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Reconfiguring Professional Services and Business Intelligence in the Age of Artificial Intelligence, Edge Computing, and Circular Economy Models

Abstract

The contemporary business environment is undergoing a profound structural transformation driven by advances in artificial intelligence, machine learning, edge computing, and sustainability-oriented economic models. Professional service firms, particularly management consulting and business advisory organizations, are positioned at the intersection of these technological and institutional shifts. This article develops an integrative and theoretically grounded analysis of how emerging digital technologies and circular economy principles are reshaping business intelligence, consulting practices, organizational forms, and value creation mechanisms. Drawing strictly on established literature spanning professional service firm theory, business intelligence, artificial intelligence applications, and educational and cultural competence research, the study synthesizes fragmented scholarly conversations into a coherent analytical framework. The article explores how AI-driven predictive analytics, fraud detection systems, real-time personalized marketing, and IoT-enabled predictive maintenance are redefining consulting value propositions, while circular economy models challenge traditional linear business logics. At the same time, institutional theory and the economics of the firm are used to examine how professional legitimacy, knowledge asymmetries, and organizational boundaries are being renegotiated. The methodology follows a qualitative, integrative literature synthesis approach, emphasizing theoretical elaboration, comparative interpretation, and contextual analysis rather than empirical measurement. The findings suggest that professional service firms are evolving from expertise-based advisory models toward hybrid configurations that combine technological platforms, data-driven insights, and culturally embedded human judgment. The discussion highlights structural tensions, ethical risks, capability gaps, and future research directions, particularly for small and medium-sized enterprises and emerging markets. The article concludes that sustainable competitive advantage in professional services increasingly depends on the alignment of advanced analytics, organizational adaptability, and socially embedded professional practices.

Keywords

Artificial intelligence, business intelligence, professional service firms, management consulting

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References

  1. Amonini, C., McColl-Kennedy, J. R., Soutar, G. N., & Sweeney, J. C. (2010). How professional service firms compete in the market: An exploratory study. Journal of Marketing Management, 26(1–2), 28–55.
  2. Adimulam, T., Bhoyar, M., & Reddy, P. (2019). AI-driven predictive maintenance in IoT-enabled industrial systems.
  3. Bari, M. S., Islam, S. M., Sarkar, A., Khan, A. O. R., Islam, T., & Paul, R. Circular economy models in renewable energy: Technological innovations and business viability.
  4. Brock, D. M. (2006). The changing professional organization: A review of competing archetypes. International Journal of Management Reviews, 8(3), 157–174.
  5. Brock, D. M. (2012). Building global capabilities: A study of globalizing professional service firms. The Service Industries Journal, 32(10), 1593–1607.
  6. Chaudhary, A. A. (2018). Enhancing academic achievement and language proficiency through bilingual education: A comprehensive study of elementary school students. Educational Administration: Theory and Practice, 24(4), 803–812.
  7. Chaudhary, A. A. (2022). Asset-based vs deficit-based ESL instruction: Effects on elementary students’ academic achievement and classroom engagement. Migration Letters, 19(S8), 1763–1774.
  8. Chaudhary, A. A. (2018). Exploring the impact of multicultural literature on empathy and cultural competence in elementary education. Remittances Review, 3(2), 183–205.
  9. Christensen, C. M., Wang, D., & van Bever, D. (2013). Consulting on the cusp of disruption. Harvard Business Review, 3–10.
  10. Coase, R. H. (1937). The nature of the firm. Economica, 4, 386–405.
  11. Das, G. (2013). Market for consulting still maturing in India: Interview with Jim Moffatt. Business Today.
  12. David, R. J., Sine, W. D., & Haveman, H. A. (2013). Seizing opportunity in emerging fields: How institutional entrepreneurs legitimated the professional form of management consulting. Organization Science, 24(2), 356–377.
  13. Economist, The. (2002). Management consulting: Consultant, heal thyself. The Economist, 54–62.
  14. Economist, The. (2013). To the brainy, the spoils. The Economist.
  15. Fincham, R., Mohe, M., & Seidl, D. (2013). Management consulting and uncertainty: Mapping the territory. International Studies of Management and Organization, 43(3), 3–10.
  16. Fortune Magazine. (2003). The incredible shrinking consultant. Fortune, 49–51.
  17. Furusten, S. (2013). Commercialized professionalism on the field of management consulting. Journal of Organizational Change Management, 26(2), 265–285.
  18. Islam, T., Islam, S. M., Sarkar, A., Obaidur, A. J. M., Khan, R., Paul, R., & Bari, M. S. Artificial intelligence in fraud detection and financial risk mitigation: Future directions and business applications.
  19. Kovalchuk, A. (2025). Complex model of business consulting for small and medium-sized enterprises. Theory, methodology and practice of implementation. https://doi.org/10.25313/kovalchuk-monograph-2025-90
  20. Paul, R., Islam, S. M., Sarkar, A., Khan, A. O. R., Islam, T., & Bari, M. S. The role of edge computing in driving real-time personalized marketing: A data-driven business perspective.
  21. Rahaman, M. M., Rani, S., Islam, M. R., & Bhuiyan, M. M. R. (2023). Machine learning in business analytics: Advancing statistical methods for data-driven innovation. Journal of Computer Science and Technology Studies, 5(3), 104–111.
  22. Selvarajan, G. P. (2019). Integrating machine learning algorithms with OLAP systems for enhanced predictive analytics.
  23. Selvarajan, G. P. The role of machine learning algorithms in business intelligence: Transforming data into strategic insights.

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