When building AI actually makes sense
Build only when the capability is core to your differentiation, powered by proprietary data no vendor has, and you have the engineering capacity to sustain 15–30% annual maintenance indefinitely. All three must be true.
The three conditions for building
Building is justified only when a capability is core, data-differentiated, and sustainable given your engineering capacity. Miss any one and buying or integrating wins.
There is a real place for building, it is just narrow. When a capability genuinely differentiates you, your proprietary data makes your version measurably better than any vendor could, and you have the engineering depth to carry the maintenance curve without starving other work, building can create a durable moat. The failure mode is building for reasons that feel good but are not core: prestige, distrust of vendors, or the belief that custom is inherently better. It is not, the success data favors partnership roughly two to one.
Frequently asked questions.
What if we have engineers but no AI specialists?
General engineering talent is not the same as the ML, data, and MLOps skills a custom AI build needs long-term. If you would have to hire specialists whose cost rises 30–50% per year to maintain it, that strongly favors buying or integrating rather than building.