La gobernanza jurídica de la mejora y la reconversión de las competencias

en IA hacia un nuevo deber del empleador

J. Law Epistemic Stud. (2026) 4: e176

https://doi.org/10.5281/zenodo.21186038

ISSN 3091-1575

ORIGINAL ARTICLE

The legal governance of ai upskilling and reskilling: towards a new

employer duty

 Joel Menezes-Barreto

[email protected].ao

Universidade Internacional do Cuanza: Cuito, Bié, Angola

FUNIBER, España.

Received: 03 May 2026 / Accepted: 10 June 2026 / Published online: 30 June 2026

Joel Menezes-Barreto Júnior Duliet Hong León Jaime E. Pérez-Fernández

António Ramos-Congo Natalia A. Domingas-Chivango

Abstract This article examines whether the rapid adoption of arti-

cial intelligence (AI) justies a broader interpretation of employer

responsibilities regarding continuous upskilling and reskilling. An

interdisciplinary qualitative methodology was employed, combin-

ing doctrinal-comparative legal analysis with a systematic literature

review conducted according to the PRISMA 2020 guidelines. Sci-

entic evidence was collected from Scopus, Web of Science, Sci-

enceDirect and SpringerLink, complemented by reports from the

International Labour Organization (ILO), the Organisation for Eco-

nomic Co-operation and Development (OECD) and the World Eco-

nomic Forum (WEF). The ndings indicate that AI-driven work-

force transformation extends beyond technological innovation and

signicantly aects employability, requiring organisations to an-

ticipate skills disruption through strategic workforce development.

The study identies a conceptual gap between labour law, strategic

human resource management and corporate governance concern-

ing employer responsibility for workforce capability development.

To address this gap, it proposes the AI Employability Governance

Framework (AEGF), an original model integrating strategic work-

force foresight, continuous upskilling, reskilling pathways, shared

employer responsibility, labour rights protection and sustainable

employability. The framework contributes to the emerging debate

on responsible AI governance by providing a conceptual foundation

for more inclusive, resilient and legally informed models of work-

force transformation.

Keywords articial intelligence, upskilling, reskilling, labour law,

human resource management.

Resumen El presente artículo analiza si la rápida adopción de la

inteligencia articial (IA) justica una interpretación más amplia

de las responsabilidades del empleador en materia de upskilling y

reskilling continuos. Se empleó una metodología cualitativa inter-

disciplinaria que combinó el análisis jurídico doctrinal-comparado

con una revisión sistemática de la literatura realizada conforme a

las directrices PRISMA 2020. La evidencia cientíca se obtuvo de

Scopus, Web of Science, ScienceDirect y SpringerLink, comple-

mentada con informes de la Organización Internacional del Tra-

bajo (OIT), la Organización para la Cooperación y el Desarrollo

Económicos (OCDE) y el Foro Económico Mundial (WEF). Los

resultados muestran que la transformación del trabajo impulsada

por la IA trasciende la innovación tecnológica y afecta directamente

la empleabilidad, exigiendo que las organizaciones anticipen la ob-

solescencia de competencias mediante una planicación estratégica

del talento. Asimismo, se identica una brecha conceptual entre el

Derecho del Trabajo, la gestión estratégica de recursos humanos y

la gobernanza corporativa respecto de la responsabilidad empre-

sarial en el desarrollo de capacidades laborales. Como principal

aporte, se propone el AI Employability Governance Framework

(AEGF), un modelo que integra previsión estratégica, desarrollo

continuo de competencias, protección de derechos laborales y em-

pleabilidad sostenible para orientar una gobernanza responsable de

la transformación del trabajo.

Palabras clave inteligencia articial, upskilling, reskilling, dere-

cho del trabajo, gestión de recursos humanos.

How to cite

Menezes-Barreto, J., Hong, D., Pérez-Fernández, J. E., Ramos-Congo, A., & Domingas-Chivango, N. A. (2026). The legal governance of ai upskilling and

reskilling: towards a new employer duty. Journal of Law and Epistemic Studies, 4, e176. https://doi.org/10.5281/zenodo.21186038

J. Law Epistemic Stud. (2026) 4: e176

Introduction

Articial intelligence (AI) is rapidly redening the rela-

tionship between technological innovation, organisational

competitiveness and workforce development. While early

applications of AI in human resource management (HRM)

primarily focused on automating recruitment, employee

selection and administrative processes, recent advances

in generative AI and intelligent decision-support systems

have fundamentally transformed the competencies required

to participate eectively in the labour market. Rather than

replacing isolated tasks, AI is reshaping entire occupation-

al proles, accelerating skills obsolescence and compelling

organisations to reconsider how employability can be sus-

tained in an environment characterised by continuous tech-

nological disruption. This transition has shifted scholarly

attention from the adoption of AI technologies to the broader

challenge of ensuring that workers possess the capabilities

necessary to adapt to increasingly dynamic forms of work

(OECD, 2023; ILO, 2025; Kulkov et al., 2024).

Recent evidence indicates that AI-related technological

change is generating unprecedented demand for advanced

digital, analytical and socio-cognitive skills while simul-

taneously reducing the relevance of routine competencies.

According to the World Economic Forum, nearly half of

employees worldwide will require signicant reskilling or

upskilling before the end of the decade as AI increasingly

complements or transforms existing job functions. Similarly,

the Organisation for Economic Co-operation and Develop-

ment argues that technological progress alone cannot guar-

antee inclusive economic growth unless organisations invest

systematically in lifelong learning and workforce capability

development. These ndings suggest that the sustainability

of AI-driven transformation depends not only on technologi-

cal adoption but also on the institutional capacity to maintain

workers’ employability throughout their careers (Barbosa &

Real de Oliveira, 2026; Chhibber et al., 2025; Pasi et al.,

2026).

