some AI use (McElheran et al. 2024). Among dynamic young firms, AI
use was highest alongside more-educated, more-experienced, and younger owners,
including owners motivated by bringing new ideas to market or helping the community.
We can argue that there is a relatively small version of this type of entrepreneur in middle
income countries.
For instance, Webb (2020) indicates that exposure to AI is highest among highly skilled
occupations, suggesting that AI will impact a demographic markedly different from those
affected by software and robots. While low-skilled occupations are more exposed to
robots and medium-skilled occupations to software, it is highly skilled occupations that
face the greatest exposure to artificial intelligence. Furthermore, AI is significantly more
likely to affect workers with higher educational attainment and older age profiles than
these earlier technologies. Similarly, as noted in Cazzaniga et al (2024) unlike previous
waves of automation—which had their strongest effects on medium-skilled workers—
the risks of displacement associated with AI extend to higher-paid workers.
This point is particularly relevant because information technology has traditionally
focused on automating routine tasks, with programmers specifying step-by-step
instructions that can be translated into code. This approach largely affected mid-skilled
jobs, thereby contributing to employment polarisation (Autor, 2015). By contrast, AI
extends its scope beyond routine tasks to encompass more complex activities, which
primarily involves highly skilled workers.

10

Nonetheless, certain studies (e.g. Frey & Osborne, 2017) highlight specific skills—such as
persuasion and social intelligence—as potential bottlenecks to automation. This finding
is broadly consistent with the Lassébie and Quintini (2022) that identifies negotiation,
social perception, care and assistance for others, originality, and persuasion as skills that
remain challenging to automate. Thus, while AI appears to have a greater impact on more
complex activities, it also faces ongoing limitations.
A crucial aspect of analysing both the current and future impact of artificial intelligence
(AI) adoption is to differentiate the channels through which it exerts its influence. Often,
discussions focus on job exposure, suggesting that there is a certain likelihood of AI being
integrated into these tasks; however, this does not necessarily equate to a direct impact
on the exposed jobs. For instance, some roles may be entirely supplanted by AI once it
assumes all the associated tasks. Conversely, there are positions in which AI only takes
responsibility for a subset of tasks, thereby acting as a complement to the human worker
and enhancing productivity. Furthermore, certain roles may undergo transformation due
to AI's influence on specific tasks, leading to a restructuring of either the work itself or
the broader occupational framework. Finally, entirely new roles may emerge in response
to the proliferation of AI, potentially mitigating the unemployment effects precipitated by
automation.
In this context, it is imperative for developing countries to consider the effects of artificial
intelligence, given its potential as a catalyst for economic growth. Demombynes, Langbein
and Weber (2025) conclude that middle-income countries experience a lower degree of
exposure to AI compared to high-income countries, yet a higher degree than that
observed in low-income countries. Thus, there exists a correlation between the level of
exposure and national income levels. This relationship can be attributed, in part, to the

11

differing composition of work across countries. In developing nations, there is a
predominance of manual labour or roles that require personal interaction, which
naturally limits the scope for AI integration. Additionally, inadequate access to essential
infrastructure such as electricity and the internet further constraints exposure,
particularly in low-income countries. The study also reveals notable demographic
disparities. For instance, women are likely to experience higher levels of AI exposure in
high-income and upper-middle-income countries, a trend that is similarly reflected
among older workers. Such age-related differences, however, are not evident in emerging
economies.
When examining the diverse regional scenarios, a closer look at Asia reveals distinct
experiences between emerging economies and low-income countries, with AI impacting
these settings in different ways. Although both economic groups may experience
enhanced productivity, task complementarity is notably more pronounced in emerging
economies, whereas low-income countries risk heightened unemployment. The IMF
(2024) by combining exposure and skills needs methodologies proposed by Felten and
others (2021) and Pizzinelli and others (2023) analises the situation for Asian countries.
Their findings indicate that both the degree of exposure and task complementarity are
higher in advanced Asian economies than in underdeveloped ones, thereby yielding
greater productivity gains from AI. They further reveal that occupations at risk of
displacement are primarily found within services, sales, and administrative support,
while managerial, professional, and certain technical roles tend to be more
complementary to AI. Similarly, it is noted that agricultural workers, tradespeople, and
machinery operators are unlikely to be affected by AI at this stage. Sectoral differences
are also apparent, with education, finance, information technology, health, and

12

administration emerging as the sectors with the highest levels of worker exposure.
Moreover, women face a greater risk of disruption from AI, as they are more likely to
occupy roles in administrative support and services and sales, whereas men are
disproportionately represented in trades, agriculture, and machinery operation.
For the Philippines, approximately one-third of occupations in the country are highly
exposed to AI, although the vast majority are complementary to it (Cucio and Hennig
2025). Nonetheless, the Business Process Outsourcing (BPO) sector faces significant
challenges, as it is one of the most vulnerable to automation. Despite representing a
relatively small proportion of the workforce, the BPO sector contributes substantially to
the nation’s GDP. Consequently, AI could engender profound and complex effects on
economies such as the Philippines, where extensive call centre operations and
subcontracted roles might be supplanted by chatbots.
In contrast, the situation in Africa is markedly different. A substantial portion of its
population is young and resides in rural areas—approximately 50%—with agriculture
employing around 60% of the workforce, and a large segment of the working population
are working in the informal sector. Given that emerging markets with a higher proportion
of agricultural and informal workers tend to have a lower initial exposure to AI, it is
anticipated that generative AI will have a delayed impact on African economies
(Cazzaniga et al. 2024). Consequently, the levels of exposure, task complementarity, and
the resultant productivity gains are likely to be distant, necessitating significant
intervention to retrain and upskill the workforce and to narrow the existing digital divide
(AUDA-NEPAD 2024). Additionally, another challenge faced by the continent is the low
investment in research and development, which further complicates the adoption of this
new technology (Azaroual 2024). Lastly, the AI investment in South Africa has a

13

significant and adverse effect on low-skilled employment, highlighting the need to
consider both the direct and indirect consequences of such policies (Giwa and Ngepah
2024) .
The scholarship for the Latin American case suggests that large language models (LLMs)
would affect less than half of the population, with greater exposure among women,
individuals with higher education, formal employees, and higher-income groups (Azuara
Herrera, Ripani, and Torres Ramirez 2024) . This suggests a potential exacerbation of
labour inequality in the region as a consequence of this technology's adoption. In addition
Gmyrek and others (2024) demonstrate that, for instance, in Brazil and Mexico, workers
in the highest income quintile are at least twice as likely to hold positions that would
benefit from the use of generative AI. Furthermore, when adjustments are made for
technology access, these disparities become even more pronounced.
With regard to job automation, the findings indicate a generally low level of automation;
however, the impact is disproportionately concentrated among women and young
people, a consideration that should be addressed by public policies. Moreover, some
studies offer less optimistic or divergent perspectives. For instance, Bakker and others
(2024) finds that approximately one quarter of jobs in Brazil, Chile, Colombia, Mexico,
and Peru exhibit high exposure to AI yet low task complementarity, rendering them
highly susceptible to automation—as observed in call centres. Conversely, around 20
percent of occupations, such as those in the medical field, display both high exposure and
high task complementarity with AI, potentially resulting in significant productivity gains
without displacing workers.
Finally, numerous studies concur that public policies concerning AI adoption and its
effects on the labour market must account for the existing digital divide, which is typically
14

more pronounced in regions such as Africa and in low-income countries. Ensuring the
provision of essential services, such as reliable electricity, is also imperative. Additionally,
it is advisable to prioritise the use of AI to augment human productivity rather than to
drive automation. Encouraging AI applications that enhance human productivity, rather
than replace workers, can help safeguard livelihoods and promote inclusive economic
growth (Demombynes, Langbein, and Weber 2025). Furthermore, policymakers should
concentrate on educational and training programmes to develop a digitally proficient
workforce capable of harnessing AI technologies—a priority that is particularly relevant
in emerging and developing markets, where relatively few jobs currently offer the
potential for productivity gains through AI (IMF 2024). Finally, increasing investment in
research and development, which remains comparatively lower than in more developed
countries, is essential.

