AI Strategy
Build, Buy, or Partner? A Framework
The most consequential AI decisions are not about models. They are about what to own, what to rent, and what to build together.
Every serious AI agenda eventually reduces to a series of build, buy, or partner decisions. They are made constantly, often implicitly, and often wrongly, because they are treated as procurement choices rather than strategy choices.
Start from advantage, not availability
The first question is not "what can we buy?" It is "where does owning this create durable advantage?" Build where the capability is close to your competitive core and the data is yours. Buy where the capability is undifferentiated and a market has formed. Partner where the capability matters but neither ownership nor a vendor relationship is sufficient on its own.
Build where it differentiates. Buy where it doesn't. Partner where you cannot win alone.
The criteria that actually decide it
- Strategic differentiation: does owning this change your competitive position?
- Data advantage: is the proprietary data on your side of the boundary?
- Rate of change: is the technology stabilizing or still moving fast?
- Total lifecycle economics, not first-year cost.
- Organizational capacity to operate what you build.
The value in AI is migrating from models to applications to how intelligence is wired into a specific decision, workflow, and dataset. That migration should shape these choices. Buying a model is increasingly commodity. Building the application layer on top of proprietary data and decisions is increasingly where advantage lives.