The emerging literature has consequently recognised

upskilling and reskilling as strategic mechanisms through

which organisations enhance innovation capacity, organi-

sational resilience and long-term competitiveness. Within

strategic HRM, continuous learning is no longer regarded

merely as a developmental activity but as a central organi-

sational capability that enables rms to respond eectively

to technological uncertainty and rapidly changing business

environments. Recent empirical studies demonstrate that or-

ganisations investing in structured AI-related learning pro-

grammes experience greater workforce adaptability (Nawaz

et al., 2024), stronger innovation performance and higher

levels of organisational resilience than rms adopting tech-

nology without equivalent investments in human capability

development (Minbaeva, 2023) Consequently, human capi-

tal development is increasingly interpreted as a strategic in-

vestment rather than an operational cost. (Pasi et al., 2026)

Despite this growing consensus, the legal dimension of

AI-induced workforce transformation remains comparative-

ly underdeveloped. Existing scholarship has predominantly

examined AI governance through the lenses of algorithmic

accountability, automated decision-making, data protection

and ethical principles. Although these contributions have

substantially advanced understanding of responsible AI de-

ployment, considerably less attention has been devoted to a

more fundamental question: does the widespread adoption of

AI generate a legal responsibility for employers to preserve

workers’ employability through continuous upskilling and

reskilling? This question becomes particularly relevant be-

cause the risks associated with AI are no longer conned to

discriminatory automated decisions; they increasingly con-

cern the possibility that workers become structurally exclud-

ed from labour markets owing to insucient opportunities

for capability development.

This problem is not merely managerial. It raises a deeper

legal question concerning the transformation of employer

obligations in technologically intensive workplaces. Labour

law has historically recognised employer duties related to

occupational safety, non-discrimination, vocational training,

information, consultation and protection against arbitrary

dismissal. Yet the acceleration of AI adoption challenges the

adequacy of these traditional categories. If technological re-

structuring makes existing skills obsolete, and if employers

are the primary institutional actors deciding when, how and

where AI systems are introduced, the preservation of work-

ers’ employability can no longer be treated as an exclusively

individual responsibility. It becomes part of the normative

architecture through which labour law responds to asymme-

tries of power, information and opportunity within the em-

ployment relationship.

International policy debates increasingly point in this di-

rection. The Organisation for Economic Co-operation and

Development (OECD) highlights that AI governance cannot

be separated from labour market adaptation mechanisms. The

OECD Employment Outlook 2023 devotes specic attention

to articial intelligence, skills policies, ethical challenges

and the role of social dialogue in supporting the AI transition

(OECD, 2023). Similarly, the International Labour Organi-

zation (ILO) has emphasised that AI and digitalisation are

reshaping working conditions and occupational exposure,

including through generative AI, making skills development

and protection against exclusion central to fair technological

transitions (ILO, 2025). These ndings are consistent with

the analysis of Pronello & Fedeli (2025), who argue that

technological transformation requires continuous workforce

adaptation. Likewise, the World Economic Forum identies

J. Law Epistemic Stud. (2026) 4: e176

technological change, articial intelligence and digital trans-

formation as major drivers of skills disruption, reinforcing

the need for systematic upskilling and reskilling strategies

rather than episodic training interventions (World Economic

Forum, 2025).

However, these policy documents tend to frame upskilling

and reskilling as instruments of competitiveness, adaptability

or labour-market policy, rather than as potential legal obliga-

tions within the employment relationship. This reveals a sig-

nicant research gap. The current literature on AI and work

has advanced sophisticated debates on automation risks, al-

gorithmic management, data protection, platform labour and

digital surveillance, but it has not suciently conceptualised

training for AI-enabled workplaces as a juridical duty con-

nected to sustainable employability. In HRM research, skills

development is usually analysed as a strategic practice; in

labour law, training is commonly treated as a right, policy

objective or collective bargaining issue; in corporate gov-

ernance, human capital is increasingly linked to ESG and

sustainability reporting. What remains underdeveloped is an

integrated theory explaining when and why AI-related up-

skilling and reskilling should become part of the employer’s

duty of responsible workforce transformation.

This article addresses that gap by proposing the concept of

AI Employability Governance, understood as the integrat-

ed legal, organisational and strategic system through which

employers anticipate AI-induced skills disruption and ensure

fair, continuous and inclusive access to upskilling and re-

skilling opportunities. The central argument is that, in AI-en-

abled organisations, employability should not be reduced to

an individual attribute or a market outcome. Rather, it should

be understood as a shared institutional responsibility shaped

by employer power, technological decision-making, access

to training, social dialogue and corporate accountability.

This approach allows the article to move beyond the conven-

tional distinction between voluntary HR development and

mandatory labour protection, oering instead a governance

framework in which workforce capability development be-

comes a condition of lawful and legitimate AI adoption.

The objective of the article is therefore to examine wheth-

er AI-induced skills disruption justies the recognition of

a new employer duty to support sustainable employability

through continuous upskilling and reskilling. To achieve this

objective, the article combines doctrinal-comparative legal

analysis with a systematic review of recent literature on AI,

HRM, labour law, skills governance and sustainable em-

ployment. Its original contribution lies in the development

of the AI Employability Governance Framework (AEGF),

a theoretical and operational model that connects strategic

workforce foresight, continuous AI upskilling, reskilling

pathways, labour rights protection, shared employer respon-

sibility and sustainable employability outcomes.

The article proceeds as follows. First, it explains the meth-

odological approach and the selection of academic and in-

stitutional sources. Second, it analyses AI-induced skills

disruption as a legal and organisational problem. Third, it

examines the evolution of employer duties in light of techno-

logical transformation and workforce vulnerability. Fourth,

it develops the AI Employability Governance Framework as

an original contribution to HRM and labour law scholarship.

Finally, it discusses the implications of the proposed frame-

work for organisations, regulators, social partners and future

research, with particular attention to emerging economies in

Latin America.

Methodology

This study adopts an interdisciplinary qualitative research

design combining doctrinal-comparative legal analysis with

a systematic literature review (SLR) conducted in accordan-

ce with the PRISMA 2020 Statement. This methodological

integration was selected because the research objective ex-

tends beyond describing existing knowledge to developing

an original theoretical framework explaining how employer

responsibilities evolve in response to AI-driven workforce

transformation. The combination of legal analysis and evi-

dence synthesis enables a comprehensive examination of

the intersection between labour law, human resource mana-

gement (HRM), corporate governance and articial intelli-

gence (AI), thereby supporting the development of a novel

governance perspective.

The doctrinal component examined the evolution of em-

ployer obligations regarding vocational training, employa-

bility protection, lifelong learning and workforce adaptation

within contemporary labour law. Particular attention was gi-

ven to the legal implications of technological transformation,

the redistribution of responsibilities between employers and

employees, and the emergence of international regulatory

approaches promoting responsible AI adoption. The com-

parative dimension considered supranational regulatory de-

velopments, international policy instruments and emerging

governance principles applicable to AI-enabled workplaces.

