You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

Impact of Artificial Intelligence on job Automation
and Employment
Pranali Kubade, Pramod dhamdhere, pranav navale, pranav pawar, H. R. Kulkarni, Sonika Kamthe*

* Author for Correspondence, Email: sonikadalvi9@gmail.com
G H Raisoni College of Arts, Commerce & Science Pune, Maharashtra India.

Abstract:
This paper critically examines the dual-sided structural transformation of global labor markets
resulting from the adoption of Artificial Intelligence (AI) and automation. Employing a rigorous
Systematic Literature Synthesis and Expository Research Design, the study synthesizes 25 authoritative
academic and institutional sources to analyze the core tension between job displacement (Automation
Effect) and job augmentation/creation (New Tasks Framework). The analysis confirms that while AI
automates routine tasks, the recent emergence of Generative AI (GenAI) has fundamentally shifted the
risk to high-skilled cognitive labor, simultaneously highlighting its potential to democratize productivity.
The results reveal a significant exacerbation of economic inequality and labor market polarization,
necessitating an urgent and transformative skill shift towards human-centric capabilities. The paper
concludes that the net outcome of the AI transition hinges on proactive policy interventions, including
educational reform and the regulation of Algorithmic Management, to ensure the benefits of this General
Purpose Technology lead to inclusive growth rather than widening societal gaps. Future work is
recommended to focus specifically on the differential impact and policy efficacy within developing
econom.
Keywords: Artificial Intelligence (AI), Automation, Labor Market Polarization, Generative AI (GenAI),
Economic Inequality.
1. Introduction
The Emergence of AI as a General Purpose Technology (GPT)
The integration of Artificial Intelligence (AI) and autonomous systems has instigated the most
profound structural shift in global labor markets since the dawn of the Information Age. AI, particularly
following breakthroughs in deep learning and computational power, has fundamentally established itself
as a General Purpose Technology (GPT)[8] akin to electricity or the steam engine. Its pervasive nature
means its innovations are applicable across virtually all sectors of the economy [23]. This transformative
capacity leads to expectations of massive productivity gains[21] and economic growth, but simultaneously
generates deep societal anxieties regarding the future of employment. This paper addresses the core
tension between technological progress and its socio-economic consequences.
The Core Tension: Automation vs. Augmentation The academic and policy discourse surrounding AI and labor
is polarized by two distinct, yet interacting, economic mechanisms:
1. The Displacement Mechanism (Automation Effect)
The primary concern centers on job displacement—the direct substitution of human labor by intelligent
machines. The foundational work by Frey and Osborne (2017) [5]used a task-based methodology to
quantify this risk, concluding that a substantial portion of employment, particularly jobs characterized by
routine and predictable tasks, is highly susceptible to computerization. Institutional forecasts, such as those
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 255

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

by the McKinsey Global Institute (2017)[6], corroborate this by projecting the large-scale necessity for
millions of workers to transition into entirely new occupational categories by 2030. Furthermore, empirical
economic analysis, notably by Acemoglu and Restrepo (2020) [12], has provided concrete evidence
demonstrating the inverse relationship between the adoption of industrial robots and local labor market
employment and wage levels. This line of research suggests that in the short-to-medium term, the
displacement effect can be a powerful force contributing to unemployment in specific demographics[2].
2. The Augmentation and Productivity Mechanism (Creation Effect)
A critical counter-narrative, often supported by economic history, emphasizes the concept of labor
augmentation and new task creation. Acemoglu and Restrepo (2019)[9] formalized this framework,
arguing that technological progress generates entirely new work processes, products, and services that
necessitate new forms of human labor, offsetting displacement. Brynjolfsson and McAfee
(2014)[1]highlighted that AI's true value is unlocked when it acts as a complement, not a substitute, to
human intelligence, allowing workers to focus on tasks requiring creativity, judgment, and emotional
intelligence[7]. Therefore, while AI automates tasks, it often redefines and amplifies the complexity and
value of the remaining jobs.
The New Frontier: Generative AI and Skill Polarization
The advent of Generative AI (GenAI), including Large Language Models (LLMs), marks a critical
inflection point, fundamentally changing the risk profile of workers.
Impact on Cognitive Labor: Earlier automation primarily targeted manual and low-skilled routine
tasks. GenAI, however, is disrupting high-skilled cognitive labor (e.g., programming, legal analysis, content
creation)[17]. The IMF (2024) [22] highlighted that while roughly 40% of global jobs are exposed to AI,
advanced economies face greater exposure among high-skill roles.
Worsening Inequality: This shift contributes significantly to skill polarization and potentially exacerbates
economic inequality[13]. Workers who possess the advanced digital skills to effectively utilize GenAI
experience substantial productivity gains [18], leading to higher wages, while those unable to adapt face
stagnation or displacement. The resultant pressure for continuous reskilling and upskilling is a paramount
challenge for both governments and educational institutions[15].
Algorithmic Management: Furthermore, the workplace structure itself is being transformed by
Algorithmic Management, where AI oversees performance and decision-making [24], raising ethical and
well-being concerns regarding worker autonomy and stress.
Research Problem and Objectives:
Despite extensive research on AI's impact in advanced economies, there remains a critical need for
synthesized analysis that fully integrates the dual mechanisms and the recent GenAI disruptions.The
primary objective of this paper is to critically examine the net effect of AI-driven automation on
employment structure and propose comprehensive policy responses. Specifically, this research aims to.
Synthesize the evidence on the extent of job displacement versus augmentation across various
occupational categories.Analyze the immediate and future demands for skill transformation in the
workforce.Propose evidence-based policy interventions (e.g., in education, labor law, and social safety nets)
required to ensure the benefits of AI-driven productivity are broadly shared and lead to inclusive job
creation [19].