15

III.

Methods

This review employed a systematic search strategy to identify relevant articles published
in preferably English, with an emphasis on those published since 2020. The primary
objective was to explore the relationship between Artificial Intelligence (AI) and its
impact on the labour market preferably in developing countries. However, due to
evidence constraints, much of the available literature originates from developed
economies—a reflection not of a research preference but rather of the greater volume of
studies produced for those regions. To achieve this, keywords and search syntax were
meticulously adapted to meet the specific requirements of each scientific database
consulted.
The search terms were carefully selected to focus on the intersection of AI and
employment dynamics within developing nations. Keywords included combinations such
as "Artificial Intelligence," "AI," "labour market," "employment," "automation," "job
displacement," "developing countries," “future of work,”, “income inequality,” and
"emerging economies." These terms were adjusted according to the specifications of each
academic search engine to optimise search results. Emphasis was placed on terms that
directly relate to how AI technologies influence employment patterns in these countries.
Academic databases and search engines utilized in the selection process included Google
Scholar and Scopus. These platforms were chosen for their extensive coverage of
scholarly publications across multiple disciplines, ensuring a comprehensive capture of
relevant literature.

16

In addition to traditional academic sources, grey literature was incorporated to enrich
the review with diverse perspectives and the most current data. Grey literature sources
comprised evaluations and reports from international organisations such as the United
Nations, Inter-American Development Bank, the World Bank, and the International
Monetary Fund.
The inclusion criteria for selecting documents were as follows: publications must be in
English, focus on AI's impact on the labour market, and pertain specifically to developing
countries within the specified time frame. Studies were excluded if they did not directly
address the interplay between AI and employment or if they focused exclusively on
developed nations. This ensured that the review remained relevant to the targeted
context.
The synthesised data were analysed thematically to identify common patterns, trends,
and gaps in the existing literature. The thematic analysis enabled a structured synthesis
of findings, highlighting how AI influences labour markets in developing countries. This
approach also facilitated the identification of areas requiring further research, thereby
contributing to the advancement of knowledge in this field. In sum, this review aims to
provide a comprehensive overview of the current state of knowledge on this critical
subject.
Nevertheless, the disproportionate representation of data from developed countries
highlights a significant methodological limitation. Preliminary findings indicate that AI is
influencing labour markets globally, affecting both developed and developing economies.
However, the scarcity of publications from developing countries suggests that the full
extent and nuances of AI's impact in these regions may not be fully captured.
Consequently, caution is warranted when extrapolating these findings to developing
17

economies, and further research is essential to achieve a more balanced and globally
representative evidence base.
Future research should aim to bridge this gap by fostering greater inclusion of studies
from under-represented regions. Such efforts are essential for developing a more
comprehensive and globally representative understanding of how AI is reshaping labour
markets, ensuring that policies and strategies are effectively tailored to meet the needs
of diverse economic landscapes.

18

IV.

Results

A.

Differences between countries

Although technological advancements tend to reach different parts of the world swiftly
due to high levels of globalisation, their adoption typically proceeds much more rapidly
in highly developed countries. The scope and pace of digital development contribute to
unequal developmental trajectories among individuals and economies(APEC 2022). This
observation is critical for understanding the impact of Artificial Intelligence (AI) on the
future of work, as the technology will likely affect developed and developing countries in
different ways. Indeed, there are growing concerns that AI could widen existing
inequalities, enabling high-income countries to reap most of the benefits while leaving
low- and middle-income countries behind (United Nations, 2024). Such disparities often
stem from divergent levels of digital infrastructure, combined with sizeable populations
that may lack essential resources—ranging from reliable electricity to water for cooling
large-scale computing facilities.
Attewell (2001) distinguishes between primary and secondary digital divides. Primary
divides refer to discrepancies in access to the internet, while secondary divides concern
variations in how the internet is used. These distinctions are particularly pertinent in
poorer and developing countries, where many individuals lack any internet access.
According to the International Telecommunication Union approximately 63% of the
global population used the internet in 2021. Meanwhile, according to GSMA (2021)
estimates that around three billion individuals in low- and middle-income economies
access the internet via mobile phones, representing one of the primary means of internet
19

connectivity in these regions. Even among those with basic internet access, producing
content or fully engaging in digital economies often requires specialised training,
education, and employment opportunities—gaps that create significant differences in
economic development and individual well-being (APEC 2022). These gaps are most
pronounced among those from low-income countries.
Taken together, wealthier countries are more exposed to potential AI-driven automation
in the labour market, yet they are also better positioned to harness the resultant
productivity gains. In contrast, developing countries may initially experience relatively
limited employment impacts due to insufficient digital infrastructure. However, this very
limitation curtails their ability to capitalise on the productivity improvements enabled by
AI (United Nations 2024).
Furthermore, low- and middle-income countries appear to have a proportion of jobs with
significant potential for AI-driven enhancement that is broadly similar to that of highincome countries (World Bank 2024). The key difference, therefore, is primarily
attributable to the prevailing digital divide. Nevertheless, certain occupations in low- and
middle-income nations remain vulnerable to automation, particularly those outsourced
by developed economies—such as call centre positions in the Philippines and India (Berg
et al. 2023).
A further noteworthy study for Latin America is Egaña-delSol and Bravo-Ortega (2025).2
Drawing on multiple data sources (including the World Bank’s Skills Towards
Employment and Productivity (STEP) survey, the OECD’s Programme for the
International Assessment of Adult Competencies (PIAAC), and the two-digit International

2 The countries considered for LAC were Bolivia, Colombia, Chile, Ecuador, El Salvador, Mexico and Perú.

20

Standard Classification of Occupations (ISCO-08)), the authors apply an expectationmaximisation algorithm (Ibrahim 1990) combined with automation risk estimates
derived from Autor et al. (2003), Frey and Osborne (2017), Felten and others (2021) and
Webb (2020). This methodology identifies the tasks most vulnerable to automation and
calculates the estimated risk for each occupation.
In a nutshell, Egana-delSol and Bravo-Ortega’s (2025) study reveals significant
disparities in AI exposure across countries, gender, education, and age in Latin America
compared to OECD countries, challenging conventional results on AI-based automation’s
labor market effects in developed economies (Felten and others, (2021); and Webb,
(2020)).
Table 1 shows the correlation of skills and/or tasks using two different AI exposure
indices. It is straightforward to see the differences across group of countries (i.e. LAC vs
OECD), gender, level of education and age. For details on the estimations at country level
see Egana-delSol and Bravo-Ortega’s (2025). We will discuss this Table further in the
following subsections.