The systematic literature review followed the reporting

recommendations established by the PRISMA 2020 State-

ment (Page et al., 2021). Searches were performed in four

major scientic databases—Scopus, Web of Science Core

Collection, ScienceDirect and SpringerLink—because of

their extensive coverage of high-impact research in HRM,

labour law, organisational studies and digital governance.

Additional institutional publications were retrieved from the

websites of the International Labour Organization (ILO), the

Organisation for Economic Co-operation and Development

(OECD), the European Commission and the World Econo-

mic Forum (WEF) to incorporate recent policy and regula-

tory developments relevant to AI, employment and workfor-

J. Law Epistemic Stud. (2026) 4: e176

ce skills.

The search strategy combined controlled vocabulary and

free-text keywords using Boolean operators. The principal

search string included the following terms:

(“articial intelligence” OR “generative AI”) AND (“ups-

killing” OR “reskilling” OR “lifelong learning”) AND (“hu-

man resource management” OR “human capital”) AND (“la-

bour law” OR “employment law”) AND (“employability”

OR “future skills” OR “workforce transformation”).

To ensure the relevance and contemporaneity of the evi-

dence, the review was limited to publications released be-

tween January 2022 and March 2026, although seminal

works were retained where necessary to support foundatio-

nal theoretical concepts. Eligible sources included peer-re-

viewed journal articles, international reports, regulatory

documents and ocial policy papers published in English.

Editorials, conference abstracts, opinion pieces, duplicated

records and non-peer-reviewed documents were excluded

unless issued by internationally recognised organisations

such as the ILO or OECD.

Following duplicate removal, titles and abstracts were

independently screened according to predened inclusion

criteria. Full-text assessment was subsequently undertaken

to determine conceptual relevance, methodological quality

and direct alignment with the research objective. Particular

emphasis was placed on studies addressing AI-enabled wor-

kforce transformation, digital skills, strategic HRM, emplo-

yability, organisational capability development, labour regu-

lation and corporate governance. Rather than conducting a

quantitative meta-analysis, the review employed a thematic

synthesis, allowing the identication of recurrent concepts,

theoretical convergences, normative divergences and unre-

solved research gaps across disciplines.

The analytical process comprised four sequential stages.

First, the selected studies were classied according to dis-

ciplinary orientation, including HRM, labour law, organisa-

tional governance, AI regulation and public policy. Second,

recurring themes associated with employer responsibilities,

workforce capability development and sustainable employa-

bility were identied through iterative coding. Third, these

ndings were interpreted comparatively in light of contem-

porary labour law principles and international governance

initiatives. Finally, the resulting evidence informed the de-

velopment of the AI Employability Governance Framework

(AEGF), which constitutes the principal theoretical contri-

bution of this article.

Although the study relies exclusively on publicly avai-

lable academic and institutional sources and therefore did

not require approval from a research ethics committee, the

research adhered to recognised standards of academic inte-

grity, transparency and methodological rigour. All sources

were critically evaluated according to their scientic quality,

institutional credibility and relevance to the research objecti-

ve. Nevertheless, some limitations should be acknowledged.

The review was restricted to English-language publications

indexed in major international databases, which may have

excluded valuable regional scholarship published in other

languages. Furthermore, because the proposed framework

is conceptual, future empirical research will be required to

validate its applicability across dierent legal systems, in-

dustrial sectors and organisational contexts.

Results and discussion

The rapid diusion of articial intelligence (AI), particu-

larly generative AI, is transforming not only how organisa-

tions perform work but also the competencies required to

remain employable. Unlike previous waves of technological

innovation, which primarily automated repetitive or routine

tasks, contemporary AI systems increasingly complement or

substitute complex cognitive activities, altering occupatio-

nal structures across both high- and low-skilled professions.

Consequently, workforce transformation can no longer be

understood merely as a process of technological adaptation;

it represents a structural reconguration of human capital

requirements that aects organisational competitiveness, la-

bour market participation and long-term employability.

Recent international evidence conrms that AI is accele-

rating the pace of skills disruption. The Future of Jobs Re-

port 2025 estimates that approximately 39% of workers’ core

skills will change by 2030, largely because of technological

progress, generative AI and digital transformation. The re-

port also identies analytical thinking, AI literacy, techno-

logical prociency, resilience and continuous learning as

among the fastest-growing competencies required by emplo-

yers worldwide (World Economic Forum, 2025). Rather than

eliminating work altogether, AI is redening the composi-

tion of jobs, increasing demand for adaptable workers capa-

ble of integrating technological knowledge with cognitive,

interpersonal and ethical capabilities.

A similar conclusion emerges from the OECD Employ-

ment Outlook 2023, which argues that AI should not be

analysed exclusively in terms of job displacement. Instead,

its principal impact lies in the transformation of task com-

position, requiring continuous capability renewal throughout

employees’ careers. The OECD further notes that the benets

of AI depend signicantly on complementary investments in

education, vocational training and lifelong learning policies.

Organisations that fail to develop workforce capabilities risk

creating productivity gaps, reduced innovation capacity and

growing inequalities between highly skilled and less adapta-

ble workers (OECD, 2023).

From the perspective of strategic human resource manage-

ment (SHRM), these developments reinforce the argument

that organisational competitiveness increasingly depends on

J. Law Epistemic Stud. (2026) 4: e176

dynamic human capabilities rather than static knowledge

assets. Recent HRM scholarship suggests that rms derive

sustainable competitive advantage when they systematically

anticipate future competency requirements and align lear-

ning strategies with technological change (Minbaeva, 2023).

Accordingly, upskilling and reskilling should not be viewed

as isolated training initiatives but as strategic investments

that strengthen organisational resilience, innovation capacity

and long-term workforce sustainability.

Nevertheless, the literature reveals a signicant imbalance.

Most empirical studies evaluate the eectiveness of AI-rela-

ted training programmes by examining productivity gains,

digital readiness or employee performance. Comparatively

little attention has been devoted to the legal implications ari-

sing when organisations introduce AI technologies without

providing workers with realistic opportunities to acquire the

competencies required for new forms of work. This omission

is particularly signicant because technological transforma-

tion is generally initiated by organisational decisions rather

than by employees themselves. Consequently, responsibility

for managing the risks associated with skills obsolescence

cannot reasonably rest solely with individual workers.