2. Literature Review
This literature review systematically synthesizes the key academic, institutional, and policy perspectives
regarding the impact of Artificial Intelligence (AI) and automation on job markets and employment
structure. The discussion is structured around three dominant themes: the foundational economic theory of
displacement versus augmentation, the changing nature of required human capital and skill sets, and the
disruptive influence of Generative AI.
The Foundational Economic Conflict: Displacement vs. Augmentation
The core academic conflict in this domain is the net effect of AI—whether the jobs it destroys are offset by
the jobs it creates.
A. The Displacement Hypothesis and Quantified Risk
The most impactful work quantifying the threat of automation remains Frey and Osborne (2017) [5], who
used a sophisticated methodology to estimate that approximately 47% of total US employment is at high
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 256

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

risk of computerization, with this susceptibility concentrated in occupations involving routine cognitive
and manual tasks. Supporting this quantification, the McKinsey Global Institute (2017) [6] projected a
massive global labor shift, estimating that between 400 and 800 million global jobs could be displaced by
2030, necessitating large-scale career transitions. From an empirical economic standpoint, Acemoglu and
Restrepo (2020) 1[12] provided robust evidence on the causal link between the adoption of industrial
robots and a decline in both employment and wages in specific US labor markets, highlighting the
concrete impact of automation on reducing labor demand.2 This perspective fuels the structural pessimism
articulated by Martin Ford (2015) [4] in Rise of the Robots, which questioned whether traditional economic
mechanisms can manage such rapid technological displacement.
B. The Augmentation Mechanism and New Task Creation
The counter-argument is anchored in historical labor market resilience. Autor (2015) 3[2] challenged the
notion of mass technological unemployment by arguing that human labor retains a non-routine,
complementary component that resists automation. 4 This was formalized by Acemoglu and Restrepo
(2019) [9] with the "New Tasks" framework, which posits that technological change is a source of new
task creation that sustains labor demand. The work of Brynjolfsson and McAfee (2014) [1] popularized
the necessity of "running with the machine," arguing that the true value of AI is realized when it
complements human intellect, leading to higher productivity and value-added work. Daugherty and
Wilson (2018) [7] reinforced this through the Human+Machine approach, emphasizing that AI augments
human decision-making and performance rather than merely substituting for it. Furthermore, Trajtenberg
(2018) [8] analyzed AI as a powerful General Purpose Technology (GPT) whose pervasive economic
impact[10] drives overall economic growth [21] far beyond immediate job losses. Human Capital
Transformation and Policy Imperatives The literature is unanimous: the composition of skills required
for work will fundamentally change, driving both opportunities and economic inequality.
A. The Inevitable Skill Shift
The economic consequence of task automation is a profound skill shift. AI automates routine tasks and
simultaneously raises the premium on non-routine cognitive skills like complex communication,
creativity, and critical thinking 5[19].6 The OECD (2023) 7[20] and Felten (2021) 8[14] underscored that
continuous upskilling and reskilling is no longer optional but a necessity for economic survival, a
challenge particularly acute for older or less-educated workers.9 The urgency of this educational response
is highlighted by Lamb and Jones (2022) [15] and summarized in the comprehensive review by Georgieff
and Wanner (2022) [16], who document the widespread nature of these skill demands. Tacke & Spatt
(2024) [23] specifically examine the 'AI-readiness' of the workforce, linking preparedness to successful
integration.
B. Economic Polarization and Inequality
The key danger is that productivity gains from AI may not be shared equally. Korinek and Stiglitz
(2021) 10[13] provided a detailed analysis of how AI can lead to severe income inequality if its benefits
primarily accrue to capital owners and a small elite of high-skilled workers.11 This risk of polarization is
echoed by the IMF (2024) [22], which noted that while AI exposure is high globally, the potential for wage
increases is concentrated among those who can effectively leverage the technology. Autor, Mindell, &
Reynolds (2023) 12[19] argue that the challenge is not just the quantity of jobs, but building "better jobs"
that offer security and dignity in the intelligent machine age. 13
C. The Rise of Algorithmic Management
Beyond direct task automation, AI is transforming managerial practices. 14 The European Parliament
(2025) [24] drew attention to Algorithmic Management, where AI systems execute core supervisory
functions, including monitoring, scheduling, and performance evaluation. This practice raises critical
ethical and policy concerns regarding the erosion of worker autonomy, increased work intensity, potential
algorithmic bias, and its adverse impact on worker well-being and mental health.15
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 257

