Microsoft Word - AI & The Workforce Research - Sylvia Erigbe.docx 360 American Academic Scientific Research Journal for Engineering, Technology, and Sciences ISSN (Print) 2313-4410, ISSN (Online) 2313-4402 https://asrjetsjournal.org/index.php/American_Scientific_Journal/index Reshaping the Workforce: The Impact of AI, Automation, and Robotics on Labor Markets Sylvia Erigbe * Lumpkin College of Business and Technology, Eastern Illinois University, Illinois Email: sylviaerigbe@yahoo.com Abstract The triad of artificial intelligence (AI), automation and robotics is considered both a threat and a strategic cutting- edge tool across all societies. However, this paper explores how artificial intelligence, automation, and robotics are reshaping labor markets by examining whether they primarily displace workers or transform job roles and skills. The paper is guided by three research questions, and a qualitative approach is adopted. Grounded in Automation Theory, Human Capital Theory, and an adaptation of Maslow’s Hierarchy of Needs, the research argues that labor market outcomes are shaped not by technology itself, but by organizational strategies and policy choices. The study further highlights the critical importance of ethical AI integration, workforce reskilling, and human-centered innovation. It offers a framework for scholars, managers, and policymakers to navigate the evolving future of work by centering human development alongside technological advancement. In conclusion, that the triad of AI, automation and robotics is significantly reshaping labour markets is incontestable. It is therefore recommended that organizations and institutions continue to adopt and adapt AI to add value to work processes. Keywords: Artificial Intelligence; Automation; Robotics; Labor market; Workforce; Job. 1. Introduction Technology has caused a great shift across sectors, globally [1]. The evolution of technology is causing a shift in the labor market; what once required human thought, hands, and judgement is increasingly being handled by machines capable of learning, adapting, and even making decisions. Recent projections from Wheeler [2] estimate that 86% of businesses globally will be reshaped by AI technologies by 2030, reinforcing the scale and urgency of these transformations. According to Schwab [3], this is not merely the next stage in technological evolution, but it is a workforce revolution that is redefining the value of human labor in real time. ------------------------------------------------------------------------ Received: 5/15/2025 Accepted: 7/1/2025 Published: 7/13/2025 ------------------------------------------------------------------------ * Corresponding author. https://asrjetsjournal.org/index.php/American_Scientific_Journal/index American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 361 The journey from mechanical automation to cognitive systems began decades ago. In the 1960s, Unimate, the first industrial robot, was deployed in a General Motors plant, ushering in a new era of manufacturing efficiency and job restructuring [4]. The breakthrough laid the groundwork for industrial automation that would soon spread across global production systems. As robotics became more advanced, machines began replacing not only manual tasks but also roles requiring speed, repetition, and endurance [5]. While industrial robotics dominated the second half of the 20 th century, the rise of Artificial Intelligence (AI) in the early 21 st century marked a new kind of disruption. According to [6], AI emerged from decades of academic development but gained traction in the workforce only after advances in big data, algorithmic training, and cloud infrastructure. AI soon entered core business functions, performing tasks such as customer profiling, fraud detection, document analysis, and real-time forecasting [7]. These systems began influencing not just how jobs were done but whether humans were needed to do them at all [8]. This shift is not abstract. According to Ellingrud and his colleagues [9], nearly one-third of all hours worked in the U.S economy could be automated by 2030. That figure represents a fundamental reordering of job roles, time allocation, and human contribution. Schwab [3] forecasts that while 85 million jobs may be lost to automation, another 97 million could emerge but mostly for individuals with highly adaptive and technical skills. These projections underscore a growing paradox: the technologies that promise increased efficiency and innovation may also deepen economic polarization and social disruption. The impact of automation is far from uniform. In developed nations with strong educational infrastructure and proactive labor policies, AI may enhance productivity and create new forms of employment. In contrast, many developing economies risk workforce displacement due