21

Table 1: Correlation of Skills/Tasks with AI Exposure Using Webb and Felten Indices

0.084***

Webb
Index
(OECD
Males)
0.013*

0.048***

0.050***

0.084***

0.034***

0.035***

0.082***

0.057***

0.070***

0.050***

-0.009

0.043***

0.049***

0.045***

STEM Skills

0.055***

0.055***

0.065***

0.069***

0.035**

0.011

0.023***

0.006

Accounting Skills

0.068***

0.061***

-0.051***

-0.044***

0.007

0.030**

-0.061***

-0.033***

Physical Tasks

-0.082***

-0.052***

-0.036***

-0.031***

-0.091***

-0.042***

-0.093***

-0.045***

Customer Contact

-0.035***

-0.012***

-0.080***

-0.060***

0.046***

0.026

0.042**

0.022***

Self-Organization

-0.006

-0.026**

0.017**

0.002

0.003

-0.010

0.005

-0.025

Readiness to Learn

-0.005

-0.006

0.016**

-0.001

-0.010

0.033**

-0.013

0.008

Autonomy in Work

0.011

0.016

-0.007

-0.051***

0.003

0.030*

0.020

0.054**

Repetitive Tasks

0.004

-0.009

0.012

-0.000

0.007

0.030**

-0.031

-0.033

Critical Thinking

0.017

0.026**

-0.006

-0.001

0.011

0.035**

0.020

0.054**

0.047***

0.008

0.030***

0.021*

0.040***

0.034**

0.026

0.017

Skill/Task

Webb Index
(LAC Males)

Webb Index
(LAC
Females)

Management Skills

0.041***

ICT Skills

Medium-Level
Education
High-Level Education

Webb Index
Felten
Felten
Felten Index
Felten Index
(OECD
Index (LAC Index (LAC
(OECD
(OECD Males)
Females)
Males)
Females)
Females)

0.189***

0.177***

0.048***

0.107***

0.269***

0.193***

0.181***

0.141***

Young Adults (18–25)

-0.061***

0.003

-0.063***

-0.030**

-0.022

-0.008

-0.071***

-0.023

Older Workers (25–40)

-0.028***

0.006

-0.019***

0.001

0.020

-0.008

-0.023

-0.007

Note: Table taken from Egana-delSol and Bravo-Ortega (2025). Statistical significance levels are marked as
follows: ***p < 0.01 (highly significant); **p < 0.05 (moderately significant); *p < 0.10 (weakly significant).

B.

Exposed occupations to AI

In addition to cross-country differences, numerous studies have attempted to estimate
both the number and types of jobs likely to be affected by the use of Artificial Intelligence
(AI). These studies generally adopt task-based models that identify the most common
tasks within particular occupations, determining whether such tasks are (or are not)
linked to AI (Acemoglu and Restrepo 2022). Through these models, two key
considerations emerge: first, whether an occupation or sector is exposed to AI—defined
by whether its tasks could be performed or complemented by this technology—and
second, whether AI is expected to replace human workers, complement them, or remain
in a grey area of uncertainty.

22

Cazzaniga (2024) based on exposure indices such as those introduced by Felten, Raj, and
Seamans (2021) and Pizzinelli and others (2023), they find that in developed economies,
approximately 60% of jobs are exposed to AI, of which half could be negatively affected
while the other half could benefit from AI-related complementarities. They also show that
exposure rates in emerging economies and low-income economies are 40% and 26%,
respectively. Their findings indicate that professionals, managers, and technicians are
most exposed.
A separate study focusing on developing Latin American economies is Gmyrek et al.
(2024), which employs the model proposed by Gmyrek, Berg, and Bescond (2023). The
authors find that between 30% and 40% of employment in Latin America and the
Caribbean (LAC) is in some way exposed to generative AI. On the negative side, the
proportion of jobs susceptible to automation represents 2% to 5% of total employment.
On the positive side, the share of jobs that could benefit from productive transformation
supported by generative AI consistently exceeds the proportion at risk of automation in
all Latin American and Caribbean countries, ranging from 8% to 12% of total
employment. However, the authors emphasise that realising these gains depends on
bridging the digital divide still present in these economies relative to developed
countries.
In terms of occupations with high risk of AI based automation, Egana-delSol and Bravo
(2025) find results a diverse range of occupations for the countries in LAC. For the case
of Mexico, for instance, they estimate that the top 5 occupation in term of AI exposure risk
are science and engineering professionals, Health professionals, Teaching profesionals,
public servants, and ICT professionals. These results are similar across countries.
Moreover, these occupations clearly differ from the more routine, less skillful occupations

23

typically found in the pre-AI automation literature (i.e. (Egana-delSol et al. 2022). In
addition, when looking the occupations with larger number of workers under high risk,
they find that the top 5 occupations are Sales, Plant operators, cleaners and helpers, food
processing, and mining, construction, and manufacturing workers, for the case of Mexico
as well.
Moreover, the same authors reveal for the case of sample of LAC countries that higher AI
exposure is linked to significant employment growth but has an unclear impact on wages.
Using Webb’s methodology, one unit of extra AI exposure corresponds to a 1.29% rise in
employment, while Felten’s measure shows a 0.6% increase—both statistically
significant. However, wage effects remain small and statistically insignificant, suggesting
that AI adoption may drive job creation and task reallocation, but its influence on wage
growth is uncertain.
These findings imply that while AI adoption leads to employment expansion, it does not
necessarily translate into higher wages, possibly due to the relocation of workers to
occupations with higher complementarity with AI, without proportionally increasing
labor income. This highlights the need for targeted policy interventions to ensure that AIdriven employment growth also contributes to increase in wages due to the increase of
labor productivity.
Lastly, Benitez and Parrado (2024), employing a similar approach and developing a new
index of exposure (the Generated Index of Occupational Exposure, or GENOE), use labour
data from the United States and Mexico to show that occupations have a 28% probability
of being potentially affected by AI within the next year, rising to 38% in the next five years
and 44% in the next ten. Extrapolating these findings worldwide, they note that telephone

24

operators, credit authorisers, travel agents, and similar roles are among those most
exposed to this emerging technology.

C.

Skills of individuals exposed to Artificial Intelligence

Focusing on those occupations most exposed to automation through artificial
intelligence, recent evidence indicates that administrative jobs involve the highest
proportion of tasks with medium or high automation potential (Berg et al. 2023). This
category encompasses administrative support roles and positions responsible for typing
and data entry, where over 50% of tasks may be automated (World Bank 2024).
As Table 1 indicates, their findings highlight pronounced gender differences, particularly
in Latin America, where women in high-level managerial and ICT roles face greater AI
exposure than men. This pattern suggests that even skilled female-dominated
occupations remain susceptible to ai-based automation, exacerbating existing gender
inequalities.
The findings indicate that, unlike pre-AI automation—which negatively correlates with
high levels of education and physical skills—AI exposure is positively correlated with
higher levels of education, as well as with ICT and STEM skills, but negatively correlated
with younger workers (aged 18–25). Comparing OECD countries with those in Latin
America and the Caribbean, the effects are found to be larger in the latter. In OECD
countries, but not in LAC, physical skills correlate negatively with AI exposure. In both
regions and for both earlier automation and AI, contact with clients shows a negative
correlation, suggesting that customer-facing tasks might be a bottleneck.
Table 1 shows that physical tasks remain one of the least automatable categories,
showing strong negative correlations with AI exposure in both LAC and OECD. In LAC,
25