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

The Disruptive Force of Generative AI (GenAI)
The latest wave of AI, GenAI, has shifted the focus of automation risk from the factory floor to the office
desk, fundamentally altering previous assumptions about job security.16
Targeting Cognitive Labor: The most salient finding regarding GenAI is its impact on white-collar tasks.
Eloundou et al. (2023) [17] found that Large Language Models (LLMs) significantly expose professional
occupations (e.g., lawyers, writers, programmers) to automation, a stark contrast to previous waves of
automation. Similarly, the Stanford University Digital Economy Lab (2025) 17[25] observed that entry-level
employment for young workers in highly AI-exposed cognitive roles has experienced a significant relative
decline, suggesting that entry-level barriers for white-collar work are being automated.18
Productivity Gains and Skill Floor: Conversely, Noy and Zhang (2023) 19[18] provided strong
experimental evidence that GenAI delivers substantial productivity gains, particularly for lessexperienced workers, effectively lowering the skill requirement (the "skill floor") for performing complex
cognitive tasks.20 This finding indicates that GenAI could democratize access to high productivity,
though this benefit must be balanced against the potential for job compression. 21
Synthesis and Research Gap
The synthesized literature unequivocally establishes that AI represents an inevitable and dualedged economic force. The transition is characterized by a shift from the automation of routine manual
tasks to the augmentation and automation of cognitive tasks. While the economic literature provides robust
models for this transition in technologically advanced economies [5], a critical research gap remains in
comprehensively analyzing the specific net effects and optimal policy frameworks (including educational
reform [19] and worker regulation [24]) required for developing economies. This paper aims to contribute
to filling this gap by synthesizing these global findings into actionable policy insights relevant to contexts
with different labor market rigidities and social safety net structures.

3. Methodology
This research paper employs a rigorous and systematic approach to analyze the complex impact of
Artificial Intelligence (AI) and automation on global employment structures. Given the broad scope of the
research—integrating economic theory, technological projections, and policy analysis—a Systematic
Literature Synthesis and Expository Research Design were adopted.
Research Design and Rationale
A. Expository and Descriptive Design
The study utilizes an Expository Research Design to explain the causal relationships between the
independent variable (AI and automation adoption) and the dependent variables (changes in employment
levels, wage polarization, and skill demands). It is fundamentally descriptive, aiming to interpret existing
data rather than generating new empirical data. The primary objective is to move beyond simple
description to offer a robust synthesis and interpretation of the diverse findings across major academic and
institutional sources.
B. Rationale for Secondary Data
The research relies exclusively on secondary data (peer-reviewed articles, working papers, and
authoritative institutional reports) [5]. This choice is justified because:
Complexity and Scale: The phenomenon of AI's impact on global labor is too vast and complex for a
primary data study to capture comprehensively.
Projections: Analyzing future employment scenarios inherently requires the use of established
economic and econometric models already published by leading institutions [12].
Synthesis Requirement: The core conflict (displacement vs. augmentation) requires synthesizing
established theories (e.g., the Task-Based Framework [2]) and comparing contradictory empirical results
[9].
Data Collection and Selection Criteria
The data set comprises the 25 high-value reference papers identified in the initial stage. The
systematic selection process adhered to the following strict criteria:
Source Authority: Preference was given to publications from established economic organizations (OECD,
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 258