to limited access to digital tools and training [10]. The lack of strategic preparation can make automation a driver of exclusion rather than advancement. In countries like Portugal, studies have shown that AI may intensify structural unemployment unless accompanied by supportive upskilling frameworks [11]. Meanwhile, workers themselves are grappling with a rising sense of uncertainty. Employees across sectors, from transportation and retail to finance and healthcare, report heightened anxiety about the relevance of their roles in a rapidly digitizing environment [12]. This anxiety is not just about job loss but about staying valuable in a world where machines increasingly make decisions, deliver services, and even write reports. Deming & Summers [13] echo these concerns, noting that job security is being replaced by a new pressure which is the constant need to evolve or be replaced. This paper seeks to answer the overarching research question: How are AI, automation, and robotics reshaping labor market structures, job functions, and workforce skills in the modern economy? To guide this exploration, three sub-questions are posed: 1. In what ways are AI and automation altering the structure and composition of labor markets across different industries and regions? 2. How are job roles and responsibilities evolving in response to the integration of intelligent American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 362 technologies in the workplace? 3. What new skill sets are emerging, and how can organizations and governments support workforce adaptability and long-term employability? Constraint of the study surround non-inclusion of recent statistics on the application of robotics and labor market shifts as a result of automation. Detailed comparative analysis of impact is fraught with subjectivity. This is a relative constraint to the current paper. However, rigorous quality analysis of the concept under investigation provides insightful clarification. Nonetheless, findings offer valuable insights for scholars, students, policy makers, and business professionals seeking to understand and respond to workforce disruptions. The rest of the paper is structured as follows: section 2 is the literature review; section 3 dwells on application of Maslow’s hierarchy of needs while section 4 discusses findings. In section 5, direction of future research is highlighted. Finally, section 6 concludes and offers recommendations. 2. Literature Review The rise of AI, Automation, and Robotics has generated a vast body of interdisciplinary research examining their effects on the labor market. Scholars across Economics, Management, Sociology, and Technology studies have offered competing perspectives on whether intelligent technologies displace human labor or drive transformation and growth. One dominant perspective is the theory of skill-biased technological change (SBTC), which suggests that as digital technologies evolve, they disproportionately benefit workers with higher levels of education and technological proficiency. As a result, low and middle skill jobs, especially those involving routine and repetitive tasks, are at risk of automation and displacement [14]. This view is supported by empirical data from the United States (US) and other Organization for Economic Co-operation and Development (OECD) countries, where automation has contributed to job polarization and wage inequality. In contrast, high skill roles in areas like data science, AI design and cybersecurity are increasingly in demand. However, other scholars challenge the determinism of the SBTC framework. Krakowski, Luger, & Raisch [8] argue that AI does not inevitably replace workers but instead reshapes the boundaries of human and machine collaboration. They suggest that most jobs will not be fully automated; rather, individual tasks within jobs will be redesigned, shifting human labor toward areas of strategic judgement, empathy, and creativity. This “task reallocation” model is echoed by Chan [7], who contends that the future of work will be hybrid, with humans and machines performing complementary functions. These insights signal a shift away from viewing AI as a job killer and toward seeing it as a force for role evolution. The centrality of human capital is another important perspective. According to the human capital theory of Becker (1964), the ability of workers to adapt depends on access to education, reskilling opportunities, and supportive institutions. Mukherjee [15] found that firms investing in employee upskilling during the COVID-19 pandemic were more resilient to disruption and experienced higher employee retention. Similarly, Md, Saiful and Jannatul [16] argue that strategic AI integration depends not only on technical infrastructure but on cultivating an adaptive and skilled workforce. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 363 Regional analyses highlight important differences in readiness for AI-driven change. Montenegro [11] further explores the Portuguese labor market and warns that without public investment in education and digital skills, AI could exacerbate structural unemployment. Tachicart [17] reaches a similar conclusion in Morocco, where automation risks outpace policy interventions. In contrast, nations like Germany and Singapore are often cited for implementing national AI strategies that emphasize inclusive reskilling pathways [10]. These findings suggest that the distributional effects of AI are shaped as much by economic policy and governance as by the technology itself. Industry-specific studies offer further nuance. In manufacturing and transportation, robotics have already replaced thousands of jobs, particularly in repetitive and dangerous tasks [18]. Similarly, in the transportation industry, companies like Uber are increasingly leveraging AI for route optimization, dynamic pricing, and are actively piloting autonomous vehicle technologies that could eventually reduce reliance on human drivers [7]. However, in the healthcare sector, robotic-assisted surgeries, such as those performed by the da Vinci Surgical System, demonstrate how AI and robotics enhance precision and efficiency without fully replacing human surgeons, instead augmenting their capabilities. In the same way, AI is enhancing diagnostics, treatment planning, and patient monitoring. Gans [ 6 ] notes that competitive advantage increasingly depends on how firms integrate AI into their operations, not just for efficiency, but for innovation, safety and agility. This raises questions about the types of leadership and change management needed to guide ethical and effective AI adoption. The ethical and psychological dimensions of workforce transformation are also gaining attention. Pazzanese [19] warns that AI driven decisions such as automated layoffs or algorithmic scheduling can erode worker trust and wellbeing if not handled transparently. Hemmerdinger [20] and Pazzanese [19] report increasing worker anxiety about being replaced by machines, particularly among mid-career employees. This fear is compounded by the opacity of AI systems, many of which lack explainability or meaningful human oversight. Finally, emerging research highlights the importance of digital equity. Vassilliadis & Hilpert [21] argue that nations that prioritize inclusive digital education, especially among youth and women are better positioned to thrive in AI-driven economies. World Economic Forum [22[ opines that governments develop agile reskilling systems and strengthen partnerships between academia, industry, and the public sector to close emerging skills gaps. Meanwhile, Senz [23] emphasizes that companies that treat AI as a tool to augment rather than replace human talent report better long-term outcomes in productivity, trust, and innovation. Theoretical Framework and Hypothesis Testing This research is built around two competing, testable hypotheses: H₀: AI, automation, and robotics will reshape the labor market by replacing human labor and contributing primarily to job displacement. H₁: AI, automation, and robotics will reshape the labor market by transforming job roles, creating new employment opportunities, and increasing demand for advanced human skills. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 364 There are several strong arguments which claim that AI, Automation and Robotics will replace workers in the workplace and contribute to job displacement, globally. According to [14], businesses favor automation because technological systems can perform tasks more quickly, more reliably, and at a lower cost than humans. AI, automation, and robotics have accelerated this process by automating not only physical tasks but increasingly cognitive decision-making functions as well [18]. In addition, routine, repetitive tasks, especially those found in manufacturing, logistics, retail, and basic administrative roles are particularly vulnerable to replacement [7]. Right now, many retailers have implemented self-checkout machines to replace cashiers, warehouses have adopted robotic systems to streamline logistics, and companies have deployed AI chatbots to handle customer service inquiries, reducing the need for human agents [22]. In these cases, businesses achieved major short- term cost savings by cutting wage expenses, reducing human error, minimizing training costs, and enabling operations that run continuously without breaks or shifts [17]. However, scholars caution that such approaches, while economically attractive at first, risk widening inequality and creating social instability as displaced workers struggle to find new employment [24]. The counter argument which claims that AI, automation, and robotics