men working in physically intensive jobs have an AI exposure of -0.082*, while women
show a similar trend at -0.052***. In OECD countries, the negative correlation is even
stronger for men (-0.036***) and women (-0.031***), indicating that these roles continue
to be less susceptible to automation. These findings suggest that manual labor remains a
bottleneck for AI-driven automation, likely due to the complexity of physical dexterity
and the need for human adaptability in non-repetitive tasks.
Similarly, customer-facing roles appear resistant to AI displacement, as evidenced by
strong negative correlations in both regions. In LAC, customer-facing roles decrease AI
exposure for men (-0.035*) and women (-0.012***), while in OECD countries, the
negative effect is even greater (-0.080*** for men and -0.060*** for women).** These
results indicate that AI has not yet reached the level of sophistication required to fully
replace human interaction in client-facing occupations, reinforcing the importance of soft
skills in labor market resilience.
Moreover, STEM skills are positively correlated with AI exposure in both LAC and OECD,
suggesting that even high-skilled technical roles are increasingly vulnerable to
automation. The results show a consistent AI exposure increase across men and women
in both regions, though the effect is slightly stronger in LAC (0.055***) than in OECD
(0.065*** for men and 0.069*** for women). This pattern aligns with findings that AI
technologies are primarily affecting knowledge-based tasks that require technical
expertise.
ICT skills also increase AI exposure across all groups, but the impact is particularly strong
for women. In LAC, women with high ICT skills face an AI exposure of 0.057*, while in
OECD countries, the effect is similarly significant at 0.050*. These results suggest that

26

highly technical roles, often requiring programming, data analysis, or digital
competencies, are at greater risk of automation regardless of region.
Another striking difference is observed in accounting skills, which exhibit opposite
correlations across regions. In OECD countries, accounting skills reduce AI exposure (0.051*), indicating that these roles might involve more strategic, analytical, or regulatory
aspects that are harder to automate. However, in LAC, accounting skills are strongly
associated with increased AI exposure (0.068*), possibly reflecting that accounting tasks
in the region remain highly routinized and therefore more susceptible to AI-driven
automation.
By contrast, while roles requiring greater levels of knowledge and abstraction also face
some degree of AI exposure, this tends to be partial. As a result, AI is more likely to act as
a productivity enhancer in these occupations, rather than fully automating tasks and
displacing workers (Berg et al. 2023). Lastly, and distinct from earlier automation
processes, agricultural and craft-based jobs are among the least susceptible to
automation, with fewer than 10% of their tasks considered automatable (Banco Mundial
2024).

D.

Education of individuals exposed to Artificial Intelligence

With respect to the educational level of those most exposed to AI, various estimates show
that highly educated workers—generally those with tertiary qualifications—who are
employed in the formal sector and earn relatively higher incomes have greater
opportunities to engage with this technology (Gmyrek et al. 2024). Accordingly,
university-educated workers are also better positioned to transition from jobs at risk of

27

displacement to roles that exhibit strong complementarities with AI (Cazzaniga et al.
2024).
Egana-delSol and Bravo-Ortega’s (2025) also finds that, contrary to traditional economic
models, higher educational attainment in Latin America correlates with increased AI
exposure, likely due to sectoral dynamics that make high-skilled roles more vulnerable to
AI-based automation. This calls into question the assumption that education alone shields
workers from technological disruption, emphasizing the need for curriculum adaptation
and continuous reskilling.
In both LAC and OECD countries, higher education is strongly correlated with AI
exposure. In LAC, men with high education levels experience an AI exposure of 0.189*,
while women face an even higher risk at 0.177*. Similarly, in OECD countries, higher
education significantly increases AI exposure for both men (0.048***) and women
(0.107***).** These findings indicate that highly educated workers are more likely to be
employed in roles that AI can automate, highlighting the need for reskilling programs that
focus on adaptability rather than just educational attainment.
In sum, this contrasts with the earlier wave of automation, which most adversely affected
routine-based skills. In that context, middle-class workers primarily occupied positions
susceptible to software-driven replacement, leading to a shock for this group and
triggering labour market polarisation. By contrast, as AI now affects people with higher
educational attainment—who typically command higher incomes—it is unlikely to yield
the same polarising consequences.
Lastly, when examining the possibility of occupational transitions linked to holding a
tertiary degree, workers without university education are shown to face a substantially

28

higher risk of downward mobility, a trend relatively independent of their current
occupations (Cazzaniga 2024). Their vulnerability to automation is therefore heightened,
as they lack the capacity to move into less exposed roles. This stands in contrast to
workers with university-level education, who tend to follow “upward” career paths and
possess greater potential for occupational retraining.

E.

Age of individuals exposed to AI

Another important factor influencing workers’ exposure to AI—and the potential effects
thereof—is age. Indeed, older workers may be particularly susceptible to AI-driven
transformations (Cazzaniga et al. 2024). This vulnerability may stem, in large part, from
their reduced capacity to adapt to new technologies, increasing their risk of being
replaced. They may also have less flexibility in moving from high-exposure roles to those
involving lower exposure—an issue often more pronounced for older workers with
higher education.
Conversely, for more experienced (and therefore older) individuals in supervisory or
managerial roles, AI can facilitate increased productivity and serve as a valuable
complement in areas where automation poses a comparatively lower threat.
Regarding age group and AI risk, Egana-delSol and Bravo-Ortega (2025) suggest that
younger workers (ages 18–25) are less exposed to AI across all regions, though the effect
is more pronounced in LAC (-0.061*) than in OECD (-0.063*** for men and -0.030** for
women).** This suggests that younger workers may be employed in less structured or
adaptable roles, which are currently less susceptible to AI-driven automation. In contrast,
for older workers (ages 25–40), the correlations are mostly insignificant, though in LAC,
men face a slight negative correlation (-0.028*)**, while in OECD, the effect is minimal.

29

These findings indicate that early-career workers may have a temporary advantage in
avoiding AI disruption but may face increasing risks as they advance into roles with
higher automation potential.
By contrast, younger workers tend to face a higher degree of job automation, particularly
in finance, insurance, and public administration (Gmyrek et al. 2024). Nonetheless, they
often possess greater adaptability, as their stronger familiarity with technology enables
them to develop the requisite skills through training with relative ease.

F.

Gender of individuals exposed to AI

Another important source of inequality in the development and use of Artificial
Intelligence (AI) concerns gender. First, women are more concentrated in sectors that are
particularly susceptible to AI-driven automation, such as finance, insurance, and public
administration (Gmyrek et al., 2024). Within these sectors, women often hold
administrative and office-based positions, rendering them especially vulnerable to AIrelated displacement (Benitez and Parrado, 2024). Second, gender gaps originating in the
education system can also lead to uneven AI outcomes, as women tend to be
underrepresented in STEM fields, which themselves face a high degree of AI exposure
(Egana-delSol and Bravo-Ortega 2025). This imbalance could have negative implications
if AI proves complementary to skills typically taught in STEM disciplines. Lastly, a
significant digital divide remains—particularly in Latin America—where many workers
do not use computers in their jobs (United Nations, 2024). This divide is even wider
among women: in 2019, approximately 55% of men used the internet, compared with
just 48% of women (ITU 2020).