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

IMF [20]), global policy think tanks (McKinsey, WEF [6]), and top-tier academic journals [5].
Thematic Relevance: Only sources directly addressing the nexus of AI, automation, and labor
market outcomes (employment, wages, skills) were included [4].
Temporal Scope and Recency: The selection focused heavily on literature published post-2017 (following
the publication of the seminal Frey & Osborne paper [5]), ensuring the inclusion of recent analyses on the
impact of Generative AI (2023-2025) [17].
Balance of Perspective: References were purposefully chosen to represent both the displacement thesis [4]
and the augmentation thesis [7], ensuring a comprehensive and unbiased discussion.
Data Analysis and Synthesis Framework
The collected literature was systematically analyzed using a three-pronged methodological framework
to structure the Literature Review and subsequent Discussion.
A. Task-Based Framework Analysis (TBF)
The core structure of the analysis is built upon the Task-Based Framework (TBF), pioneered by Autor
[2]. This methodology shifts the focus from the susceptibility of entire jobs to the automation and
augmentation of specific tasks within those jobs.
Application: Each reference was coded to identify whether its findings related to the automation of
routine tasks or the augmentation/creation of non-routine cognitive/manual tasks. This allowed for a
precise mapping of AI's impact across occupational categories [5].
B. Comparative Effect Analysis (CEA)
A Comparative Effect Analysis was conducted to reconcile the divergent findings regarding the net
impact on employment. This involved synthesizing findings from:
Economic Modeling: Comparing quantitative predictions of job losses (e.g., from Goldman Sachs [21])
with models predicting job creation through new tasks [9].Policy Assessment: Analyzing how
international bodies (like the European Parliament [24] and OECD [20]) interpret the risk/opportunity
balance, moving beyond simple displacement to focus on necessary policy responses.

4. Results and Discussion
The systematic synthesis of literature reveals that the impact of Artificial Intelligence (AI) on
employment is characterized by a complex, dual-sided structural transformation rather than a simple linear
process of job elimination. The results necessitate a fundamental re-evaluation of educational, economic,
and policy frameworks.
Synthesis of Core Findings (Net Effect Analysis)
The research confirms the existence of two powerful, opposing economic forces operating
concurrently in the AI era, resulting in a complex net effect on employment. The Confirmation of Dual
Mechanisms. The initial fear of widespread unemployment [4] is countered by strong evidence supporting
the creation and augmentation mechanisms [1]. The findings of Frey and Osborne (2017) [5] and the
empirical data on robotics from Acemoglu and Restrepo (2020) [12] establish the high risk of job
displacement in routine and medium-skill sectors [6]. Conversely, the "New Tasks" framework [9] and the
analysis of AI as a General Purpose Technology (GPT) [8] confirm that AI creates demand for new
complementary roles, drives productivity [21], and generates new industries [1] (the augmentation effect).
The Generative AI Inflection Point
The rise of Generative AI (GenAI) introduces a new, critical finding: the automation risk has
successfully migrated from the factory floor to the office desk. The analysis of Eloundou et al. (2023) [17]
confirms that high-skilled cognitive labor (e.g., coding, content creation) is now highly exposed. This
finding dramatically alters the displacement risk profile, suggesting that even highly educated workers must
fundamentally reskill [14]. However, the experimental results of Noy and Zhang (2023) [18]
simultaneously highlight GenAI’s potential to democratize productivity by significantly boosting the
output of less-experienced workers, effectively lowering the skill floor for complex tasks.
Discussion: Economic and Societal Implications
The combined displacement, augmentation, and GenAI effects lead to several significant economic and
societal implications:

G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 259

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

A. Exacerbation of Economic Inequality and Polarization
The most critical outcome is the worsening of labor market polarization and economic inequality [13].
The synthesis confirms that the benefits of AI-driven productivity gains primarily accrue to capital owners
and a small segment of highly skilled, AI-complementary workers [13].
Skill Premium: AI automation suppresses wages and employment for those performing routine,
automatable tasks, while those who can leverage AI (possessing non-routine cognitive skills [19])
command a higher skill premium.
Policy Challenge: The findings strongly support the warnings issued by Korinek and Stiglitz (2021) [13],
suggesting that without robust policy interventions, AI will amplify income divergence, demanding
radical rethinking of social safety nets and tax structures [4].
B. The Urgency of Human Capital Restructuring
The required skill shift is not incremental but transformative. The results underscore that the future of
work hinges on human capital that complements AI, focusing on uniquely human traits:
Required Skills: Critical thinking, complex communication, creativity, and adaptability are now the most
valuable economic assets [15].
The Educational Imperative: The urgency of this shift places immense pressure on educational systems
to move away from rote learning toward project-based, technologically-integrated curricula from an early
stage [14]. The research confirms that continuous reskilling and upskilling is a permanent economic feature
of the AI age [16].
C. The Governance of Work (Algorithmic Management)
Beyond job loss, AI is redefining the worker-employer relationship through Algorithmic Management
[24]. This result has profound implications for labor welfare and rights. The synthesis highlights that while
AI tools can optimize scheduling and performance, the practice introduces risks related to surveillance,
burnout, algorithmic bias, and the erosion of worker autonomy. This necessitates proactive legal and
ethical frameworks to govern the digital workplace [24].

5. Conclusion
The systematic synthesis of literature confirms that Artificial Intelligence (AI) represents an
unstoppable, dual-edged economic force that is fundamentally redefining the concept of human work [1].
The research has unequivocally established that the impact of AI is not defined by simple job elimination but
by a turbulent structural transition marked by significant displacement in routine tasks [5] and a profound
augmentation in non-routine, complex roles [9]. The recent rise of Generative AI (GenAI) has acted as a
critical inflection point, extending automation risk from manual labor to high-skill cognitive labor [17],
thereby amplifying labor market polarization [13]. The core challenge is shifting the focus from simply
adapting to technology to governing technology to ensure equitable and inclusive outcomes [22].
Ultimately, the net outcome— whether AI leads to shared prosperity or heightened inequality—hinges
critically on the immediate and strategic policy responses adopted by nations.

6. Discussion of Key Findings
The key findings necessitate a major recalibration of policy. The most immediate concern is the
exacerbation of economic inequality [13]. As confirmed by the synthesis, productivity gains from AI tend
to accrue disproportionately to capital owners, leaving workers performing automatable tasks vulnerable to
wage suppression and displacement [4]. This requires new policy thinking, potentially exploring taxation
mechanisms on automation-derived profits to fund universal social safety nets. Furthermore, the mandatory
nature of the skill shift cannot be overstated: the workforce requires a rapid transition away from outdated,
rule-based learning towards capabilities that complement AI, such as creativity, critical thinking, and
emotional intelligence [19]. This transition is non-negotiable for economic survival. Finally, the research
underscores the necessity of managing Algorithmic Management [24] to protect worker autonomy and wellbeing in the increasingly digitized workplace.
Policy Recommendations
Based on the evidence across the 25 reference papers, several strategic policy interventions are
crucial. Firstly, there must be a complete overhaul of educational systems to implement national AIreadiness curricula from the ground up, promoting digital literacy and adaptability [14]. Secondly,
governments and industries must collaborate to create robust, accessible, and continuous reskilling
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 260

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

frameworks that focus specifically on high-demand, non-routine cognitive skills [15]. Thirdly, labor laws
must be updated to explicitly regulate Algorithmic Management, ensuring transparency, fairness, and the
protection of worker privacy against constant digital surveillance [24]. Lastly, economic policy must
explore innovative measures—like an automation tax or specific taxes on large data-driven profits—to
finance these massive educational and social welfare investments, ensuring the benefits of AI are broadly
shared [13].

7. Future Works
This research primarily utilized a systematic literature synthesis focused on global trends and
economic models. Future work should focus on filling critical research gaps:
Developing Economy Specialization: The vast majority of empirical data focuses on advanced economies
[5]. Future research must conduct primary, region-specific studies on the differential impact of AI and
GenAI on job creation, wage compression, and social mobility within developing economies, where labor
market rigidities and social safety nets are fundamentally different. Qualitative Impact of GenAI: Further
research is needed to qualitatively assess the psychological impact and changes in job satisfaction resulting
from the adoption of GenAI in creative and cognitive professions [17]. Policy Efficacy Evaluation:
Longitudinal studies are necessary to evaluate the long-term efficacy of various policy interventions
currently under discussion (e.g., specific reskilling programs, automation taxes), measuring their true
impact on reducing inequality and stimulating net employment growth.

