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    },
    {
        "idx": 2,
        "result": "unsupported"
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<reference>
The Future of Work in the Age of Artificial
Intelligence
Michaela Doubková1
Abstract: The research investigates how artificial intelligence (AI) reshapes labour markets,
focusing on job composition, evolving skill demands, and changes in HR practices. The
analysis reveals that AI can simultaneously promote efficiency and the creation of new highskill positions while exacerbating labour market polarization and eroding stable employment
forms. The scope and intensity of these effects vary significantly across sectors, occupational
categories, and institutional environments. The results highlight that techno-logical progress
is not inherently inclusive; rather, its impact depends on how technology is integrated into
organisational and social frameworks. Employee involvement, training and retraining
systems, and effective social dialogue emerge as key determinants of equitable adaptation.
The study concludes that the governance of technological change, rather than technology
itself, will shape the future configuration of labour markets.
Keywords: Labour Market, Artificial Intelligence, Future of work
JEL Classification: E24 · E32 · J24 · L86 · O15 · O29
1 Introduction
Artificial intelligence (AI) has become a catalyst of structural transformation in the labour market, influencing not only
productivity but also the very nature of employment relations. While its early diffusion was concentrated among large IT
corporations, smaller and younger firms are increasingly adopting AI tools, expanding innovation capacity across diverse
sectors such as services, research, and business (Damioli et al., 2024). The IT industry remains the most dynamic
environment for observing these changes, where automation, algorithmic management, and outsourcing reshape work
organisation and job stability (Doellgast, 2023). At the same time, AI’s capacity for data-driven insight, for instance in
predicting employee turnover through advanced models like LightGBM and logistic regression, illustrates its potential to
enhance workforce planning and retention (Lazzari et al., 2022). However, the transformation extends beyond mere
automation. The emergence of advanced and general AI enables convergence between previously separate fields—such as
healthcare and data science—resulting in new professional domains that prioritise interdisciplinary expertise, creativity,
and adaptive thinking (Giacomoni, 2022). Thus, AI operates simultaneously as a driver of efficiency and as an “enabling
technology” that redefines qualifications, competences, and the structure of work itself.

2 Methods
This study employs a systematic qualitative literature review combined with thematic analysis to examine how artificial
intelligence (AI) affects labour markets, employment structures, skill requirements, and human resource management
practices. The objective is not merely to summarise existing research but to identify recurring patterns, dominant narratives,
and points of divergence across empirical and conceptual studies. The methodological approach follows the principles of
structured literature reviews and is informed by the PRISMA 2020 guidelines to ensure transparency and reproducibility.
The literature search was conducted using the Web of Science Core Collection database, which was selected due to its
rigorous indexing standards and broad coverage of high-quality peer-reviewed journals in economics, management, and
social sciences. The review focuses on articles published between 2020 and 2025, reflecting the period of accelerated AI
diffusion and its increasing relevance for labour market research. Only publications written in English were considered.
The search strategy was designed to capture studies addressing both technological and socio-economic dimensions of
AI. Boolean operators were used to combine keywords related to artificial intelligence and labour market outcomes. The
core search query included combinations of the following terms:

1 University of South Bohemia in České Budějovice, Faculty of Economics, Faculty Department of Applied Mathematics and Informatics

Studentská 13, 370 05 České Budějovice, Czech Republic, mdoubkova@ef.jcu.cz

132

•

“artificial intelligence”, “AI”, “machine learning”, “natural language processing”, “algorithmic management”,
“digital transformation”,
AND

•

“labour market”, “employment”, “future of work”, “human resource management”, “skills”, “labour market
dynamics”.

The search was conducted across titles, abstracts, and author keywords to ensure comprehensive coverage. Initial searches
yielded more than 3,000 publications.
To ensure relevance and methodological quality, explicit inclusion and exclusion criteria were applied. Studies were
included if they:
•

Explicitly addressed the impact of AI or related technologies on employment, skills, working conditions, or HR
practices,

•

Were empirical studies, systematic literature reviews, or theoretically grounded conceptual papers,

•

Were published in peer-reviewed academic journals.

