Abstract

Background: This thesis examines how artificial intelligence (AI) reshapes risk management in the UAE private sector, with emphasis on project-risk decisions that depend on rapid, data-grounded analysis.

Methodology: An in-depth qualitative multiple-case study of two leading companies, using 30 semi-structured interviews with mid-level managers; data were analysed using Thematic Framework Analysis.

Findings: AI improves the timeliness and accuracy of risk work by (i) engineering cleaner, unified data and real-time exception signals; (ii) automating frontline controls (e.g., computer vision and robotics); (iii) formalising resilience through ERP/DR, reliability runbooks and incident drills; and (iv) building human capability via education partnerships and governance routines. The national AI/4IR agenda acts as a catalyst, while sector-specific tailoring and skills determine translation into practice.

Theoretical contribution: The study extends Diffusion of Innovation (DOI) and Technology–Organisation–Environment (TOE) perspectives to project risk management by identifying policy signalling and organisational reliability as adoption mechanisms, and by specifying sector-fit and talent pipelines as boundary conditions shaping outcomes.

Practical contribution: It offers a five-lever playbook—data plumbing, analytics automation, resilience stack (ERP/DR), skills pipelines, and ethics/privacy governance—that organisations can adapt to strengthen project risk management under uncertainty. Overall, the research clarifies how AI can shift risk management from retrospective reporting to proactive, decision-ready insight in the UAE private sector.