8. Reference
1. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and
Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.[1]
2. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace
automation. Journal of Economic Perspectives, 29(3), 3-30.[2]
3. Bessen, J. (2015). Learning by Doing: The Real Connection between Innovation, Wages, and
Wealth. Yale University Press.[3]
4. Ford, M. (2015). Rise of the Robots: Technology and the Threat of a Jobless Future. Basic
Books.[4]
5. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to
computerisation? Technological Forecasting and Social Change, 114, 254-280.[5]
6. Manyika, J., Chui, M., Miremadi, M., Bughin, J., Lund, S., Corbett, A., & Ko, R. (2017). A future
that works: Automation, employment, and productivity. McKinsey Global Institute Report.[6]
7. Daugherty, P. R., & Wilson, H. J. (2018). Human+ Machine: Reimagining Work in the Age of AI.
Harvard Business Review Press.[7]
8. Trajtenberg, M. (2018). AI as a general purpose technology: Some implications for economic
growth. NBER Working Paper 24653.[8]
9. Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology Affects Labor.
Journal of Economic Perspectives, 33(2), 3-30.[9]
10. Aghion, P., Jones, B., & Jones, C. I. (2019). Artificial Intelligence and Economic Growth.
In
The Economics of Artificial Intelligence: An Agenda (pp. 309-346). University of Chicago
Press.[10]
11. World Economic Forum (WEF). (2020). The Future of Jobs Report 2020. WEF Report.[11]
12. Acemoglu, D., & Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets.
13. Journal of Political Economy, 128(6), 2188-2244.[12]
14. Korinek, A., & Stiglitz, J. E. (2021). Artificial Intelligence and Economic Inequality. NBER
Working Paper 28628.[13]
15. Felten, E. (2021). The effects of artificial intelligence on the labor market: An analysis of skill
shifts and occupational change. Brookings Institution Report.[14]
16. Lamb, J., & Jones, R. (2022). Reskilling and Upskilling for the Age of AI. Harvard Business
Review.[15]
17. Georgieff, A., & Wanner, I. (2022). Artificial intelligence and employment: A literature review.
IZA Journal of Labor Policy, 11(1).[16]
G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 261

© 2026 JETIR January 2026, Volume 13, Issue 1
G H Raisoni International Skill Tech University Pune

www.jetir.org (ISSN-2349-5162)

18. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the
Labor Market Impact of Large Language Models. arXiv preprint arXiv:2303.10142.[17]
19. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative
artificial intelligence. Science, 381(6657), 585-592.[18]
20. Autor, D., Mindell, D., & Reynolds, E. (2023). The work of the future: Building better jobs in an
age of intelligent machines. MIT Task Force on the Work of the Future.[19]
21. OECD. (2023). Artificial Intelligence, Employment and Skills: The Final Frontier? OECD Social,
Employment and Migration Working Papers, No. 291.[20].
22. Goldman Sachs Research. (2023). The Potentially Large Effects of AI on Economic Growth.
23. Goldman Sachs Economic Report.[21]
24. IMF. (2024). AI Will Transform the Global Economy. Let's Make Sure It Benefits Humanity.
25. IMF Blog/Policy Paper.[22]
26. Tacke, U., & Spatt, V. (2024). The AI-Readiness of the Workforce: A Conceptual Framework and
Empirical Evidence. Journal of Vocational Behavior, 142, 103859.[23]
27. European Parliament. (2025). Digitalisation, artificial intelligence and algorithmic management in
the workplace: Shaping the future of work. EP Research Paper.[24]
28. Stanford University Digital Economy Lab. (2025). Canaries in the Coal Mine? Six Facts about the
Recent Employment Effects of Artificial Intelligence. Stanford Research Report.[25]

G. H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune, Maharashtra-412207, India.
JETIRHG06027
Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 262
</reference>

<statements>
1. Rather than generating uniform technological unemployment, AI is driving job transformation, task reconfiguration, and organizational restructuring, with heterogeneous outcomes shaped by sectoral conditions, firm strategies, and institutional frameworks.
2. These roles often command wage premia, reflecting scarcity of relevant skills and the strategic importance of AI capabilities for firms.
</statements>

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