Studies were excluded if they:
•

Focused exclusively on technical or engineering aspects of AI without socio-economic implications,

•

Were conference proceedings, editorials, commentaries, or non-peer-reviewed publications,

•

Did not meet basic quality standards in terms of methodological clarity and scholarly contribution.

The selection process followed a multi-stage screening procedure consistent with the PRISMA framework. After removing
duplicates, titles and abstracts were screened for relevance. The remaining articles were subjected to full-text review, during
which the inclusion and exclusion criteria were applied more rigorously. This process resulted in a final sample of 39
studies, which formed the analytical basis of the review.
The selected studies were analysed using qualitative thematic analysis. Each article was systematically coded, and recurring
themes were identified and grouped into higher-order analytical categories. Five core dimensions emerged from the
analysis:
•

Employment effects (job creation, displacement, and polarisation),

•

Skill transformation and changing qualification requirements,

•

Applications of AI in human resource management and workplace organisation,

•

Sectoral and occupational heterogeneity of AI impacts,

•

Institutional, regulatory, and governance factors shaping outcomes.

The analysis focused on identifying cross-study patterns, areas of convergence and divergence, and contextual factors
influencing AI’s labour market effects. Rather than evaluating individual studies in isolation, the synthesis emphasises how
findings collectively contribute to understanding the future of work in the age of artificial intelligence.

3 Research results
3.1 Artificial Intelligence Applications
Machine learning (ML) represents one of the core pillars of artificial intelligence and has become an indispensable tool
for analysing complex data structures and predicting dynamic processes. Beyond traditional supervised and unsupervised
learning, recent progress in reinforcement learning and particularly deep reinforcement learning, has enabled adaptive
decision-making even under uncertain or incomplete data conditions (Shi et al., 2023). ML techniques are widely applied
across domains (from healthcare and genomics to business intelligence), supporting prediction, classification, and causal
inference. Meaningful integration of these methods requires a solid methodological framework encompassing data
preprocessing, model tuning, and validation, while ensuring transparency, replicability, and ethical integrity (Cho et al.,
2024). Another rapidly advancing field is Natural Language Processing (NLP), which focuses on enabling machines to

133

understand and generate human language. With the advent of deep learning and large-scale pre-trained models such as
BERT and GPT, NLP systems have achieved remarkable progress in translation, sentiment analysis, and automated
reasoning (Lauriola et al., 2022). In parallel, recommender systems have become an essential AI application in enterprise
and HR information systems, where algorithms personalise recommendations for products, learning content, or career
development based on user similarity or hybrid data-driven approaches (Yassine et al., 2021). Together, these technologies
demonstrate how AI is not only automating routine processes but also expanding decision-making and analytical
capabilities across organisational and social contexts.