The distinction between technological adoption and wor-

kforce capability transformation therefore becomes critical.

Organisations frequently invest substantial nancial resour-

ces in AI infrastructure while allocating comparatively fewer

resources to systematic capability development. This asym-

metry creates what may be described as a capability transi-

tion gap, whereby technological implementation progresses

more rapidly than workforce adaptation. The result is not

only reduced organisational performance but also increased

vulnerability for employees whose existing competencies

gradually lose economic value despite continued contractual

commitment.

This capability transition gap has important implications

for labour law. Traditionally, employability has been consi-

dered largely an individual responsibility inuenced by edu-

cation, experience and labour market conditions. However, in

AI-enabled workplaces, employability increasingly depends

on strategic organisational decisions regarding technology

adoption, work redesign and access to learning opportuni-

ties. When employers determine the pace and direction of

technological transformation, they simultaneously inuence

workers’ capacity to remain professionally relevant. Under

these circumstances, employability evolves from a purely

personal characteristic into a shared organisational responsi-

bility with legal, managerial and ethical dimensions.

The evidence reviewed therefore suggests that AI-induced

workforce transformation should be interpreted not simply

as a technological challenge but as a governance challenge.

Sustainable AI adoption requires organisations to balance

innovation with continuous human capability development.

Failure to do so risks producing structural inequalities in ac-

cess to future employment, undermining organisational resi-

lience and weakening the legitimacy of AI-driven business

transformation. These ndings provide the conceptual foun-

dation for reconsidering whether employer-sponsored upski-

lling and reskilling should remain discretionary HR practices

or gradually evolve into components of a broader duty of

responsible workforce governance.

Labour law has traditionally recognised vocational trai-

ning as an important instrument for promoting productivi-

ty, professional development and labour market integration.

However, the legal foundations of employer-sponsored trai-

ning have historically been conceived within industrial pro-

duction systems characterised by relatively stable occupatio-

nal structures and gradual technological change. Under these

conditions, workforce training was generally understood as a

complementary organisational practice designed to improve

eciency rather than as a mechanism for protecting workers

against technological exclusion.

The acceleration of articial intelligence (AI) fundamen-

tally challenges these assumptions. Unlike previous tech-

nological transitions, AI continuously reshapes job content,

competency requirements and occupational trajectories, re-

ducing the useful life of existing knowledge while simulta-

neously creating demand for new technical, analytical and

socio-cognitive capabilities. As a result, employability can

no longer be viewed solely as the consequence of individual

educational attainment or professional experience; rather, it

increasingly depends on organisational decisions concerning

technology adoption, work redesign and access to conti-

nuous learning opportunities.

This transformation has signicant legal implications. The

employment relationship has long been characterised by

an inherent imbalance of economic and managerial power,

which justies the existence of protective labour legislation.

Employers determine organisational structures, production

processes, technological investments and strategic priorities,

whereas employees generally exercise limited inuence over

these decisions. Consequently, when organisations introdu-

ce AI systems that substantially modify skill requirements,

workers face risks that they neither initiate nor control. From

this perspective, the erosion of employability resulting from

technological transformation cannot reasonably be attributed

exclusively to individual responsibility.

Contemporary international legal and policy instruments

increasingly acknowledge the importance of lifelong lear-

ning as an essential component of decent work and sustai-

nable economic development. The ILO has consistently

emphasised that technological innovation should contribute

to inclusive labour markets by expanding access to skills de-

velopment throughout working life rather than reinforcing

existing inequalities (ILO, 2025). Similarly, the OECD ar-

J. Law Epistemic Stud. (2026) 4: e176

gues that governments, employers and workers share respon-

sibility for ensuring that technological progress translates

into inclusive labour market outcomes through continuous

investment in workforce capabilities (OECD, 2023). These

developments indicate an emerging international consensus

that employability constitutes a collective governance objec-

tive rather than a purely individual concern.

Nevertheless, a careful examination of the legal literatu-

re reveals that the concept of employer responsibility has

not evolved at the same pace as technological transforma-

tion. Existing doctrinal analyses predominantly address

employer obligations in relation to occupational health and

safety, equality, non-discrimination, consultation rights and

data protection. Although these areas remain indispensable,

they do not adequately address the consequences of AI-in-

duced skills obsolescence. In most jurisdictions, legal obli-

gations concerning employee training remain fragmented,

sector-specic or linked to contractual provisions, leaving

signicant uncertainty regarding whether employers bear

responsibility for maintaining workforce employability in

rapidly changing technological environments.

A parallel trend can be observed within strategic human

resource management research. Studies on upskilling and

reskilling generally demonstrate positive associations with

organisational performance, innovation capability, emplo-

yee adaptability and competitive advantage. However, these

practices are typically framed as voluntary strategic invest-

ments motivated by business objectives rather than as res-

ponsibilities derived from the governance of technological

change. Consequently, HRM scholarship and labour law

scholarship have developed largely in parallel, with limited

conceptual integration between organisational strategy and

legal accountability.

This separation generates an important theoretical gap. If

organisations decide to deploy AI technologies that substan-

tially alter the competencies required for continued employ-

ment, should they also assume responsibility for facilitating

workers’ adaptation to those changes? Current scholarship

oers no coherent framework capable of answering this

question. Existing approaches either treat training as a mana-

gerial choice or analyse labour rights without incorporating

the strategic realities of AI-driven organisational transfor-

mation. As a result, the relationship between technological

innovation, workforce capability development and employer

responsibility remains conceptually fragmented.

The present study argues that this fragmentation limits

both legal analysis and organisational practice. AI does not

merely introduce new technologies into existing workpla-

ces; it transforms the conditions under which employability

is created, maintained and potentially lost. Therefore, em-

ployer responsibilities should not be conned to preventing

unlawful dismissal or ensuring equal treatment after techno-

logical change has occurred. A more proactive approach is

required—one that recognises workforce capability develop-

ment as an integral component of responsible organisational

governance. This perspective shifts attention from reactive

legal protection to anticipatory institutional responsibility,

emphasising that sustainable technological transformation

depends on preserving employees’ capacity to participate

meaningfully in evolving labour markets.

