I help founders, CEOs and functional leaders turn pressure to “do something with AI” into a small number of valuable decisions—then design and deliver the systems worth building. Twenty years in technology, with implementation teams in Dubai and Riyadh.
A useful AI strategy is not a catalogue of tools. It identifies an expensive business friction, tests whether AI is the right mechanism, and defines what must change in data, workflow, ownership and risk for the result to last.
Map opportunities against business impact, technical feasibility, data readiness and operating risk. Leave with a ranked shortlist, not an innovation wish list.
Define the owner, workflow boundary, measurable result, build-versus-buy decision and realistic path from pilot to production.
Stay accountable through architecture, vendor selection, prototype, integration, adoption and measurement—with engineering available when the answer needs building.
The engagement begins with a business problem. The technology is selected only after the workflow, economics and constraints are clear.
Turn competing proposals into a decision framework leadership can defend.
Find the missing operating change, ownership or trust that prevents adoption.
Design private or controlled AI architecture around real security and data-residency requirements.
The goal is not to keep an AI programme alive. It is to create enough evidence to scale, change or stop it intelligently.
Map the actual workflow, exceptions, cost of delay, available data and accountable owner.
Test the smallest complete loop against real work and real users—not a curated demonstration.
Measure value, implementation cost, operating change and risk before committing further capital.