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>
International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

The Paradox of Progress: AI Adoption,
Workforce Restructuring, and Global
Employment Trends in the Digital Era
Dr Aelyamma P J1, Dr G N Prakash2, Dr Vince Thomas3,
Ms Savitha M. A4
1

Associate Professor of Commerce, Maharaja's College, Ernakulam
Associate Professor of Mathematics, Maharaja's College, Ernakulam
3
Assistant Professor of Commerce, Government Arts & Science College, Elanthoor
4
Assistant Professor of Commerce, Govt. Arts & Science College, Tripunithura
2

Abstract
Artificial Intelligence (AI) has emerged as a transformative force across global industries, promising
efficiency, productivity, and innovation. However, this digital acceleration has also precipitated an
unsettling paradox—while AI adoption surges, large-scale layoffs are simultaneously occurring across
major corporations. Drawing from recent global data on workforce reductions, corporate AI investment
trends, and cross-sectoral earnings differences, this study explores the complex relationship between AIdriven innovation and employment restructuring. The findings indicate that while AI integration enhances
organizational productivity and profitability, it also necessitates significant realignment of human
resources, disproportionately affecting administrative and middle-management roles. The study
underscores the urgent need for adaptive reskilling policies, ethical AI governance, and equitable labour
transition frameworks to mitigate job displacement and ensure sustainable technological progress.
Keywords: Artificial Intelligence, Workforce
Organisational Change, Digital Economy

Restructuring,

Technological

Unemployment,

1. Introduction
The rapid proliferation of Artificial Intelligence (AI) technologies across industries has transformed
business models, operational structures, and labour dynamics. Recent data from Stanford University's AI
Index 2025, PwC's Global AI Jobs Barometer, and layoffs. Fyi reveals a striking trend: as AI investment
increases, corporate layoffs also accelerate.
Companies such as Amazon, Meta, Microsoft, and Tata Consultancy Services (TCS) have all undertaken
large-scale workforce reductions, citing the need to enhance efficiency and reinvest in AI-driven tools.
For example, Amazon announced in 2025 that it would reduce its global workforce by nearly 14,000
employees, while TCS restructured approximately 12,000 positions to align with its AI adoption strategy
(Parthasarathy, 2025). These developments underscore a global shift from human-centric operational
models to data-augmented, AI-enabled management systems.
This paper investigates the paradoxical relationship between AI adoption and workforce downsizing using
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International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

quantitative data and qualitative thematic analysis.
2. Review of Literature
Various studies indicate that while AI enhances productivity, it also disrupts traditional employment
models. Brynjolfsson and McAfee (2014) argue that digital technologies create a divergence between
productivity and employment. Frey and Osborne (2017) estimate that nearly 47% of U.S. jobs are at risk
of automation. Reports from the World Economic Forum (2023) suggest a structural shift towards AIintensive job roles. This literature positions AI adoption as both an opportunity and a challenge for labour
markets.
3. Research Methodology
This study adopts a secondary data analysis approach. Data were sourced from:
Stanford University’s AI Index 2025
PwC’s 2025 Global AI Jobs Barometer
layoffs.fyi database (2022–2025 trends)
LinkedIn’s AI Hiring and Skills Reports (2024–2025)
The researcher has used Descriptive and inferential methods to interpret global employment data, layoff
patterns, and industry-wise AI investment trends. Thematic analysis is applied to identify recurring
narratives and policy implications.
4. Data Analysis and Interpretation
4.1 Global Layoff Trends (2022–2025)
Global layoffs increased from 1.46 million in 2022 to 2.6 million in 2025, even as corporate investment
in AI rose sharply. The number of companies reporting layoffs reached 12,772 in 2025. The sectors most
affected included e-commerce, IT services, and HR operations, where automation replaced repetitive
human tasks.
4.2 Industry-Wise Shifts in Employment
According to PwC (2025), AI-intensive industries—data analytics, automation, and customer
intelligence—recorded the highest layoffs in administrative and support roles but also saw significant
growth in technical and data management positions. Salesforce, Meta, and Amazon accounted for the
majority of tech layoffs, indicating a sector-wide shift toward leaner, automated organizational models.
4.3 Global AI Investment Trends (2013–2025)
AI investment rose from $19.2 billion in 2013 to $360.7 billion in 2025 (PwC, 2025). This surge
underscores the centrality of AI in corporate strategy, with firms viewing it as a cost-optimization tool.
Despite this, human employment elasticity declined, showing limited translation of productivity gains into
job growth.
4.4 Regional and Sectoral Differences
LinkedIn's (2025) data shows that the AI hiring ratio grew significantly in North America, Europe, and
India, whereas traditional sectors—manufacturing and administration—faced contraction.
Workers with AI-related skills earned 46% higher salaries than those without such competencies, with the
most significant pay disparities in the IT, energy, and finance industries.