30

In particular, the results from Egaña-delSol and Bravo-Ortega (2025) indicate significant
gender disparities in AI exposure, particularly in Latin America and the Caribbean (LAC).
Women with high management skills face substantially greater AI exposure in LAC
(0.084***) compared to their male counterparts (0.041***). This suggests that femaledominated managerial roles in LAC may involve more structured and routinizable
decision-making processes that AI can automate. In contrast, in OECD countries, the effect
of management skills on AI exposure is more balanced, with both men (0.013) and women
(0.048**) experiencing increased risk, though the impact remains lower than in LAC**.

G.

Inequality

As previously noted, not only are some occupations potentially more susceptible to the
impacts of Artificial Intelligence (AI) than others, but workers’ characteristics also play a
crucial role, revealing disparities that may exacerbate inequality. In particular, the
adoption of AI could intensify income inequalities by disproportionately affecting lowand middle-income workers (Benitez and Parrado 2024), compared with their higherincome counterparts.
Furthermore, nearly half of the occupations that could benefit from AI-driven expansion
are constrained by digital skill deficits, which hinder the realisation of their full potential.
Specifically, 6.24 per cent of the jobs held by women and 6.22 per cent of those held by
men are adversely affected by these deficiencies (Gmyrek et al. 2024). In addition, the
potential complementarity of AI is positively correlated with income, thus contributing
to growing inequality (Cazzaniga et al. 2024).
These disparities manifest not only within individual countries but also across national
boundaries. For instance, the digital divide is a significant gap between developed nations

31

and those of medium or low development. Similarly, the gender gap tends to be more
pronounced in less developed countries. Recent data from the APEC region indicate that
women use the internet less frequently than men in all economies—except the United
States—with the widest gaps recorded in Peru, Indonesia, and Malaysia (OECD 2019; ITU
2020). In this sense, AI has the potential to exacerbate inequality both within and among
countries.

H.

Others

An avenue worth exploring is that while AI affects jobs in developing countries (such as
call-centre roles), it can also have a positive impact by stimulating investment in the
infrastructure necessary for its use—data centres, for instance—or by creating remote
employment opportunities in more advanced economies. Such developments could
capitalise on AI’s ability to overcome language barriers and reduce labour costs. For these
possibilities to materialise, however, countries must provide favourable conditions for
investment and equip their populations with the requisite skills to seize emerging
opportunities.
At the same time, despite existing gaps in technology and resources, AI adoption may
prove even more transformative for poorer countries, precisely because they are further
removed from the technological frontier. By effectively harnessing AI, these countries
could unlock economic growth and accelerate convergence with higher-income
economies.
Finally, both workers and employers report that AI can alleviate tedious and hazardous
tasks, thereby enhancing worker engagement and physical safety. Nevertheless, there are
also indications that AI-driven automation of simpler tasks may leave workers in faster-

32

paced, more intense roles (OECD 2023). As such, the way in which workers respond to
these new technologies—alongside limits on the scope of AI implementation—warrants
careful consideration. Furthermore, the degree of regulation or deregulation surrounding
AI will likely play a pivotal role in determining how opportunities are realised and the
extent to which outcomes differ among countries with varying policy frameworks.

V.

Policy proposals

The transformative potential of AI continues to reshape global labour markets, public
services, and the broader economic landscape. As AI-driven technologies grow
increasingly sophisticated, many workers fear displacement and diminished job
satisfaction, while gaps in infrastructure and digital literacy risk leaving entire
communities behind. At the same time, the rapid expansion of AI offers opportunities to
enhance social protection systems, spur productivity, and modernise traditional
vocational training approaches. Policymakers thus face the dual challenge of maximising
AI’s potential for social and economic progress while minimising the inequalities it may
exacerbate, particularly for mid-level qualified employees or those residing in regions
plagued by insufficient internet access. In response, governments, international
organisations, and private-sector actors have begun introducing measures designed to
promote inclusive, human-centred AI adoption. The success of these initiatives, however,
depends on striking an appropriate balance between fostering innovation and
safeguarding the fundamental rights and welfare of workers worldwide.
This section explores key dimensions of AI’s impact on employment and policy responses
designed to address them. First, it examines workers’ concerns, focusing on anxieties
surrounding job security and life satisfaction, as AI-driven disruptions transform

33

traditional roles. Next, it underscores the importance of access to internet services,
emphasising how digital infrastructure can enable or hinder AI adoption and perpetuate
existing divides, especially in developing countries. Attention then shifts to social
protection, highlighting how governments can harness AI to improve service delivery
while mitigating the risks of technological exclusion. Building on this, the section
addresses labour market formalisation, illustrating how regulations, collective
bargaining, and antitrust enforcement can protect workers from wage suppression and
precarious employment arrangements. It subsequently discusses vocational training,
emphasising the need for comprehensive programmes that equip individuals with AIfocused skills. Finally, it examines worldwide actions and initiatives, illuminating global
efforts to harmonise tax policies, foster equitable AI governance, and empower public
institutions.

A.

Workers’ Concerns

Recent evidence underscores the growing apprehension amongst workers regarding the
potential impact of AI, particularly Generative AI, on their future employment prospects.
Surveys suggest that a significant proportion of employees fear job loss as AI systems
become increasingly sophisticated, eroding traditional roles and responsibilities
(Gmyrek et al. 2024). Beyond mere job security, the advent of AI also appears to be
correlated with diminished life satisfaction. Indeed, workers exposed to AI report lower
life satisfaction, with an estimated difference of 0.04 standard deviations relative to those
not exposed (Giuntella, Koenig, and Stella 2023). This figure, while seemingly small,
amounts to approximately 16% of the positive effect of holding a university degree on life
satisfaction, or around 13% of the negative effect of being unemployed.

34

In light of these trends, policymakers face the challenge of designing strategies that both
assuage employees’ concerns and ensure a balanced distribution of AI’s benefits. One
potential approach involves promoting lifelong learning and continuous upskilling. Statesubsidised training programmes can equip workers with the competencies needed to
adapt to shifting demands, mitigating the threat of redundancy and easing anxieties
surrounding obsolescence. Furthermore, targeted interventions that address the unique
vulnerabilities of mid-level qualified workers, who have experienced more pronounced
declines in job satisfaction since 2015, could help stabilise employment outcomes such
as the case of Germany (Giuntella, Koenig, and Stella 2023).
Encouraging open dialogue between employers, employees, and government bodies is
equally vital. Through structured frameworks for employee consultation, both parties
can establish fair guidelines for the integration of AI technologies, ensuring that workers’
concerns are accounted for in organisational decision-making. Additionally, public
awareness campaigns may serve to clarify misconceptions around AI, thus fostering a
more informed and less fearful workforce, especially for largely exposed occupations to
AI. By combining these policy measures, governments can proactively address the
implications of AI on life satisfaction and job security, helping to ensure that the
technological revolution delivers broad-based benefits rather than compounding existing
insecurities.

In OECD countries where consultations have taken place, there is a

discrepancy ranging from 11 percentage points in the manufacturing sector to 17
percentage points in the financial sector, with workers expecting that the utilisation of
artificial intelligence in performing their tasks will result in higher wages. Moreover,
employers are more inclined to report that the adoption of artificial intelligence confers
greater benefits in terms of working conditions and productivity. Finally, the primary

35

concerns raised by workers pertain to skills and training, which have immediate
implications for the development of new guidelines (Lane, Williams, and Broecke 2023).