Accordingly, the traditional concept of employer-spon-

sored training requires reinterpretation. Rather than being

viewed as an optional human resource practice or a discretio-

nary investment in productivity, continuous upskilling and

reskilling should be understood as governance mechanisms

that enable organisations to reconcile technological inno-

vation with social sustainability. This reconceptualisation

provides the theoretical foundation for the development of

the AI Employability Governance Framework, introduced in

the following section as a model that integrates labour law,

strategic HRM and corporate governance into a coherent

approach to responsible workforce transformation.

The preceding analysis demonstrates that existing scholar-

ship has generated substantial knowledge regarding articial

intelligence (AI), workforce transformation and strategic

human resource management. Nevertheless, these contribu-

tions remain conceptually fragmented. Labour law primarily

addresses workers’ protection once adverse consequences

materialise, whereas HRM literature focuses on organisatio-

nal capability development as a source of competitive advan-

tage. Corporate governance research, meanwhile, increasin-

gly emphasises human capital disclosure and sustainability

but rarely establishes explicit links between technological

transformation and employer obligations. Consequently, no

comprehensive framework currently explains how organisa-

tions should govern workforce capability development when

AI fundamentally reshapes the conditions of employability.

To address this gap, this article proposes the AI Emplo-

yability Governance Framework (AEGF). The framework

is grounded in the premise that employability in AI-enabled

organisations is not solely an individual asset or an organi-

sational resource; rather, it constitutes a shared governance

responsibility arising from the interaction between techno-

logical innovation, managerial decision-making and labour

protection. Under this perspective, employers do not merely

manage human resources; they also shape the institutional

conditions that determine whether workers can adapt to tech-

nological change without experiencing unnecessary exclu-

sion from employment.

Unlike traditional training models, which typically eva-

luate learning interventions according to productivity or

performance outcomes, the AEGF conceptualises upskilling

and reskilling as governance mechanisms that reconcile or-

ganisational innovation with social sustainability. The fra-

J. Law Epistemic Stud. (2026) 4: e176

mework therefore extends beyond competency development

by incorporating legal accountability, organisational fore-

sight and strategic workforce planning into a unied analyti-

cal structure.

The rst dimension, Strategic Workforce Foresight, re-

quires organisations to anticipate future competency needs

before implementing AI technologies. Rather than reacting

to technological disruption after it occurs, employers should

conduct systematic assessments of how AI will modify oc-

cupational proles, identify competencies at risk of obsoles-

cence and forecast emerging skill requirements. This anti-

cipatory approach reduces organisational uncertainty while

enabling more eective workforce planning.

The second dimension, Continuous AI Upskilling, recog-

nises that incremental technological change requires conti-

nuous updating of existing competencies. Employees per-

forming evolving job functions should receive structured

learning opportunities allowing them to integrate AI into

daily work practices while maintaining professional relevan-

ce. Upskilling therefore becomes an ongoing organisational

process rather than an occasional response to technological

innovation.

The third dimension, AI Reskilling Pathways, addresses

situations in which technological transformation fundamen-

tally alters occupational structures. In these circumstances,

incremental learning may no longer be sucient, requiring

employees to acquire entirely new competencies for alterna-

tive functions within or beyond the organisation. Eective

reskilling pathways reduce the social costs associated with

technological restructuring while preserving organisational

knowledge and workforce continuity.

The fourth dimension, Shared Employer Responsibility,

represents the principal legal innovation of the framework.

Traditional conceptions frequently portray employability as

an individual responsibility shaped by workers’ educational

choices and career decisions. However, where employers

initiate AI-driven organisational transformation, they si-

multaneously inuence the relevance of existing workfor-

ce capabilities. Consequently, responsibility for preserving

employability should be distributed more equitably between

employers, employees and public institutions. This does not

imply an unlimited legal obligation on employers but recog-

nises that technological governance entails corresponding

responsibilities regarding workforce adaptation.(Taheri Hos-

seinkhani, 2025)

The fth dimension, Labour Rights Protection, integrates

established labour law principles into AI-related workforce

governance. Continuous capability development should be

implemented consistently with equality, non-discrimination,

transparency, access to training opportunities and fair treat-

ment throughout employment. Particular attention should be

paid to vulnerable groups that may experience dispropor-

tionate barriers to technological adaptation, including older

workers, employees with lower digital literacy and workers

in sectors undergoing rapid automation. Embedding these

safeguards within workforce development strategies reinfor-

ces both legal compliance and organisational legitimacy.

Finally, the sixth dimension, Sustainable Employability

Outcomes, shifts evaluation beyond short-term productivity

indicators. The eectiveness of organisational upskilling and

reskilling initiatives should be assessed according to broader

outcomes, including employees’ capacity to remain emplo-

yable over time, internal career mobility, adaptability to te-

chnological change, organisational resilience and the sustai-

nability of workforce transformation. These outcomes align

organisational performance with broader social objectives

and support the integration of workforce capability develop-

ment into corporate sustainability strategies.

Taken together, these six dimensions form an integrated

governance system rather than a sequence of isolated HR

practices. Strategic foresight identies future competency

requirements; upskilling and reskilling provide mechanis-

ms for capability renewal; shared responsibility establishes

the normative allocation of obligations; labour rights ensure

fairness throughout implementation; and sustainable em-

ployability outcomes evaluate the long-term eectiveness

of organisational transformation. The framework therefore

bridges the traditional divide between legal protection and

strategic HRM by demonstrating that workforce capability

development constitutes both a governance function and a

mechanism for responsible technological transition.

From a theoretical perspective, the AEGF contributes

to the emerging literature on AI and employment in three

principal ways. First, it reconceptualises employability as

a governance construct rather than merely an individual la-

bour-market attribute. Second, it integrates labour law, stra-

tegic HRM and corporate governance within a single analyti-

cal framework, overcoming disciplinary fragmentation.

Third, it advances the argument that continuous upskilling

and reskilling should be interpreted as institutional respon-

ses to AI-induced workforce transformation rather than as

discretionary organisational practices. These contributions

provide a foundation for future empirical research exami-

ning how organisations operationalise responsible workfor-

ce governance across dierent legal systems and economic

contexts.

