The AI vendor evaluation questions that actually predict failure

A typical AI vendor evaluation is a feature comparison: what can it do, how accurate is it, what's the price. Those questions matter, and they're also not the ones that predict whether a business is still using this tool in eighteen months, which is a different and more useful question to be asking.

The predictive questions are about the vendor's own trajectory and your exit costs: how is the company funded and how long is that funding likely to last, how portable is your data if you need to leave, and how much of your workflow logic lives inside their proprietary system versus in something you control.

I've watched more AI tool adoptions fail from the vendor being acquired, pivoting, or shutting down a feature than from the tool itself being technically inadequate. The technical evaluation gets all the attention; the vendor-risk evaluation is what actually determines whether this was a good decision in retrospect.

None of this means avoiding smaller or newer vendors, sometimes they're the only ones solving the actual problem. It means going in with a data-portability plan and a realistic sense of what happens to your workflow if the vendor doesn't exist in two years.