B.

Access to internet services

Ensuring equitable access to high-speed internet services is a central priority for
policymakers seeking to harness the full potential of AI. While AI-driven technologies
offer considerable promise for enhancing productivity and innovation, digital
deficiencies continue to inhibit these benefits in almost half of the positions that stand to
gain most (Gmyrek et al. 2024). This reality highlights the urgent need for targeted
interventions aimed at reducing disparities in connectivity infrastructure and promoting
the widespread availability of reliable internet.
A key driver of technology adoption lies in the existence of robust, high-speed internet
networks, which serve as the backbone for implementing advanced digital tools (Arntz et
al. 2024). In practice, governments can achieve such connectivity objectives by investing
in the expansion of broadband coverage, offering subsidies to lower access costs, and
introducing regulatory frameworks that encourage competition among service
providers. Through these measures, it becomes possible to foster an environment in
which businesses, public institutions, and households alike can seize the opportunities
afforded by AI. For instance, Chile has recently enacted legislation declaring internet
access a public service. Consequently, it qualifies for demand-side subsidies, and internet
providers are mandated to ensure comprehensive coverage within their designated
service areas. Moreover, the legislation authorises the provision of internet services via
cooperatives and establishes mechanisms for the regulatory oversight of the service.

36

Nevertheless, policy efforts to enhance digital infrastructure must also recognise the
sizeable internet and electricity gaps that persist in less developed countries. Indeed,
limited connectivity not only hinders the immediate uptake of cutting-edge technologies
but also perpetuates a widening digital divide, leaving vast segments of the population
unable to benefit from emerging innovations (Berg et al. 2023). In such contexts, policies
tailored to rural and underserved areas—ranging from public-private partnerships to
community-based digital education programmes—can help mitigate infrastructure and
knowledge barriers. Moreover, linking internet expansion to broader initiatives in energy
provision ensures that even the most remote communities can effectively participate in
the digital economy.

C.

Social protection

As AI technologies advance at an unprecedented pace, their impact on social protection
frameworks becomes increasingly evident. On one hand, AI promises to enhance the
efficiency and accessibility of benefits, including more targeted assistance and faster
disbursement of crucial aid (OECD 2024b). By matching individuals’ profiles with
available forms of support, social security agencies can proactively inform eligible
citizens of programmes they might otherwise overlook, thereby expanding the reach of
essential services. Automation further streamlines benefits administration by eliminating
human error and reducing administrative burdens, enabling social protection agencies to
allocate more resources towards improving service coverage and quality (OECD 2024a).
For instance, Togo has implemented NOVISSI, an unconditional cash transfer programme
that employs machine learning and artificial intelligence to prioritise impoverished rural
populations during emergencies, particularly in contexts lacking a robust social registry
and shock-responsive social protection delivery mechanisms (Lawson et al. 2023).

37

Similarly, Brazil utilises AI to detect fraud within its largest cash transfer programme,
Bolsa Família, and its corresponding social registry (CadÚnico). Finally, Korea extensively
applies AI to identify fraud and misuse within its public health insurance system,
leveraging existing data on the utilisation of health services and insurance claims to flag
suspicious cases (OECD 2024b).
On the other hand, the advent of AI raises critical distributional questions. The way AI
property rights are defined and how redistributive fiscal policies are structured will
largely determine whether productivity gains sufficiently offset potential labour market
disruptions (Cazzaniga et al. 2024). By regulating ownership and profit-sharing
mechanisms for AI innovations, policymakers can channel part of the resultant economic
gains into social programmes designed to protect workers from sudden job displacement
and income shocks. This approach becomes especially relevant in contexts where
automation can shift the distribution of returns in both labour and capital markets, as
some firms may accumulate substantial profits while others fall behind (Bastani and
Waldenström 2024).
Collective bargaining and social dialogue also play a vital role in guiding a fair transition
to AI-driven workplaces (OECD 2023). By engaging employees and employers in
discussions on wage-setting, skills development, and working conditions, policymakers
can help ensure that AI tools do not exacerbate existing power imbalances (Berg et al.
2023). This inclusive dialogue, ideally undertaken in anticipation of upcoming
technological shifts, helps align AI’s potential efficiencies with overarching goals of equity
and fairness. Similarly, strengthening social safety nets and offering continuous training
or upskilling opportunities to workers most at risk of displacement serves as a
cornerstone of inclusive labour markets (Cazzaniga et al. 2024). For example, trade

38

unions in the US and Germany have been raising concerns about the use of AI on
surveillance introducing power imbalances between workers and employers (Krämer
and Cazes 2022). Fiscal interventions grounded in progressive taxation can raise the
revenue needed for such initiatives, ensuring that those most likely to benefit from AI
innovations contribute proportionately to collective welfare.
Although taxation can partly compensate for wage declines, direct benefit transfers and
robust welfare systems have proven particularly effective in cushioning the adverse
impacts of automation on total household disposable income (Doorley et al. 2023). This
underscores the importance of reinforcing unemployment insurance, expanding
universal basic services, and creating targeted support programmes to protect vulnerable
populations from sudden earnings losses. Equally vital is the need for robust data
governance and specialised technological infrastructure to implement AI solutions
securely and transparently, thereby preventing potential breaches of public trust, such as
the case of e-government developed by Estonia (OECD 2024b).
Finally, the introduction of AI-based solutions must be designed to avoid creating new
forms of exclusion. Individuals with limited digital access or insufficient digital literacy
risk being marginalised from automated services, thus widening the very gaps these
technologies aim to close. Policymakers and social protection agencies can mitigate this
risk by embedding inclusive strategies within AI initiatives—ensuring that all segments
of society benefit equally (OECD 2024b). By doing so, they will not only foster public
confidence in AI-driven social protection but also contribute to more equitable, resilient,
and inclusive welfare systems(OECD 2024a). There are instances where AI-based
procedures have resulted in the exclusion of households from social benefits. For
example, in the Netherlands, households have been categorised as fraudsters, while in

39

Sweden, errors in the automated decision-making algorithms have led to the rejection of
unemployment benefits for eligible individuals. Similarly, in Australia, there have been
cases of income misreporting among households from ethnic minority backgrounds
(Wagner, Ferro, and Stein-Kaempfe 2024).

D.