will reshape the labor market by transforming job roles, creating new employment opportunities, and increasing demand for advanced human skills draws from human capital theory, complementary frameworks, and the task augmentation model. Human capital theory proposes that workers are not static resources; they can develop new capabilities through education and training, thereby increasing their value within the labor market [25]. Firms that invest in upskilling programs can enable employees to transition into roles that leverage uniquely human strengths. Complementarity frameworks further support the idea that technology and human labor are not necessarily adversaries. Scholars highlight that AI excels in structured data processing, but humans remain superior in critical thinking, emotional intelligence, leadership, and creative problem- solving [8]. The task augmentation model shows that AI rarely replaces entire occupations but automates certain components of jobs, allowing humans to focus on the parts that require higher-order skills [6]. For instance, in healthcare, AI systems assist in analyzing medical images, but physicians are still responsible for diagnosis, patient communication, and treatment decisions [9]. Organizations that embrace this human-AI partnership, like IBM and Microsoft, have demonstrated that reskilling workers leads to greater organizational resilience and stronger market performance [16]. From a cost perspective, firms focusing on automation and displacement can realize faster savings by eliminating labor costs. Yet, research shows that those investing in workforce transformation create sustainable long-term advantages. Companies that upskill workers build loyalty, encourage innovation, and maintain greater adaptability to future technological disruptions [10]. Pazzanese [19] highlights that scholars emphasize that organizations that implement AI ethically (focusing on transparency, fairness, and human oversight) achieve better trust and more positive labor outcomes. Furthermore, broader societal research warns that nations failing to prepare their workforce for AI integration risk facing worsening unemployment and inequality, while those prioritizing digital education and human capital development will capture the most benefits from the AI economy [11]. To further illustrate how organizational strategies influence the human experience of AI-driven labor market American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 365 change, this study adapts Maslow’s Hierarchy of Needs to the realities of job displacement and job transformation. Adaptation of Maslow’s Hierarchy to the Future of Work Figure 1: Maslow’s Hierarchy of Needs (McLeod,2025) Maslow’s Level Impact Under Ho: Job Displacement Impact Under H1: Job Transformation Self-Actualization Limited growth; creativity restricted due to job loss Growth through creative, strategic, and leadership opportunities Esteem Needs Loss of self-worth due to redundancy and unemployment Stronger self-confidence through meaningful upskilling and new roles Social Needs (Belonging) Isolation from being displaced; reduced teamwork and purpose Collaboration with AI; strengthened human-machine teamwork Safety Needs Job insecurity; financial Instability Career security through reskilling and adaptability Physiological Needs Threats to basic survival needs (income, healthcare) Continued ability to meet basic needs through new employment paths Figure 2: Adaptation of Maslow’s Hierarchy of Needs to the Impact of AI, Automation, and Robotics on the Labor Market. American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 366 3. Application of Maslow’s Hierarchy of Needs Traditionally, Maslow’s model suggests that individuals must first satisfy basic needs such as physiological and safety needs before advancing toward social belonging, esteem, and ultimately self-actualization. In the context of labor market disruption, the strategies organizations choose whether displacement-focused or transformation focused, will determine whether workers regress toward insecurity or progress toward growth and fulfillment. On the displacement side, companies prioritize automation to cut labor costs and drive operational efficiency. As machines replace human labor in routine and repetitive tasks, many workers lose their jobs and face immediate threats to their physiological needs, such as stable income, housing, and healthcare [14]. Their sense of safety also erodes as job security disappears and financial vulnerability increases [17]. Without stable employment, displaced workers often experience social isolation and reduced professional belonging, further damaging their mental health and community engagement [24]. Their ability to achieve esteem needs diminishes as they lose opportunities to contribute meaningfully to society, and without