Attach AI to a real workflow
Begin with the repeated decision or operational bottleneck, then choose the smallest model role that improves it.
AI SYSTEMS / EMERGING MARKETS
AI products need local operating context
A practical lens for deploying AI utilities and agent systems where language, cost, trust, workflows, and infrastructure differ from the model demo.
Discuss this marketTHE OPERATING THESIS
Model capability is only one layer of an AI product. Useful deployment depends on the local workflow, data quality, cost tolerance, escalation path, language performance, and consequences of an incorrect action.
WHERE EXPANSION BREAKS
Problems this framework addresses
- Model demos mistaken for complete products
- Automation coverage prioritized over safe handoff
- English performance assumed to transfer across languages
- Infrastructure cost disconnected from local willingness to pay
FIELD PRINCIPLES
How I approach it
Design the failure path
Confidence, review, escalation, and accountability are part of the user experience.
Measure economics per useful outcome
Token cost matters only in relation to a completed task, recovered booking, reduced delay, or improved decision.
MARKET BRIEFINGS
Go deeper on the decision
RELATED PUBLIC PROOF
Projects and decisions
ADVISORY / MENA + SOUTHEAST ASIA