Labour market formalisation

Recent scholarship highlights how the widespread adoption of AI and automation can
have uneven repercussions on labour markets, particularly when firms wield monopsony
power. Under such conditions, automation exerts downward pressure on both the
number of jobs available and the wages of the remaining workforce, effectively
amplifying the negative consequences of technological change (Azar et al. 2023). This
dual impact underscores the importance of formulating robust policies that foster labour
market formalisation to mitigate the risks posed by automation while safeguarding
workers’ rights and livelihoods.
A first policy priority is to enhance legal frameworks and institutional structures that
support formal employment arrangements. By mandating written contracts, enforcing
transparent remuneration practices, and requiring the provision of social insurance,
governments can reduce precarious work patterns and strengthen workers’ bargaining
positions. These measures help ensure that, even in the face of technological disruption,
employees retain a baseline of security. In addition, clearly defined contractual
obligations can limit the capacity of monopsonistic firms to unilaterally dictate wages
when labour demand declines due to automation. However, there exists a threshold
beyond which rising formal labour costs incentivise greater reliance on technology,
thereby reducing the need for workers and lowering the firm’s operating expenses

40

A second approach involves actively promoting collective bargaining and worker
representation. Where employees can negotiate wages and working conditions
collectively, the scope for monopsonistic exploitation diminishes considerably. Collective
negotiations allow workers to press for fair compensation even as automation substitutes
certain tasks, thereby curbing downward pressure on wages. Governments might
encourage such arrangements through legislation that sets clear guidelines to ensure fair
negotiations to adjust the impacts on wages in the formal sector, especially in mid-skilled
workers and occupations. Lane, Williams and Broecke (2023) found that, for developed
OECD member countries (Austria, Canada, France, Germany, Ireland, the UK, and the
United States), companies that conducted consultation sessions with workers regarding
AI achieved better adoption and utilisation of this technology, as well as more positive
perceptions concerning safety and health, compared to those that did not engage in such
processes. Moreover, the results indicate that workers using AI are more likely to report
that it improved their performance and working conditions when their companies
consulted with them or with worker representatives prior to adopting new workplace
technologies. Conversely, workers’ organisations ought to harness digital technologies to
mobilise and organise labour. This approach should involve enhanced engagement with
informal workers and the adoption of more inclusive organising strategies (ILO 2019).
For example, in Germany, trade unions have actively engaged in debates on AI regulation,
thereby fostering cooperative strategies between management and works councils
concerning both the implementation and oversight of AI technologies (Krzywdzinski,
Gerst, and Butollo 2023).
Antitrust enforcement also emerges as a key factor in mitigating the adverse
consequences of automation. By scrutinising mergers and acquisitions that risk

41

consolidating excessive labour market power, policymakers can preserve competitive
conditions, thus counteracting the wage suppression that automation may induce (Azar
et al. 2023). Simultaneously, active labour market policies—such as re-skilling
programmes and job-search assistance (Solutions for Youth Employment (S4YE) 2023)
or Digital Talent for Chile (Neilson, Egana-delSol, and Humphries 2024)—can assist
displaced workers in securing alternative routes to gainful employment, thereby
reducing periods of unemployment and alleviating downward pressure on wages.
On one hand, artificial intelligence is being employed as a tool for matching labour market
demand and supply on a skills-based approach within public employment services in
countries such as Latvia, Korea, Spain and Greece (Brioscú et al. 2024). On the other hand,
Singapore has implemented the SkillsFuture initiative to mitigate the disruptive impact
of artificial intelligence by offering training specifically tailored to AI technologies.
SkillsFuture is a government-led programme that promotes lifelong learning and skill
development, thereby equipping workers with the competencies necessary to adapt to
rapid technological changes (International Monetary Fund 2024)

E.

Vocational training

The rapid expansion of AI technologies has created a pressing need for enhanced
vocational training programmes tailored to address emerging skills gaps. While many
firms report offering AI-related training, the persistent shortage of qualified personnel
remains a primary obstacle to wider adoption and effective implementation (OECD
2023). This skills deficit not only undermines productivity and competitiveness but also
impedes more inclusive labour market outcomes, as certain occupations become
increasingly susceptible to technological displacement. By designing and funding

42

comprehensive vocational training initiatives, policymakers can better equip the current
and future workforce to harness the transformative potential of AI while mitigating
adverse consequences for vulnerable and exposed occupational groups.
One key rationale for focusing on vocational training lies in its demonstrable impact on
employment prospects. Research indicates that individuals with AI-related skills enjoy
improved odds of receiving interview invitations, as well as higher hourly wages
(Drydakis 2024). This advantage not only reflects an evolving demand for AI
competencies but also underscores the broader value of educational capital in labour
markets subject to technological change. Considering these findings, strategies aimed at
integrating AI modules into existing technical and vocational curricula can help bridge
the gap between conventional training models and the rapidly shifting demands of the
digital economy.
To optimise these efforts, governments should pursue a two-pronged approach. First,
advanced and emerging market economies ought to invest in AI innovation and
integration while establishing robust regulatory frameworks that foster ethical and
inclusive technological development (Cazzaniga et al. 2024). By doing so, policymakers
can encourage businesses to collaborate with educational institutions to develop targeted
training modules and support apprenticeships, thereby ensuring that learners acquire
the specific skills most in demand for the local labour market. This collaboration could
include financial incentives, public-private partnerships, or scholarship schemes aimed
at promoting enrolment in AI-focused vocational training.
Second, in less prepared emerging and developing market economies, priority should be
given to building foundational infrastructure and cultivating a digitally skilled workforce
(Cazzaniga et al. 2024). These efforts involve expanding reliable internet access, ensuring
43

stable electricity supplies, and creating local ecosystems of innovation that can nurture
newly trained AI professionals. Vocational schools in these regions must be equipped
with up-to-date facilities and supported by well-trained educators capable of delivering
relevant, high-quality programmes. International cooperation and development
assistance could play a critical role here, complementing domestic efforts through
technical expertise and targeted investment.

F.

Worldwide actions and initiatives

One prominent concern arises from the prospect of shifting tax burdens: as AI reshapes
economic activity, governments may find it necessary to shift taxation away from labour
and towards capital in order to sustain revenue for public investment and redistribution
(Bastani and Waldenström 2024). In the event that AI-driven technologies also facilitate
greater international mobility of both labour and capital, the coordination of tax policies
across borders becomes increasingly pressing. Without such coordination, countries risk
engaging in a race to the bottom, undermining their collective capacity to finance
essential social protection programmes.
Recognising these challenges, various international organisations and national
governments have proposed collaborative strategies aimed at shaping a fair and inclusive
AI ecosystem. Among these proposals is a call for enhanced international cooperation and
knowledge exchange, underpinned by a unified methodology for measuring AI’s
economic and social impact (United Nations 2024). Such cooperation extends to the
establishment of joint training initiatives and partnerships between countries, designed
to foster innovation and ensure that AI infrastructure and benefits are distributed
equitably. Alongside these collaborative measures, recommendations for the continuous

44

education and training of affected workers underscore the importance of upskilling and
reskilling, ensuring that the workforce remains prepared for the rapidly evolving
demands of AI-intensive industries and reduce potential losses on employment and
wages.
Governments and intergovernmental agencies also acknowledge the need to regulate AI
in a manner that preserves competition and protects vulnerable groups. The potential for
AI to reinforce the market dominance of major technology firms, displace lower-skilled
occupations, and exacerbate income inequality is a growing concern (World Bank 2024).
Regulatory responses, however, must be carefully balanced. While fragmented or overly
rigid regulations risk stifling innovation and encouraging regulatory arbitrage, overly
permissive regimes may fail to address emerging ethical dilemmas or labour market
inequalities (World Bank 2024). Striking a balance between these extremes is crucial for
fostering an environment that rewards responsible innovation.
Equally important is the integration of AI solutions into public institutions, which can
significantly enhance the delivery of public services and support effective governance
(World Bank 2024). For example, AI-driven approaches have the potential to modernise
social protection systems by automating administrative tasks, improving targeting
mechanisms, and reducing errors in the allocation of benefits (OECD 2024a). Yet, to
realise these gains without compromising the welfare of those likely to be displaced by
automation, policymakers must prioritise inclusive, human-centred design and ensure
that new technologies are supported by robust data governance frameworks.