pathways for personal growth, self- actualization becomes unattainable. For organizations, this leads to broader consequences: firms may face reputational risks, lower consumer loyalty, and higher social tension. Economically, mass displacement can shrink consumer markets, increase social welfare costs, and deepen inequality, weakening long-term economic resilience. In contrast, on the transformation side, organizations view human capital as a strategic asset and proactively invest in employee reskilling and workforce adaptation. Workers who gain new skills such as AI management, digital communication, and strategic leadership are able to maintain or rebuild their physiological safety security even in the face of technological change [25]. Secure career pathways ensure continued access to income and benefits [9]. AI-driven collaboration fosters new teamwork, strengthening social belonging within and across organizations [8]. As workers master new technologies and drive innovation capacity, and stronger brand loyalty. Economically, investing in human development expands the labor market’s productive potential, enhances social stability, and ensures that technological progress translates into shared prosperity. Thus, adapting Maslow’s Hierarchy to the AI-affected labor market reveals that the stakes extend far beyond individual employment. Companies that pursue short-term cost savings through automation risk creating a fragile, unequal economy where many workers remain stuck at the lowest levels of human need. Conversely, organizations that prioritize reskilling, human-centric job redesign, and ethical AI use empower individuals to ascend the hierarchy toward self-actualization, while building sustainable, resilient economies. 4. Discussion of Findings One of the most significant contributions of this research is the application of Maslow’s Hierarchy to the labor market disruption not simply as a technical or economic issue, but as a profound human issue affecting workers’ ability to meet their physiological, psychological, and self-development needs. Organizations that pursue displacement-centered strategies may achieve short-term cost savings but risk creating fragile, socially unstable systems. However, firms that invest in human-centered transformation strategies support workers in moving up Maslow’s hierarchy by promoting career security, social belonging, self-esteem, and self-actualization. For scholars, this study deepens ongoing debates about the future of work by emphasizing the human developmental dimension of technological change. While previous research often emphasized on macroeconomic effects [14] American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 367 or firm level automation trends, scholars such as [26] have recently pointed out that the European Union’s labor market faces a structural transformation where skill-based innovation becomes crucial. This research aligns with such perspectives and provides a human-centered model for analyzing organizational responses to AI. For managers and organizations, the findings offer actionable insights. Managers who treat AI as merely a tool for labor cost reduction risk long-term damage to workforce morale, consumer trust, and organizational reputation [17]. Ethical and sustainable AI adoption requires proactive investment in reskilling programs, human-AI collaboration models, and new role designs that maximize human creativity and problem-solving capacity. Already, Uber’s adoption of AI-driven route optimization and the healthcare sector’s use of robotic-assisted surgeries underscore how technology is not eliminating human roles entirely but reshaping them. Research by Md, Saiful & Jannatul [16] explains that companies which strategically leverage AI for decision augmentation rather than decision replacement experience higher employee engagement and performance. Additionally, Gans [6] emphasizes that successful AI adoption depends heavily on the complementary fit between machine capabilities and human strengths rather than simple substitution. Managers must realize that sustaining competitive advantage will increasingly rely on the quality of their workforce’s adaptive skills rather than on the breadth of their automation alone. Also, managers should not only focus on technical adoption but also on designing jobs that strategically integrate human judgement, creativity, and adaptability alongside machine intelligence. This research is relevant to multiple audiences. Business leaders and HR executives must rethink traditional hiring and developmental practices, prioritizing continuous learning pathways and emotional intelligence competencies. Educational institutions must adjust curricula to prioritize adaptability, critical thinking and human-AI collaboration skills [21]. Policymakers must craft regulatory frameworks that incentivize ethical AI adoption, workforce development, and job transformation rather than passive displacement. Finally, scholars must continue to refine theoretical models that place human development at the center of discussions about technological change, ensuring that academic research remains anchored to real-world impacts on workers, organizations, and societies. 