The AI Employability Governance Framework (AEGF)

extends current scholarship by positioning employability as

a governance issue situated at the intersection of strategic hu-

man resource management (SHRM), labour law and corpo-

rate governance. While previous studies have acknowledged

the importance of upskilling and reskilling in technologically

dynamic environments, these practices have generally been

J. Law Epistemic Stud. (2026) 4: e176

Figure 1. Employability Governance Framework (AEGF): An integrated Model for Responsible Workforce Transformation

examined from disciplinary perspectives that remain largely

disconnected. HRM research has primarily focused on their

contribution to organisational performance and competitive

advantage, whereas labour law has traditionally addressed

training as one among several mechanisms for promoting

workers’ professional development. Corporate governance,

in turn, has increasingly recognised human capital as a stra-

tegic asset, yet has devoted comparatively limited attention

to the institutional responsibilities associated with workforce

capability transformation in AI-enabled organisations.

The proposed framework contributes to overcoming this

fragmentation by arguing that workforce capability develo-

pment should be understood simultaneously as a strategic,

legal and governance responsibility. This interpretation

J. Law Epistemic Stud. (2026) 4: e176

is consistent with recent developments in strategic HRM,

which emphasise that organisational resilience depends on

the ability to continuously renew human capital in response

to environmental change (Minbaeva, 2023). However, the

AEGF advances this perspective by suggesting that capabi-

lity renewal should not be justied exclusively by producti-

vity or innovation objectives. Rather, it should also be eva-

luated according to its contribution to preserving equitable

access to employment opportunities and supporting socially

sustainable technological transitions.

From an HRM perspective, the framework encourages or-

ganisations to move beyond reactive training models. Con-

ventional approaches frequently design learning program-

mes after technological implementation has already altered

job requirements, creating delays between capability needs

and organisational responses. Such reactive practices increa-

se adaptation costs, reduce employee condence and may

generate avoidable workforce displacement. By contrast,

the AEGF advocates a proactive model in which workfor-

ce capability planning precedes technological deployment.

Integrating strategic workforce foresight with AI adoption

enables organisations to anticipate competency gaps, alloca-

te training resources more eciently and facilitate smoother

organisational transformation.

The framework also has important implications for la-

bour law. Contemporary labour legislation has historically

developed around corrective mechanisms that operate once

adverse employment consequences have materialised, inclu-

ding dismissal protection, compensation, anti-discrimination

guarantees and occupational health measures. Although the-

se safeguards remain essential, they are insucient to ad-

dress the preventive challenges associated with AI-induced

skills obsolescence. The AEGF therefore proposes a comple-

mentary perspective based on anticipatory protection. Under

this approach, the objective is not merely to remedy exclu-

sion after it occurs but to reduce the likelihood of exclusion

by ensuring that workers have meaningful opportunities to

adapt to technological change throughout the employment

relationship.

This preventive orientation aligns with broader internatio-

nal discussions concerning lifelong learning and sustainable

labour markets. Nevertheless, it does not imply the creation

of an unlimited employer obligation. Workforce capabili-

ty development should instead be interpreted according to

principles of proportionality, organisational capacity, secto-

ral characteristics and social dialogue. The precise scope of

employer responsibilities will inevitably vary across legal

systems, enterprise size and industrial contexts. Consequent-

ly, the framework should be understood as a normative go-

vernance model capable of informing future legislative de-

velopment rather than prescribing uniform legal obligations

applicable in every jurisdiction.

Corporate governance constitutes a third area in which the

framework generates signicant implications. Human capital

has become an increasingly important component of corpo-

rate sustainability and environmental, social and governance

(ESG) reporting. Investors, regulators and stakeholders now

expect organisations to disclose not only workforce compo-

sition but also talent development, learning investment and

long-term capability strategies. Within this context, AI-rela-

ted upskilling and reskilling represent more than operational

HR activities; they become indicators of responsible gover-

nance, organisational resilience and sustainable value crea-

tion. Organisations that systematically invest in workforce

capability development are therefore likely to strengthen

both stakeholder trust and long-term corporate legitimacy.

The framework further highlights the strategic relationship

between AI governance and organisational justice. Emplo-

yees are more likely to perceive technological transforma-

tion as legitimate when they are provided with transparent

information regarding organisational change, equitable

access to learning opportunities and realistic pathways for

professional adaptation. Conversely, introducing AI systems

without corresponding investments in workforce capability

development may generate perceptions of procedural un-

fairness, reduced organisational commitment and resistance

to technological innovation. Consequently, capability deve-

lopment functions not only as a mechanism for improving

productivity but also as an instrument for strengthening trust

between employers and employees during periods of techno-

logical transition.

An additional contribution of the AEGF concerns emer-

ging economies, where technological transformation often

occurs under conditions of institutional asymmetry and

unequal access to digital skills. In many Latin American

countries, AI adoption is advancing more rapidly than labour

market adaptation, creating signicant disparities between

highly qualied workers and those employed in traditional

occupations. Under these circumstances, continuous upski-

lling and reskilling acquire particular relevance as mecha-

nisms for reducing technological inequalities and supporting

inclusive economic development. Rather than replicating re-

gulatory approaches developed in high-income economies,

policymakers may utilise governance frameworks such as

the AEGF to design context-sensitive strategies that integra-

te economic competitiveness with social protection.

Despite these contributions, the framework should be in-

terpreted with appropriate caution. As a conceptual model, it

requires empirical validation across dierent legal systems,

sectors and organisational contexts. Future research should

examine how organisations operationalise strategic workfor-

ce foresight, the eectiveness of dierent upskilling and res-

killing strategies, employee perceptions of shared employer

responsibility and the relationship between workforce capa-

J. Law Epistemic Stud. (2026) 4: e176

bility development and organisational performance. Compa-

rative legal studies could also investigate whether existing

labour legislation already contains implicit foundations ca-

pable of supporting a broader duty to preserve employability

in AI-enabled workplaces.

Overall, the discussion indicates that AI Employability

Governance represents more than a new conceptual label. It

oers an interdisciplinary lens through which technological

innovation, workforce capability development and emplo-

yer responsibility can be analysed as mutually reinforcing

elements of responsible organisational transformation. By

integrating insights from HRM, labour law and corporate

governance, the framework contributes to a more coherent

understanding of how organisations can pursue AI-driven

innovation while preserving employability, organisational

legitimacy and long-term social sustainability.