3.2 AI in the Workplace
AI-driven automation is perceived differently across organisational hierarchies. Management emphasizes efficiency
gains and strategic renewal, whereas employees more often associate it with job insecurity, stress, and upskilling pressure
(Gómez Gandía et al., 2025). This asymmetry signals that digital transformation is not merely a technical upgrade but a
socio-organisational process that reshapes worker–firm relations. Sustainable adaptation depends on participatory
introduction, competence development, and transparent communication about technological change (Gómez Gandía et al.,
2025). Human capabilities remain central. Social skills, domain know-how, and experience, combined with technical and
cognitive skills, create complementary profiles that are difficult to decompose or fully automate, preserving the value of
human interaction and adaptability even in AI-rich settings (Das, 2021). In HR, AI tools (ML, predictive analytics, chatbots)
now support workforce planning, talent management, performance appraisal, and learning, shifting HR from an
administrative burden to a strategic partner (Benabou & Touhami, 2025). Chatbots streamline recruiting and internal
communications but still struggle with complex queries and require maintenance (Majumder & Mondal, 2021). Despite
productivity improvements and faster decisions, full substitution of human labour is constrained by data quality, technical
limits, and market conditions. The extent to which AI replaces or complements HR activities depends largely on market
conditions and organisational strategy. In highly standardised markets, AI tends to substitute routine human tasks, whereas
in more complex or innovation-driven environments, it often complements human expertise and supports hybrid
collaboration models. Industry demand, strategic priorities, and available resources ultimately determine which of these
approaches prevails (Das, 2021). Ethical and distributional issues accompany these gains. Automation of routine tasks can
displace workers and create reskilling needs while opening new roles in AI oversight and strategic decision-making
(Benabou & Touhami, 2025).
At the micro level, AI can raise engagement and performance by offloading routine work; at the firm level, it tends to
lower costs and improve competitiveness but may also heighten uncertainty, stress, and perceived substitution risks, calling
for robust change management and socio-technical interventions (Budhwar et al., 2022). At the macro level, digital
transformation is positively associated with economic growth, labour productivity, and overall employment, particularly
among women. However, its impact on vulnerable forms of employment remains limited unless supported by adequate
social and legal protection measures (Aly, 2022).
AI adoption remains uneven across the economy. In 2017, only 5.8% of U.S. firms reported using AI, but the employeeweighted rate reached 18.2%, showing that adoption is concentrated in large enterprises and fast-growing startups capable
of turning AI into process innovation. This unequal diffusion risks widening productivity and wage gaps between digital
leaders and lagging firms. Adoption intensity also differs by sector. Information technology, healthcare, and manufacturing
are at the forefront, while retail, transport, construction, and agriculture remain behind, making the effects on employment,
skills, and inequality highly sector-dependent (McElheran et al., 2024). The impact of digital technologies varies by type.
Exposure to robotics and traditional software is often linked to poorer working conditions, whereas AI adoption tends to
improve job prospects by shifting focus from manual substitution to support for high-skilled tasks. However, deep
participation in global value chains is still associated with weaker labour conditions and persistent gender disparities,
particularly disadvantaging women and younger workers (Parteka et al., 2024); Serrano, 2025).
Current research indicates that augmentation, rather than full automation, dominates in knowledge-intensive sectors. AI
primarily complements human labour (especially in research and development) by enhancing creativity, innovation, and
decision-making, while complete automation remains concentrated in manufacturing. Job roles are being redefined rather
than eliminated, increasing the value of adaptability and human–AI collaboration (Johnson et al., 2022). The balance
between substitution and complementarity depends on economic and social conditions. Rising demand can strengthen
complementarity, leading to higher wages and job creation (Das, 2021). The demand for AI-related skills has surged across
industries, particularly in high-value, innovation-driven firms, which reward these competencies with wage premiums,
whereas routine service roles are more prone to automation (Alekseeva et al., 2021).

134

Human resource management is undergoing a major transformation as AI reshapes recruitment, learning, and employee
engagement. Hiring increasingly relies on AI-driven sourcing and screening, training shifts toward micro-learning, and
engagement uses gamification (Kraus et al., 2023). Current AI technologies fall into the category of “weak AI,” meaning
they execute specialised functions based on predefined algorithms without autonomous reasoning or general understanding.
“Strong AI,” envisioned as human-level intelligence capable of independent learning, remains theoretical. Consequently,
AI adoption currently leads to partial automation of routine activities while stimulating demand for highly skilled
professionals able to design, manage, and collaborate with such systems (Zhang, 2023). Tools such as AR and VR can
support workforce adaptation by enhancing training and real-time learning (Harborth & Kümpers, 2022). However,
digitalization also raises concerns about privacy, oversight, and motivation (Shahid et al., 2025). Persistent labour costs
following AI adoption suggest a complementary human–technology dynamic that heightens the demand for skills and
continuous organisational learning (Wang & Qiu, 2023).

