AI Strategy and Discovery
Most AI projects go wrong before a single model gets trained, in the gap between what an Abu Dhabi institution assumed its data could support and what it actually can. This work opens with a plain, sometimes uncomfortable look at whether the data is genuinely ready.
- Data readiness and quality assessment before any commitment is made
- Use-case prioritization based on real institutional impact, not novelty
- Build-versus-buy analysis for off-the-shelf APIs versus custom models
- ADGM or federal jurisdiction mapping built into the roadmap where relevant