The transition towards AI-enabled workplaces is no lon-

ger solely an organisational phenomenon; it has become a

public policy challenge with signicant implications for la-

bour market resilience, economic competitiveness and social

inclusion. While organisations remain the primary actors

responsible for introducing AI technologies into production

systems, the broader institutional environment—including

governments, educational institutions, social partners and

international organisations—plays a decisive role in shaping

the conditions under which workforce transformation oc-

curs. Consequently, eective AI Employability Governance

requires coordinated action extending beyond individual or-

ganisational initiatives.

One of the principal implications of the proposed fra-

mework concerns the evolution of labour regulation. Exis-

ting labour legislation in many jurisdictions continues to

regulate training through fragmented provisions, collective

agreements or sector-specic obligations. Such approaches

were developed for labour markets characterised by relati-

vely gradual technological change and are increasingly ina-

dequate in environments where AI can rapidly alter occupa-

tional structures and competency requirements.

Rather than imposing rigid statutory obligations applica-

ble to all employers, policymakers should consider adopting

regulatory models based on shared responsibility, proportio-

nality and preventive governance. Such approaches would

encourage organisations to anticipate workforce capability

needs while preserving sucient exibility to accommodate

dierences in enterprise size, industrial sector and technolo-

gical maturity. In this context, governments could promote

voluntary or mandatory workforce transition plans for or-

ganisations implementing AI at scale, integrating skills as-

sessment, training commitments and mechanisms for social

dialogue.

Public policy should also strengthen incentives for lifelong

learning through tax benets, co-nanced training program-

mes, public-private partnerships and national AI skills stra-

tegies. These measures would reduce disparities in access to

professional development while supporting labour market

adaptability and long-term economic productivity. Impor-

tantly, regulatory interventions should prioritise vulnerable

groups that face greater barriers to digital inclusion, thereby

ensuring that technological transformation does not reinfor-

ce pre-existing social inequalities.

For HR professionals, the AI Employability Governance

Framework suggests a fundamental shift from operational

training management to strategic capability governance. HR

departments should no longer view learning initiatives as

isolated responses to emerging technological needs but as

integral components of organisational strategy.

Strategic workforce planning should therefore incorporate

continuous monitoring of technological trends, systematic

identication of future competency requirements and early

assessment of occupations most susceptible to AI-driven

transformation. Integrating these processes into workforce

planning enables organisations to reduce capability gaps be-

fore they become operational constraints.

Furthermore, HR functions should develop transparent

criteria governing access to AI-related learning opportuni-

ties. Equitable participation in upskilling and reskilling pro-

grammes is essential to avoid reinforcing existing inequa-

lities associated with age, gender, educational background

or occupational status. Fair allocation of training resources

contributes not only to organisational eectiveness but also

to employees’ perceptions of procedural justice and institu-

tional trust.

The framework also encourages organisations to rede-

ne learning metrics. Traditional indicators such as training

hours or participation rates provide only limited insight into

organisational capability development. More comprehensive

evaluation systems should measure competency acquisition,

adaptability, internal mobility, innovation capacity and long-

term employability outcomes, thereby aligning learning in-

vestments with broader organisational objectives. (Badie &

Rostomyan, 2025).

Corporate governance has increasingly recognised human

capital as a strategic determinant of sustainable organisatio-

nal performance. Nevertheless, disclosure regarding work-

force capability development frequently remains descriptive

rather than strategic. The AEGF suggests that organisations

should integrate AI-related upskilling and reskilling into

corporate governance structures, assigning explicit oversight

responsibilities to boards or dedicated sustainability commi-

ttees.

This approach is consistent with the growing emphasis on

the social dimension of ESG, where investors and stakehol-

J. Law Epistemic Stud. (2026) 4: e176

ders increasingly evaluate organisations according to their

capacity to develop, retain and support human capital. Con-

tinuous investment in workforce capabilities demonstrates

commitment to responsible technological innovation, enhan-

ces organisational legitimacy and contributes to long-term

value creation.

In addition, organisations should incorporate AI emplo-

yability indicators into sustainability reporting frameworks.

Potential metrics include investment in AI-related training,

participation rates in reskilling programmes, internal mobili-

ty following technological restructuring, digital competency

development and employee perceptions of learning acces-

sibility. Such indicators would complement existing human

capital disclosures while improving transparency regarding

organisational preparedness for technological change.

Universities, vocational institutions and professional edu-

cation providers occupy a central position within the AI Em-

ployability Governance ecosystem. Traditional educational

models based on one-time qualication are increasingly in-

sucient in labour markets characterised by continuous te-

chnological disruption. Higher education institutions should

therefore strengthen collaboration with employers to design

exible curricula capable of responding to rapidly evolving

competency requirements.

Micro-credentials, modular learning pathways, stackable

qualications and industry-certied AI competencies repre-

sent promising mechanisms for supporting lifelong emplo-

yability. Collaboration between universities and organisa-

tions may also facilitate more accurate forecasting of future

workforce needs while reducing mismatches between educa-

tional provision and labour market demand.

Beyond technical competencies, educational programmes

should place greater emphasis on interdisciplinary capabi-

lities, including critical thinking, ethical reasoning, digital

literacy, problem-solving and collaborative skills. These

competencies are less susceptible to technological substitu-

tion and enhance workers’ capacity to adapt to future occu-

pational transitions.

The framework also reinforces the importance of social

dialogue in governing AI-enabled workforce transforma-

tion. Employers, trade unions and employee representatives

should participate actively in discussions concerning techno-

logical implementation, workforce capability requirements

and access to training opportunities. Such participation con-

tributes to greater transparency, strengthens organisational

trust and facilitates the design of workforce transition stra-

tegies that balance productivity objectives with labour pro-

tection.

Collective bargaining may play an increasingly important

role in formalising commitments regarding AI-related trai-

ning, reskilling pathways and support for employees aected

by technological restructuring. Rather than focusing exclusi-

vely on the consequences of automation, social dialogue can

become an anticipatory governance mechanism that enables

organisations and workers to jointly manage technological

change (Doellgast et al., 2025; Porcheddu & Prosdocimi,

2025)

Ultimately, the AI Employability Governance Framework

demonstrates that sustainable workforce transformation can-

not be achieved through isolated interventions. Organisatio-

nal development, labour regulation, corporate governance,

educational systems and public policy constitute interdepen-

dent components of a broader governance ecosystem. Eec-

tive responses to AI-driven skills disruption therefore require

coordination across institutional levels, ensuring that tech-

nological innovation is accompanied by equitable access to

learning, responsible organisational practices and regulatory

frameworks capable of supporting long-term employability.