3.3 Towards an Inclusive Future of Work
The digital transformation of work is redefining skill requirements. In the early stages, demand centres on technical
competencies, but as AI systems become more complex, domain expertise, interdisciplinary collaboration, and decisionmaking skills gain importance (Theben et al., 2023). Employers increasingly prioritise practical “hard skills” such as
Python, SQL, and machine learning over formal qualifications, while traditional analytical roles decline due to automation
(Liu et al., 2024). However, adoption remains uneven across the workforce. Highly educated employees adapt more easily
to AI-driven change, whereas low-skilled positions face greater risks of substitution, deepening wage disparities (Zhang,
2023). To ensure inclusive adaptation, labour market policies must focus on targeted support, adaptive skill development,
and continuous reskilling for vulnerable groups (Carbonero et al., 2023).
Evidence from China’s SMEs indicates stable overall labour demand, with low-skilled positions being replaced by
automation while demand for AI-skilled professionals rises, reflecting simultaneous job displacement and creation (Xu et
al., 2024). AI competencies carry a wage premium of around 21%, highlighting their growing market value(Stephany &
Teutloff, 2024). To ensure inclusive growth, policy efforts should focus on expanding digital infrastructure, strengthening
education and lifelong learning, and fostering collaboration between industry and training institutions (Shen, 2024;
Badulescu et al., 2025; Theben et al., 2023); At a broader scale, AI’s employment effects vary by institutional and
innovation capacity. In the EU, job growth linked to AI is concentrated in advanced innovation economies, while lessdeveloped regions see weaker benefits (Guarascio & Reljic, 2025). These asymmetries confirm that AI outcomes are shaped
by political, economic, and organisational factors rather than technological determinism. In developing economies,
digitalisation supports productivity and female employment but has limited influence on vulnerable work without
complementary social protections (Howcroft & Taylor, 2023).
Effective AI diffusion depends on economic coordination and governance. Rather than taxing automation, targeted
subsidies and coordinated state support can promote balanced adoption adapted to each country’s technological maturity
(Ünveren et al., 2023). Maintaining human oversight in AI-driven decision-making ensures that efficiency is balanced with
ethical and contextual judgment, while education systems and employers must foster adaptability, agility, and lifelong
learning to manage technological change (Ashta & Herrmann, 2021). Broader policy initiatives such as basic income,
retraining programs, and automation taxes reflect concerns about preserving autonomy, accountability, and decent work in
algorithmic management(Santoni de Sio, 2024).
The functioning of labour markets remains closely tied to education and skill formation: employment outcomes improve with digital and communication competencies, highlighting the need for curricula aligned with the digital economy
(Crețu et al., 2025). In recruitment, AI reduces costs and bias, but talent shortages persist; low labour supply elasticity
underscores the importance of internal reskilling and strong education–industry partnerships as key strategies for sustainable workforce development ((Black & van Esch, 2020); (Duch-Brown et al., 2022); (Theben et al., 2023)).