5. Direction for Future Research Although this study offers valuable insights into how AI, automation, and robotics reshape labor markets, important gaps remain. One major area requiring further investigation is the long-term career development of workers transitioning into AI-augmented roles. While studies like Paygude and his colleagues [18] have highlighted the immediate effects of technological unemployment, there is limited empirical evidence on how displaced or reskilled workers rebuild professional identity, maintain career mobility, and to achieve satisfaction over time. Another important direction is assessing the effectiveness of workforce adaptation programs. Researchers should design empirical studies comparing different reskilling models like technical upskilling versus human-AI collaboration training, and measure their impacts on employee performance, psychological resilience, and innovation outcomes [16]. Surveys, interviews, and field experiments could help identify which organizational strategies best prepare workers for sustainable roles in AI-driven environments. Finally, future research should focus more explicitly on the relationship between education, skills evolution, and technological advancement. Studies such as [21] argue that labor-based innovation relies heavily on investments in education and human capital. Understanding how educational reforms and workplace learning programs align with AI-driven labor market needs will be critical for designing more inclusive and resilient workforce systems American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 368 globally. 6. Conclusion and Recommendations In conclusion, the rise of AI, automation, and robotics is fundamentally reshaping labor markets. Framed through two competing hypotheses: whether AI leads primarily to workforce displacement or workforce transformation, this paper draws from automation theory, human capital theory and adaptation of Maslow’s hierarchy of needs, and emphasized that technological change alone does not determine labor market outcomes; rather strategic, human-centered choices made by organizations and policymakers shape the future of work. A key contribution of this paper is the integration of psychological and developmental perspectives into the analysis of AI’s impact, highlighting that workforce strategies affect not only employment structures but fundamental human needs. By moving beyond purely technical and economic discussions, this study places human resilience, skill development, and ethical AI adoption at the center of sustainable labor market evolution. For scholars, this research provides a new framework connecting AI adoption to human flourishing. For managers and policy makers, it offers practical guidance on building adaptive, innovative, and inclusive workforces. Ultimately, the future of work will not be dictated by machines alone, but by the deliberate investment in human potential alongside intelligent technologies. As the next era of work unfolds, it will not be the technologies themselves that define success, but the organizations, leaders, and societies that choose to invest boldly in human potential. It is imperative therefore to invest in the application of AI in all modern work organisations, given the potential of AI to improve human performance and productivity. In a competitive world of work and markets in which corporate organisations continually strive to maintain some cutting-edge, adoption of AI will not only add value to work processes, it will help to sustain corporate strategy in the market place. It will also mean that employee training should incorporate AI processes in line with corporate objectives. Consequently, AI should now be adopted as part of organizational culture. Acknowledgements Prof. Patience Erigbe is given due appreciation for the motivation behind this paper, contribution, criticism and suggestions. The author‘s self-drive and interest are credited for future endeavours. References [1] P., Cecchi-Dimeglio . How AI will Transform the Labor Market: Essential Insights for Leaders. Forbes, 2024 [2] K., Wheeler. WEF: How AI will Reshape 86% of Businesses by 2030. Wefreport. WEF: How AI Will Reshape 86% of Businesses by 2030 | Technology Magazine, 2025 [3] K., Schwab. The Fourth Industrial Revolution: What i t Means, how to respond. Worldeconomicforum, 2016. https://www.weforum.org/stories/2016/01/the-fourth-industrial- http://www.weforum.org/stories/2016/01/the-fourth-industrial- American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 369 revolution-what-it-means-and-how-to-respond/ [4] Association for Advancing Automation Robotics. Unimate-The First Industrial Robot. Automate. 