Artificial intelligence (AI) is implemented across various areas within organisations, including forecasting, workflow support, content generation, and decision assistance. However, in many large and regulated organisations, AI has yet to progress beyond pilots and fragmented experiments into standard operating practice. This ongoing research-in-progress examines that gap as an organisational and governance issue rather than a technical challenge.
The study explores key areas such as operating model design, decision rights, workflow integration, human oversight, governance maturity, and workforce implications to understand how AI-augmented decisions can be scaled effectively. Another issue is that AI is frequently presented not only as a productivity-enhancing tool but also as one that can reduce labour costs, thereby creating friction between the ambitions of automation and the desire for accountability, trust and organisational legitimacy.
The project adopts a mixed-methods approach, including purposive interviews with senior leaders and an exploratory survey regarding AI adoption, governance, and organisational readiness. It aims to provide a useful roadmap for organisations that wish to migrate AI projects from successful pilots to an auditable, defensible and operationally embedded practice, without reducing governance to a post hoc control exercise.
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Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210.
National Institute of Standards and Technology. (2023). AI risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce.
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of artificial intelligence. Information and Organization, 28 (1), 62–70.
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.
Baygi, R., & Huysman, M. (2025) Generative AI and the social fabric of organizations. Sage Journals.
Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372.
Saldanha, T. (2019). Why digital transformations fail: The surprising disciplines of how to take off and stay ahead. Berrett-Koehler Publishers.
Mollick, E. (2024). Co-intelligence: Living and working with AI. Portfolio.
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350.
Maas, M. M. (2025) Architectures of Global AI Governance: From Technological Change to Human Choice. Oxford Academics.
Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review Press.
Mollick, E. (2026). Navigating the Jagged Technological Frontier. SSRN.
Olson, P. (2024). Supremacy: AI, ChatGPT, and the race that will change the world. St. Martin’s Press.
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