135

4 Conclusions
This paper set out to analyse how artificial intelligence reshapes labour markets by synthesising evidence from 39 peerreviewed studies published between 2020 and 2025. Rather than treating AI as a uniform technological force, the analysis
reveals a highly differentiated set of impacts shaped by sectoral conditions, organisational strategies, skill structures, and
institutional environments.
Across the reviewed literature, a consistent pattern emerges that artificial intelligence predominantly augments human
labour in knowledge-intensive and innovation-driven sectors, while substitution effects are concentrated in routine,
standardized, and low-skilled tasks. The analysed studies do not support a narrative of widespread technological
unemployment. Instead, they point to job transformation and task reconfiguration as the dominant outcome, with
employment effects varying significantly across occupations and industries.
A second robust finding concerns skill transformation. The reviewed studies converge on the conclusion that AI
adoption increases demand for advanced technical competencies while simultaneously elevating the importance of
complementary human skills such as problem-solving, creativity, domain expertise, and social interaction. At the same
time, the literature highlights a growing risk of labour market polarisation, as highly educated workers adapt more easily
to AI-driven change, whereas low-skilled workers face higher displacement risks unless supported by reskilling and lifelong
learning systems.
In the domain of human resource management, the analysis shows that AI primarily functions as a supportive and
enabling tool rather than a full substitute for human decision-making. AI applications in recruitment, performance
management, and learning systems improve efficiency and data-driven insight but remain constrained by ethical concerns,
data quality, and contextual judgment requirements. The reviewed studies consistently emphasize that the effectiveness of
AI in HR depends on organisational governance, transparency, and employee involvement.
At the macro level, the literature analysed in this review indicates that AI adoption is generally associated with
productivity growth and, in some contexts, net employment gains. However, these positive effects are unevenly distributed,
benefiting primarily advanced innovation economies and large firms with sufficient absorptive capacity. Without
complementary institutional frameworks such as active labour market policies, education reform, and social protection
mechanisms, AI diffusion risks reinforcing existing inequalities rather than mitigating them.
Overall, the findings of this review confirm that technology itself is not the decisive factor shaping labour market
outcomes. Instead, outcomes are mediated by governance structures, organisational choices, and the capacity of societies
to invest in human capital and institutional adaptation. The future of work in the age of artificial intelligence will therefore
depend less on the pace of technological innovation and more on how effectively economic actors manage its integration
into social and organisational systems.
By systematically synthesising recent empirical and conceptual research, this study contributes to a more nuanced
understanding of AI’s labour market effects and highlights the need to move beyond deterministic narratives. Future
research should focus on comparative empirical analyses across sectors and regions, as well as longitudinal studies
capturing the long-term consequences of AI-driven transformation for employment quality, inequality, and work
organisation.

Acknowledgement
The author gratefully acknowledges the institutional support of the Faculty of Economics, University of South Bohemia.

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</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. Task automation refers to the direct substitution of human labor by AI in routinized or pattern-based tasks; productivity augmentation occurs when AI enhances worker efficiency, allowing fewer workers to produce the same output; role consolidation enables senior or highly skilled workers to absorb tasks previously distributed across junior staff; and platformization creates new forms of algorithmic management, gig work, and digital labor mediation.
4. Evidence from online labor markets further indicates a 2–21% reduction in posting volumes for automatable creative tasks following the release of ChatGPT, suggesting that AI is already displacing certain categories of cognitive gig work.
5. This pattern mirrors earlier computerization waves but extends more deeply into cognitive and service sectors due to AI’s capacity to handle unstructured data, natural language, and complex classification tasks.
6. This shift challenges the traditional assumption that high-skilled workers are uniformly insulated from automation and complicates conventional skill-biased technological change narratives.
7. At the same time, AI systems can support new forms of collaboration, knowledge sharing, and decentralized decision-making, depending on how they are integrated into organizational processes.
8. AI-based reputation systems and automated moderation further shape worker-client interactions and can entrench inequalities or biases if not carefully designed.
9. 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.
10. 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.
11. These features collectively position AI as a qualitatively different driver of labor market restructuring within the Fourth Industrial Revolution.
12. The literature therefore frames AI’s restructuring impact as contingent and path-dependent, shaped by cumulative policy and organizational decisions rather than determined solely by technical capabilities.
13. Collective bargaining and social dialogue around AI adoption can help ensure that productivity gains are shared and that job redesign favors augmentation over displacement.
14. Third, aggregate employment impacts appear modest so far, but distributional consequences in terms of inequality and polarization are substantial and likely to intensify without supportive institutions and policies.
15. 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.
16. Another gap relates to detailed occupational and regional analyses in emerging and developing economies, where data limitations hinder precise measurement of AI exposure and impacts.
</statements>

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