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International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

Figure 4.1. Global Layoffs across Companies (2022-2025)

Figure 4.1 shows an apparent rise in layoffs from 2022 to 2025. This reflects corporations' strategic
restructuring to reduce labour costs and enhance AI integration. The data indicate that technological
transformation is associated with workforce downsizing.
Figure 4.2. Number of Companies Reporting Layoffs (2022-2025)

Figure 4.2 illustrates the increase in the number of companies implementing layoffs. The broad
participation across industries implies that AI adoption is not restricted to technology firms but is
becoming a widespread organizational phenomenon.

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International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

Figure 4.3 Industry-wise Layoffs (%)

Figure 4.3 reveals a technology-led restructuring trend, with digital transformation and AI integration as
the primary drivers of layoffs, particularly in IT and e-commerce. Traditional sectors like manufacturing
and finance remain relatively resilient but are likely to face gradual workforce optimisation. The pattern
reflects a shift from labour-intensive roles to knowledge- and skill-intensive functions, emphasising the
importance of continuous upskilling and adaptability in the modern job market.
Figure 4.4 Wage Gap Between AI-skilled and Non-skilled Workers (%)

Figure 4.4 demonstrates a clear upward correlation between industry digitization and wage inequality. AIskilled workers enjoy significantly higher earnings, reinforcing the global trend where AI proficiency is
becoming a key driver of labour market differentiation and socio-economic mobility.

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International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

Figure 4.5 Global AI Investment Growth

Figure 4.5 highlights that AI is no longer a niche innovation but a central driver of global economic growth.
The rapid escalation in investment underscores AI's role in reshaping industries, labour markets, and
national competitiveness. It also explains why businesses are restructuring, automating roles, and
increasing demand for AI-skilled talent.
5. Discussion
employment structures. The findings highlight a need to reimagine workforce planning, emphasising
reskilling, human-AI collaboration, and ethical governance frameworks.
While short-term job displacement is evident, long-term benefits depend on proactive policy responses—
especially in education, vocational training, and inclusive technological adoption.
The analysis demonstrates a direct association between AI integration and workforce restructuring. While
AI improves operational efficiency and reduces costs, it also displaces routine, repetitive job roles. This
refers to a polarised labour market in which high-skill AI-related jobs expand while mid- and low-skill
jobs decline.
6. Policy and Organizational Implications
1. Establish national-level reskilling and continuous digital literacy programs.
2. Mandate ethical AI governance standards in medium and large enterprises.
3. Offer tax incentives to firms that retain and retrain existing employees rather than replacing them.
4. Develop public-private partnerships for workforce transition support.
5. Expand digital access infrastructure to ensure inclusive digital participation.
7. Conclusion
ion. To ensure inclusive development, governments and organisations must implement reskilling
programs, social protection mechanisms, and ethical AI frameworks. It will help manage the transition
towards a technologically driven economy without compromising human welfare.
The interplay between AI adoption and workforce restructuring presents a defining challenge for the 21stcentury economy. While AI enhances operational efficiency, it also has social and ethical ramifications.
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International Journal for Multidisciplinary Research (IJFMR)
E-ISSN: 2582-2160 ● Website: www.ijfmr.com

● Email: editor@ijfmr.com

Sustainable technological progress must be human-centred, balancing innovation with inclusivity and
efficiency with employment security.
References
1. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age. New York: W.W. Norton &
Company.
2. Frey, C., & Osborne, M. (2017). The future of employment: How susceptible are jobs to automation?
Technological Forecasting and Social Change.
3. layoffs.fyi. (2025). Global Layoff Tracker 2022–2025. Retrieved from https://layoffs.fyi
4. LinkedIn. (2025). AI Hiring and Skills Trends Report 2025. LinkedIn Data Insights.
5. Parthasarathy, S. (2025, November 3). As AI adoption by companies accelerates, tech layoffs continue.
The Hindu.
6. PwC. (2025). Global AI Jobs Barometer 2025. PricewaterhouseCoopers.
7. Stanford University. (2025). AI Index Report 2025. Stanford Human-Centered Artificial Intelligence
Institute.
8. World Economic Forum. (2023). Future of Jobs Report.

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</reference>

<statements>
1. The literature converges on the importance of proactive policy responses—targeted upskilling, inclusive AI governance, and social protection reforms—to steer AI-driven restructuring toward more inclusive outcomes.
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. Thus, AI-driven restructuring often manifests as internal task reallocation and role redesign rather than outright job destruction, though displacement is significant in some exposed categories.
4. Systematic reviews emphasize that a majority of empirical studies report net positive or mixed employment effects, with relatively few documenting large-scale technological unemployment.
5. 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.
6. 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.