Global Multidisciplinary Journal

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Holistic Examination of Difficulties and Strategic Opportunities for Corporate Analysts in Growing Economies Influenced by Smart Automation and Digital Intelligence for Adaptive Skill Development

4 Institute of AI Research, University of São Paulo, Brazil

Abstract

The rapid integration of smart automation and digital intelligence across global corporate ecosystems is fundamentally transforming the roles and competencies required of business analysts in emerging economies. This study investigates the multidimensional challenges and strategic opportunities that corporate analysts encounter as firms adopt intelligent technologies to streamline operations, enhance decision-making, and optimize workforce performance. By synthesizing findings from contemporary studies on digital skills development, artificial intelligence applications in education, and virtual reality-based learning paradigms (Asvathitanont et al., 2024; Ayeni et al., 2024; Singh, 2026), the research identifies key gaps in current analytical skill sets and organizational adaptation strategies.

The methodology employs a comprehensive literature-driven approach, systematically analyzing the impact of AI-driven learning frameworks, personalized educational systems, and digital upskilling initiatives on the preparedness of analysts to navigate complex, data-intensive environments. The study also integrates insights from government-led digital transformation programs and policy frameworks (Office of the National Economic and Social Development Council, 2025; Kitthiwichayakul et al., 2023) to contextualize workforce development within national strategic priorities. Emphasis is placed on the interplay between technical proficiency, cognitive adaptability, and socio-organizational alignment in fostering sustainable analytics capabilities.

Findings indicate that emerging economies face significant constraints, including gaps in advanced digital skills, limited access to adaptive learning infrastructures, and resistance to AI adoption in corporate decision-making processes. Conversely, strategic opportunities arise from targeted upskilling initiatives, AI-enhanced educational tools, and immersive virtual learning environments, which collectively facilitate the acquisition of higher-order analytical competencies and cognitive agility. The study underscores the critical role of personalized, AI-enabled educational interventions in bridging skills gaps and enabling corporate analysts to respond dynamically to evolving market demands.

This research contributes a structured framework for evaluating the nexus between digital intelligence, automation, and workforce development, providing actionable recommendations for policymakers, educators, and corporate leaders. By integrating theoretical insights with applied examples, it establishes a foundation for subsequent empirical investigations into skill development pathways that support sustainable economic growth and organizational resilience.

 

Keywords

References

📄 Asvathitanont, C., Tangjitprom, N., Jaroonsaksit, R., Puasiri, P., & Aunmueng, K. (2024). Policy and Digital Economy and Society Development Action Plan Phase 2 (BE 2566–2570)(AD 2023–2027) and Digital Economy and Society Development Action Plan for Human Resource Development Phase 2 (BE 2566–2570)(AD 2023–2027). In Islamic Finance: New Trends in Law and Regulation (pp. 677–689). Cham: Springer Nature Switzerland.
📄 Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261–271.
📄 Chrysafiadi, K., & Virvou, M. (2024). Perfusit: Personalized fuzzy logic strategies for intelligent tutoring of programming. Electronics, 13(23), 4827.
📄 Electronic Transactions Development Agency. (2021). Digital skills development for government personnel. Bangkok: Electronic Transactions Development Agency. Retrieved from: https://www.etda.or.th/th/Useful-Resource/Knowledge-Sharing/Articles/AI-in-GovernmentServices.aspx.
📄 Intaratat, K. (2021). Digital skills scenario of the workforce to promote digital economy in Thailand under & post COVID-19 pandemic. International Journal of Research and Innovation in Social Science, 10(10), 116–127.
📄 Katsionis, G., & Virvou, M. (2004). A cognitive theory for affective user modelling in a virtual reality educational game. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), vol. 2, pp. 1209–1213. IEEE.
📄 Kitthiwichayakul, B., Hongkham, W., & Kenaphoom, S. (2023). Developing Digital Skills of Government Personnel for Digital Government Transformation. International Journal of Sociologies and Anthropologies Science Reviews, 3(3), 103–114.
📄 Maghsudi, S., Lan, A., Xu, J., & van Der Schaar, M. (2021). Personalized education in the artificial intelligence era: what to expect next. IEEE Signal Processing Magazine, 38(3), 37–50.
📄 Matturro, G., Raschetti, F., & Fontán, C. (2019). A systematic mapping study on soft skills in software engineering. J. Univers. Comput. Sci., 25(1), 16–41.
📄 Naseer, F., Khan, M. N., Addas, A., Awais, Q., & Ayub, N. (2025). Game mechanics and artificial intelligence personalization: A framework for adaptive learning systems. Education Sciences, 15(3).
📄 Office of the National Economic and Social Development Council. The Thirteenth National Economic and Social Development Plan (2023–2027). Available online: https://www.nesdc.go.th/nesdb_en/ewt_dl_link.php?nid=4500 (accessed on 1 July 2025).
📄 Singh, J. (2026). Analytical Study of Challenges and Opportunities for Business Analysts in Emerging Economies Amidst AI and Automation for Evolving Skill Requirements. European Journal of Business and Management Research, 11(1), 107–112. doi: 10.24018/ejbmr.2026.11.1.52852.
📄 Tsihrintzis, G. A., Virvou, M., & Phillips-Wren, G. (2019). Surveys in artificial intelligence-based technologies. Intelligent Decision Technologies, 13(4), 393–394.
📄 Virvou, M., Alepis, E., Tsihrintzis, G. A., & Jain, L. C. (2020). Machine learning paradigms: advances in learning analytics. Springer.
📄 Virvou, M., Manos, C., Katsionis, G., & Tourtoglou, K. (2002). VR-engage: A virtual reality educational game that incorporates intelligence. In Proceedings of IEEE International Conference on Advanced Learning Technologies, pp. 16–19.

How to Cite

Rafael Costa. (2026). Holistic Examination of Difficulties and Strategic Opportunities for Corporate Analysts in Growing Economies Influenced by Smart Automation and Digital Intelligence for Adaptive Skill Development. Global Multidisciplinary Journal, 5(03), 8-21. https://www.grpublishing.org/journals/index.php/gmj/article/view/392

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