A Skeptic's Framework for Evaluating AI Vendors

A vendor demo is, definitionally, the case where their product works. That's not dishonest, it's just not the question you need answered. The question is how the product behaves on your data, in your edge cases, at your volume, and that's almost never what a sales demo shows you.

The first thing I ask for is a trial against a held-out sample of my own data, not the vendor's curated examples. If a vendor resists this, that's information. A product confident in its own accuracy numbers usually welcomes a fair test.

The second is what happens when it's wrong. Every AI system has a failure rate above zero; the question is whether failures are visible and correctable, or silent and confidently stated. A tool that says 'I'm not sure' sometimes is more trustworthy than one that never hedges.

The third is the exit plan. What does it cost to leave (in data lock-in, retraining, or process dependency) if the vendor's roadmap diverges from what you need in a year? A good vendor relationship survives that question being asked out loud.

None of this is about being anti-AI-vendor. It's the same diligence you'd apply to any dependency you're about to build a workflow around, applied honestly instead of getting swept up in a good demo.