45

VI.

Conclusions

The findings presented here underscore the multifaceted implications of technological
change for the labour market, particularly when comparing the effects of traditional
automation to those arising from AI. In the literature, automation has chiefly been
characterised by its strong focus on routine and non-cognitive tasks, exerting a polarising
effect on the employment landscape by reducing real wages for mid-tier workers and
prompting broader labour market restructuring, especially in developed economies. This
dynamic has contributed to the hollowing-out of middle-skill occupations, as routine
tasks amenable to standardisation and mechanisation are increasingly automated. By
contrast, AI appears to be reshaping a distinct segment of the labour market—one that
relies on non-routine cognitive tasks, creative aptitudes, and complex problem-solving
skills. Consequently, AI’s influence is beginning to permeate roles once viewed as
insulated from automation processes.
A key insight emerging from recent studies is that non-routine cognitive jobs are now
increasingly exposed to AI’s expanding capabilities. Although there remains limited but
growing evidence of outright job displacement in these domains, clear indications
suggest that tasks traditionally performed by humans are being replaced or substantially
transformed. In many highly skilled occupations, for instance, AI-based systems can
execute routine components of knowledge-intensive tasks, thereby freeing workers to
concentrate on higher-value or more cognitively challenging responsibilities. For
instance, Lassebie and Quintini (2022) reinforces this perspective, illustrating that
individuals with advanced digital competencies, robust cognitive skills, and the capacity
for creative thinking or social intelligence can employ AI as a complementary resource,
optimising task performance and progressing into more innovative or managerial roles.

46

In this sense, the extent of task reorganisation depends largely on workers’ skill profiles
and the enabling infrastructure.
Furthermore, it is observed that exposure will also depend on the characteristics of the
workers. These non-routine jobs target a segment of workers with more education and
older age, who will be more exposed (Egana-delSol and Bravo-Ortega 2025). The ability
to complement the use of this technology and to retrain to other less exposed areas will
be key to successfully overcome this exposure and possibility of job automation. With
this, the digital divide and its gender component is also an important attribute for the use
of this technology. Making it compatible to have access and thereby take advantage of the
benefits of using AI, reducing the gap in digital infrastructure that exists, but at the same
time under a resilient labor system due to the possibilities of automation, is a goal that
must be pursued.
Against this backdrop, upskilling and reskilling initiatives, especially those focused on AIrelated fields, emerge as pivotal for mitigating the adverse consequences of automation
and AI deployment. Vocational training programmes that emphasise creative thinking,
advanced problem-solving skills, and digital literacy offer strategic pathways for
enhancing employability in a rapidly evolving technological landscape. By fortifying the
overall skill base, such initiatives help mitigate the risks of displacement and promote a
fairer allocation of AI’s benefits. In addition, continuous professional development not
only supports individual workers but also bolsters local economies by fostering
innovation and productivity. Equipping the workforce with AI competencies allows them
to transcend routine tasks and embrace emerging opportunities across various
industries.

47

Nevertheless, successfully equipping workers with AI-relevant skills relies on addressing
the broader digital divide. Policymakers must prioritise infrastructural improvements in
regions plagued by unstable electricity supplies, patchy internet connectivity, and
inadequate technological resources. In the absence of sustained efforts to resolve these
foundational shortcomings, even the most comprehensive upskilling programmes are
likely to fall short. Reducing the digital gap, therefore, constitutes not merely an economic
imperative but also a crucial objective for human development, as it promotes equitable
participation in the digital economy and curbs the entrenchment of existing inequalities.
Furthermore, AI’s growing capacity to influence non-routine, cognitive tasks calls for
innovative policy responses that extend beyond traditional sectoral boundaries.
Governments and other institutions can no longer focus solely on interventions tailored
to the effects of automation on routine-based occupations; they must instead devise
integrative strategies that consider how AI might transform realms as diverse as
healthcare, education, financial services, and public administration. Simultaneously,
policymakers should adopt inclusive perspectives to forestall labour market polarisation
associated with AI’s rapid advancement, ensuring that workers from a range of
educational and socioeconomic backgrounds do not become marginalised. From a global
standpoint, there is an equally urgent need for multilateral coordination surrounding tax
policies, intellectual property rights, and technology transfer frameworks, so that AI does
not become a fresh driver of inequality.
These global concerns take on added importance against the backdrop of demographic
changes, particularly in regions with ageing populations. As the working-age population
share declines, many countries increasingly rely on technological progress to maintain
productivity and support growing numbers of dependents. AI promises substantial

48

benefits in automating complex tasks, optimising workflows, and generating new
avenues for economic growth. Yet for AI to deliver these advantages without worsening
inequality, national and international policies must be carefully calibrated to encourage
inclusive growth, facilitate workforce transitions, and strengthen social infrastructures.
In sum, the transition from automation to AI highlights both continuities and divergences
in the relationship between technology and employment. While the most pronounced
effect of automation has been the erosion of middle-skill and routine roles, AI is now
reaching into higher-skill, non-routine domains once perceived as safe from
mechanisation. This transformation brings with it significant opportunities—including
heightened innovation, productivity gains, and the potential enhancement of human
creativity—while also presenting policymakers and societies with new challenges. Chief
among these is ensuring that AI fosters inclusive human development, rather than
exacerbating inequalities within and between nations at varying stages of digital
infrastructure. In light of these considerations, strategies for investing in digital capacity,
fortifying infrastructure, and prioritising AI-focused training must underpin any
comprehensive policy framework. By acknowledging and proactively tackling these
critical issues, governments, employers, and international institutions can help ensure
that AI’s transformative potential is harnessed for broad-based prosperity and equitable
growth in the decades ahead.

49

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57
</reference>

<statements>
1. This broad reach means AI can reshape economic structures and institutional dynamics, amplifying existing trends such as skill-biased technological change, creative destruction, and the rise of superstar firms, while also introducing new forms of algorithmic management and platformization.
2. Theoretical ambiguity around aggregate employment impacts is a recurring theme: while AI-facilitated automation reduces labor demand in exposed tasks, productivity gains and new tasks can offset these losses under certain conditions.
3. In contrast, middle-skill routine occupations face downward pressure on wages and employment, contributing to the hollowing out of the middle of the wage distribution.
4. The distributional consequences of AI adoption are therefore contingent on institutional settings: where collective bargaining, minimum wage regulations, and social protections are strong, productivity gains are more likely to translate into broad-based income growth; where such institutions are weak, inequality tends to widen.
5. Some analyses of European regions and other contexts suggest that AI innovation may reduce the labor share of income, particularly where complementary policies to strengthen worker bargaining power and skill development are absent.
6. As these firms centralize high-value AI development and data infrastructures, routine and standardized tasks may be offshored or automated, further fragmenting employment structures and reinforcing global inequalities between countries that produce AI innovations and those that primarily adopt them.
7. Second, the diffusion of AI, particularly via cloud-based services and open-source tools, is rapid and global, allowing even smaller firms and organizations to adopt advanced AI functionalities without large upfront capital investments.
8. Fifth, policy interventions around education, training, social protection, and AI governance are central in mediating outcomes, suggesting that technological trajectories can be steered toward more inclusive configurations.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.