From this perspective, employability should no longer be

understood solely as an individual characteristic or a market

outcome. Instead, it emerges as a collective governance ob-

jective requiring shared commitment from employers, wor-

kers, governments and educational institutions. Recognising

this broader institutional dimension provides the foundation

for more inclusive and sustainable models of technological

transformation, particularly in economies where digitalisa-

tion is progressing more rapidly than workforce capability

development

Conclusions

The rapid integration of articial intelligence into organi-

sational processes has fundamentally altered the relationship

between technological innovation, workforce capability de-

velopment and employer responsibility. Although previous

scholarship has generated valuable insights into AI adoption,

strategic human resource management and lifelong learning,

the intersection between these elds has remained conceptua-

lly fragmented. This article sought to address that limitation

by examining whether AI-induced workforce transformation

justies a broader interpretation of employer responsibilities

regarding continuous upskilling and reskilling.

The ndings indicate that technological transformation

should not be understood solely as a question of producti-

vity enhancement or digital innovation. Rather, AI increa-

singly determines the conditions under which employability

is created, maintained and, in some cases, eroded. Becau-

se organisations decide when and how AI technologies are

introduced, they also inuence the relevance of employees’

existing competencies and their capacity to adapt to chan-

ging occupational demands. Consequently, workforce capa-

bility development cannot be regarded exclusively as an in-

dividual responsibility but should be recognised as a shared

J. Law Epistemic Stud. (2026) 4: e176

governance objective involving employers, employees and

public institutions.

The principal theoretical contribution of this study is the

development of the AI Employability Governance Fra-

mework (AEGF). Unlike existing approaches that analyse

upskilling, reskilling or labour regulation independently, the

proposed framework integrates strategic human resource

management, labour law and corporate governance within a

single conceptual model. By combining strategic workforce

foresight, continuous capability development, shared emplo-

yer responsibility, labour rights protection and sustainable

employability outcomes, the framework oers a more com-

prehensive understanding of how organisations can govern

workforce transformation in AI-enabled environments.

From a labour law perspective, the article contributes to

ongoing debates regarding the evolution of employer obli-

gations in technologically intensive workplaces. Rather than

advocating the immediate creation of a universal statutory

duty to provide AI-related training, the study argues that

existing legal principles concerning decent work, equali-

ty, vocational development and social protection provide a

foundation for reinterpreting employer responsibilities in li-

ght of contemporary technological realities. This perspective

encourages a preventive rather than exclusively corrective

approach to labour regulation, emphasising capability deve-

lopment as a mechanism for reducing the risks of technolo-

gical exclusion before adverse employment outcomes occur.

For human resource management, the ndings reinforce

the importance of moving beyond reactive training models

towards anticipatory capability governance. Organisations

that integrate workforce foresight into strategic planning are

better positioned to anticipate emerging competency require-

ments, reduce skills obsolescence and enhance organisatio-

nal resilience. Continuous upskilling and reskilling should

therefore be considered strategic investments that contribute

not only to innovation and competitiveness but also to work-

force sustainability and institutional legitimacy.

The framework also oers important implications for cor-

porate governance and sustainability. As stakeholders increa-

singly evaluate organisations according to their management

of human capital, workforce capability development should

become an integral component of responsible AI governan-

ce and the social dimension of ESG strategies. Transparent

investment in learning, equitable access to development

opportunities and measurable employability outcomes can

strengthen organisational accountability while supporting

long-term value creation.

Despite these contributions, several limitations should

be acknowledged. The proposed framework is conceptual

and has not yet been empirically validated across dierent

legal systems or organisational settings. Furthermore, the

study primarily draws upon English-language literature

and international policy documents, which may not fully

capture regional legal traditions or institutional dierences.

Consequently, the transferability of the framework should

be examined with caution, particularly in jurisdictions cha-

racterised by distinct labour market structures or regulatory

environments.

Future research should therefore focus on empirically

testing the AI Employability Governance Framework in or-

ganisations operating across dierent sectors and countries.

Comparative legal studies could explore how existing labour

legislation accommodates employer responsibilities relating

to technological capability development, while quantitative

research may assess the relationship between AI-related in-

vestment in workforce development, employee adaptability

and organisational performance. Longitudinal studies would

also contribute to understanding how continuous upskilling

and reskilling inuence sustainable employability over time.

Finally, further investigation is needed to determine how go-

vernments, educational institutions and social partners can

cooperate in developing governance models that promote in-

clusive technological transitions in both advanced and emer-

ging economies.

In conclusion, this article argues that the future of work

should not be framed solely as a debate about articial in-

telligence but as a discussion concerning the governance of

human capabilities in technologically evolving societies. AI

will undoubtedly continue to reshape employment, yet the

long-term sustainability of this transformation will depend

not only on the sophistication of technological systems but

also on the institutional capacity to ensure that workers re-

main capable of participating meaningfully in changing la-

bour markets. In this context, AI Employability Governance

provides a promising conceptual foundation for rethinking

employer responsibility and advancing more inclusive, re-

silient and sustainable models of workforce transformation.

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Conicts of interest

The authors declare that they have no conicts of interest..

Author contributions

Joel Menezes-Barreto Júnior: Conceptualization, metho-

dology, supervision, project administration, writing – review

& editing. Duliet Hong: Investigation, data curation, formal

analysis, validation, writing – review & editing. Jaime E.

Pérez-Fernández: Conceptualization, formal analysis, me-

thodology, visualization, writing – original draft, writing

– review & editing. António Ramos-Congo: Investigation,

literature review, data curation, validation, writing – review

& editing. Natalia A. Domingas-Chivango: Investigation, vi-

sualization, formal analysis, validation, writing – review &

editing. All authors have read and approved the nal version

of the manuscript.

Data availability statement

The datasets used and/or analyzed during the current study

are available from the corresponding author on reasonable

request.

Statement on the use of AI

The authors acknowledge the use of generative AI and

AI-assisted technologies to improve the readability and cla-

rity of the article.

Disclaimer/Editor’s note

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cations are solely those of the individual authors and con-

tributors and not of Journal of Law and Epistemic Studies.

Journal of Law and Epistemic Studies and/or the editors

disclaim any responsibility for any injury to people or pro-

perty resulting from any ideas, methods, instructions, or pro-

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