2025. https://www.automate.org/robotics/engelberger/joseph-engelberger-unimate [5] A., Marsh. In 1961, the First Robot Arm Punched in: The Era of Industrial Robots Began with Unimate, at a GM Plant in Trenton. Spectrum. 2022. https://spectrum.ieee.org/unimation-robot [6] J. S., Gans. Artificial intelligence adoption in a competitive market. Economica, 90(358), 2023, 690-705. [7] K. Chan. Analysis of the impact of technological progress such as artificial intelligence on employment. Advances in Education, Humanities and Social Science Research, 12(1), 2024, 463-463. [8] S. Krakowski, J . Luger & S . Raisch. Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425-1452, 2023 [9] K.Ellingrud, S. Sanghvi, G. Dandona, A. Madgavkar, M. Chui, O. White & P. Hasebe. Generative AI and the Future of Work in America. McKinsey. 2023. https://www.mckinsey.com/mgi/our- research/generative-ai-and the-future-of work-in-america [10] Y. Ni, M. Qi, C. Xie, & Y. Wu. The Impact of AI on Sustainable Development of The Labor Market. In SHS Web of Conferences, vol. 200, 2024, p. 02016). EDP Sciences. [11] M.C.F. Montenegro. Potential impact on workforce and labor market as a result of AI/automation: A perspective of the Portuguese labor force. 2019 (Master's thesis, ISCTE-Instituto Universitario de Lisboa (Portugal)). [12] N. Popli. AI Should Complement Humans at Work, Not Replace Them, TIME Panelists Say. TIME. 2023. https://time.com/6338489/ai-work-jobs/ [13] D. Deming & L. Summers. (2023). Is AI Already Shaking Up Labor Market?. The Harvard Gazette, 2023. Is AI already shaking up labor market? — Harvard Gazette [14] C. Frey & M. Osborne. The Future of Employment: How Susceptible are Jobs to Computerization? Sciencedirect, 2017. https://www.sciencedirect.com/science/article/abs/pii/S0040162516302244 [15] A.N. Mukherjee. Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy–an empirical investigation into pandemic ridden Indian industry. Management Matters, 19(2), 149-166, 2022. [16] S. Md, I. Saiful & F. Jannatul. Harnessing AI Adoption in the Workforce A Pathway to Sustainable Competitive Advantage through Intelligent Decision-Making and Skill Transformation. American Journal of Economics and Business Management, 8(3), 954-976, 2025. http://www.automate.org/robotics/engelberger/joseph-engelberger-unimate http://www.mckinsey.com/mgi/our-research/generative-ai-and%09the-future-of%09work-in-america http://www.mckinsey.com/mgi/our-research/generative-ai-and%09the-future-of%09work-in-america http://www.sciencedirect.com/science/article/abs/pii/S0040162516302244 American Academic Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) - Volume 102, No 1, pp 360-370 370 [17] R. Tachicart. Artificial Intelligence and Its Impact on the Moroccan Labor Market: Job Disruption or Transformation?. 2023. https://doi.org/10.20944/preprints202309.0193.v1 [18] V. Paygude, J. Thorat, M. Kakade, S. Waghmode & R. Shimpi. Robotics and Automation Using Artificial Intelligence. Journal Impact Factor: SJIF, 3(1), 2024. [19] C. Pazzanese. Ethical Concerns Mount as AI takes Bigger Decision-Making Role in More Industries. The Harvard Gazette 2020. https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai- [20] J. Hemmerdinger. EY Research Shows Most US Employees Feel AI Anxiety. EY. EY research shows most US employees feel AI anxiety | EY-US 2023 [21] M. Vassiliadis & Y.M. Hilpert. Labor-based innovation: The advantage of skills and education. [Innovación basada en el Trabajo: La ventaja de las habilidades y la educación] UCJC Business and Society Review, (65), 66-83, 2020. doi:https://doi.org/10.3232/UBR.2020.V17.N1.04. [22] World Economic Forum. The Future of Jobs Report 2023. Worldeconomicforum. The Future of Jobs Report 2023 | World Economic Forum [23] K. Senz. Is AI Coming for Your Job?. Harvard Business Review 2023. Is AI Coming for Your Job? |Working Knowledge [24] A. Zarifhonarvar. Economics of chatgpt: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics, 3(2), 100-119996, 2024. [25] G. Becker. Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, First Edition. NBER, 1964. https://www.nber.org/books-and-chapters/human- capital- theoretical-and-empirical-analysis-special-reference-education-first-edition [26] D. Jelonek & D. Walentek. (2024). Impact of Artificial Intelligence Innovations on the Labor Market of the European Union. Scientific Papers of Silesian University of Technology. Organization & Management/Zeszyty Naukowe Politechniki Slaskiej. Seria Organizacji i Zarzadzanie, (2024). http://www.nber.org/books-and-chapters/human-capital- http://www.nber.org/books-and-chapters/human-capital-