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AI in the Workplace: A Systematic Review of Skill Transformation in the Industry

Author

Listed:
  • Leili Babashahi

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Carlos Eduardo Barbosa

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil
    Centro de Análises de Sistemas Navais, Marinha do Brasil, Rio de Janeiro 21941-617, Brazil)

  • Yuri Lima

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Alan Lyra

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Herbert Salazar

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Matheus Argôlo

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Marcos Antonio de Almeida

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

  • Jano Moreira de Souza

    (Program of Systems and Computer Engineering, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil)

Abstract

Artificial Intelligence (AI) applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of AI on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively.

Suggested Citation

  • Leili Babashahi & Carlos Eduardo Barbosa & Yuri Lima & Alan Lyra & Herbert Salazar & Matheus Argôlo & Marcos Antonio de Almeida & Jano Moreira de Souza, 2024. "AI in the Workplace: A Systematic Review of Skill Transformation in the Industry," Administrative Sciences, MDPI, vol. 14(6), pages 1-28, June.
    • Handle: RePEc:gam:jadmsc:v:14:y:2024:i:6:p:127-:d:1415869
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    Citations

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    Cited by:

    1. Darren Yi Sern Low & Kar Ming Shaw & Poovarasi Balan, 2026. "Sustainable Development Integration in Higher Education Institutions: Challenges and Opportunities in the Age of AI and Big Data," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(2), pages 2386-2408, April.
    2. Rajesh Prabu Vincent De Paul, 2025. "Field Service Optimization for Manufacturing and Automotive: Advanced Design Strategies for Distributed Work Order Orchestration and Technician Routing," International Journal of Computing and Engineering, CARI Journals Limited, vol. 7(19), pages 1-13.
    3. Chang, Yu-Ling & Seo, Beomseok, 2026. "Using explainable artificial intelligence to envision the future of work and the workforce – from prediction to explanation," Technology in Society, Elsevier, vol. 84(C).
    4. Wadim Strielkowski, 2025. "The role of micro-credentials in the future digitalized AI-driven education," Papers 2511.07583, arXiv.org.
    5. Aleksandar M. Damnjanović & Milan Rašković & Svetozar D. Janković & Boris Jevtić & Volodymyr N. Skoropad & Zoran D. Marković & Violeta Lukić-Vujadinović & Zoran Injac & Srđan Marinković, 2025. "AI-Enabled Strategic Transformation and Sustainable Outcomes in Serbian SMEs," Sustainability, MDPI, vol. 17(19), pages 1-33, September.
    6. Ionela-Corina CHERSAN, 2025. "The Impact Of Artificial Intelligence On Accounting Education," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 20(3), pages 107-134, December.
    7. Mioara Chirita & Riana Iren Radu, 2025. "AI-Enabled Automation, Labor Market Vulnerabilities, and Structural Transformation within an Applied Informatics Framework for Economic Analysis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 117-125.

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