Leadership and trust, not tech, drive AI success in the GCC

Redefining the AI question

Their case studies of UltraTech Cement and Crisil illustrate that success depends more on leadership-driven redesign of decision-making, experimentation, and governance than on the technology itself.

Stop demanding immediate ROI

UltraTech measured success by decision quality, operational effectiveness, and scalability across plants, while Crisil used AI to augment analysts, accelerating research and freeing time for judgement. The authors argue that early value often appears as faster learning and stronger capability rather than direct profit.

Speed versus control

UltraTech partnered with startups and moved quickly in manufacturing, where errors could be contained. Crisil, dealing with financial analysis and regulatory risk, prioritized internal development, iterative testing, and human oversight. The authors stress that the appropriate balance of speed and control varies by context and must be set by leadership.

Trust as the real barrier

Human confidence in AI’s reliability and transparency emerged as the greatest adoption hurdle. Both case studies involved end users—plant operators and analysts—throughout development, fostering trust without forcing adoption. The authors cite PwC’s AI Jobs Barometer, which shows UAE AI job postings have more than tripled since 2021, underscoring the need for workforce confidence.

Our read: The article convincingly shifts the AI conversation from procurement to organisational design, offering three board-level questions that crystallise this shift. By grounding the argument in concrete case evidence, it provides GCC leaders with a practical lens to assess where rapid experimentation adds value and where tighter governance protects risk, making the insight directly actionable for firms navigating the region’s AI boom.