Cheat sheetAII-01

Enterprise AI Strategy

AI Intelligence / Enterprise AI Strategy

Strategy, not model access, is the durable differentiator. Align to business value, pick use cases on value and feasibility, confirm data readiness, and choose build-vs-buy deliberately.

1
Value alignmentEvery initiative names a measurable outcome: revenue, cost, risk, cycle time, or experience.
2
Value x feasibilityScore use cases on both axes; sequence early feasible wins to fund harder bets.
3
Data readinessData must be accessible, clean enough, governed, and legally usable for the specific purpose.
4
Build vs buyA spectrum. Build only for genuine advantage or data that cannot leave the boundary.
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Before approving an AI project, force a one-page answer to: which outcome, why feasible now, is the data ready, and why build rather than buy? No answer means no go.

Sequenced winsShip a feasible claims-summarisation copilot first; reinvest proven savings into harder underwriting AI.
Sourcing by advantageBuy commodity OCR; build the proprietary pricing model that uses confidential customer data.
Failure mode: "adopt AI" as the goal instead of a business outcome.
ROI must include integration, change management, monitoring, and maintenance.
Pilot purgatory = many pilots, no production; fix with outcome-linkage and focus.
strategybuild-vs-buyuse-case-selectionroidata-readiness
review in 6d