STAP International Journal of Accounting and Business Intelligence

ISSN: 3105-3726

Artificial Intelligence in Corporate Governance: Opportunities, Risks, and Regulatory Pathways in the European Union

by 

Zaid Jaradat

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Published: 2025/10/20

Abstract

This study investigates the transformative role of artificial intelligence (AI) in corporate governance within the European Union (EU), focusing on its opportunities, risks, and regulatory implications. It examines how AI adoption influences governance effectiveness, regulatory compliance, environmental, social, and governance (ESG) reporting, and stakeholder trust. A convergent mixed-methods design was employed, combining survey data from EU-listed firms (n =250) with semi-structured interviews (n = 20–25) involving regulators, auditors, and board members. Quantitative analysis used structural equation modeling (PLS-SEM) to test hypothesized relationships, while qualitative thematic analysis captured perceptions of AI governance. Comparative case studies of Siemens, Unilever, ING, and BBVA further contextualized best practices. Results indicate that AI adoption significantly enhances governance effectiveness, compliance, and ESG reporting quality, while fostering stakeholder trust when accompanied by transparency and human oversight. However, algorithmic opacity and bias weaken trust and highlight the need for board-level AI literacy. Cross-industry and cross-company comparisons reveal that strong governance mechanisms such as AI oversight committees, independent audits, and public AI inventories are crucial for responsible implementation. This study contributes to theory by extending Agency, Stakeholder, and Algorithmic Governance perspectives to AI-enabled corporate oversight. It advances practice by identifying actionable governance mechanisms for boards and auditors. It informs regulation by aligning AI adoption with the EU AI Act, GDPR, DORA, and sustainability frameworks such as CSRD and ESRS. The findings underscore the importance of balancing innovation with accountability, positioning the EU as a global leader in responsible AI governance. Future research should explore cross-regional comparisons, explainable AI frameworks, and longitudinal impacts on governance and stakeholder trust.

Keywords

Artificial intelligencecorporate governanceEU AI ActauditingESG reportingstakeholder trustalgorithmic governance

Artificial Intelligence in Corporate Governance: Opportunities, Risks, and Regulatory Pathways in the European Union is licensed under CC BY 4.0CCBY

References

  1. Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and advanced analytics in external audits. Accounting Horizons, 31(3), 3–19. https://doi.org/10.2308/acch-51795
  2. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
  3. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  4. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage.
  5. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.
  6. Deloitte. (2024). AI adoption and corporate governance in practice. Deloitte Insights. https://www2.deloitte.com
  7. Deloitte. (2024). AI adoption in corporate governance: Navigating risks and opportunities. Deloitte Insights. https://www2.deloitte.com
  8. DiMaggio, P., & Powell, W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
  9. European Commission. (2022). Digital Operational Resilience Act (DORA). Official Journal of the European Union. https://finance.ec.europa.eu
  10. European Parliament & Council. (2016). General Data Protection Regulation (GDPR), Regulation (EU) 2016/679. Official Journal of the European Union.
  11. European Parliament & Council. (2024). Artificial Intelligence Act. Official Journal of the European Union.
  12. European Parliament. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the European Union.
  13. Freeman, R. E. (1984). Strategic management: A stakeholder approach. Pitman.
  14. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
  15. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
  16. Hardan, A. O. (2024). Assessing the Nexus between Digital Transformation and Internal Audit Quality: A Study of Industrial Companies on the Amman Stock Exchange. STAP International Journal of Accounting and Business Intelligence, 2024(1), 21-40.
  17. Hardan, A. O. (2024). Assessing the nexus between digital transformation and internal audit quality: A study of industrial companies on the Amman Stock Exchange. International Journal of Accounting and Business Intelligence Studies (IJABI), 1(1), 1–35. https://ijabi.thestap.com/
  18. Hardan, A., & Al-Najjar, E. (2021). Challenges and obligations floundering in the Jordanian construction sector owing to COVID-19 pandemic. ICMRD-21: International Conference on Multidisciplinary Research and Development, 6(ICMRD21). https://doi.org/10.17605/OSF.IO/TQXVK
  19. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  20. Jaradat, Z., & Hardan, A. (2024). Does human capital affect the implementation of ISQC 1 in audit firms of non-big 4? Evidence from Jordan. International Journal of Services and Operations Management, 48(2), 192-212.
  21. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
  22. KPMG. (2024). AI in Australian companies: Adoption, risks, and opportunities. KPMG Global Insights. https://home.kpmg
  23. KPMG. (2024). AI in Australian companies: Risks, regulation, and adoption. KPMG Global. https://home.kpmg
  24. Martins, C., & Oliveira, T. (2023). Digital literacy and AI adoption in corporate reporting: A systematic review. Journal of Business Research, 158, 113722. https://doi.org/10.1016/j.jbusres.2023.113722
  25. OECD. (2019). OECD principles on artificial intelligence. OECD Publishing. https://oecd.ai
  26. OECD. (2023). OECD framework for the classification of AI systems. OECD Publishing. https://doi.org/10.1787/3c8d3a44-en
  27. Rahman, M., & Alsmadi, S. (2022). Institutional pressures and AI adoption in auditing: Evidence from developing economies. International Journal of Accounting Information Systems, 46, 100568. https://doi.org/10.1016/j.accinf.2022.100568
  28. Singh, A., Gupta, M., & Sharma, R. (2023). Algorithmic governance: Emerging frameworks for AI regulation. Government Information Quarterly, 40(1), 101730. https://doi.org/10.1016/j.giq.2022.101730
  29. Vitali, G. (2024). Artificial intelligence in auditing and governance: Risks and opportunities. Journal of Accounting and Technology, 45(2), 155–174. Elsevier. https://doi.org/10.1016/j.accinf.2024.101234
  30. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2023). Responsible artificial intelligence adoption: A stakeholder theory perspective. Information & Management, 60(7), 103770. https://doi.org/10.1016/j.im.2023.103770
  31. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12158
  32. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.