The AI vendor landscape has grown crowded enough that differentiating genuine enterprise-grade platforms from thin wrappers around a public API has become its own skill. A structured due-diligence process protects against costly mistakes that only surface months after signing.
Look Past the Demo
Vendor demos are optimized to impress, not to reflect your actual data and edge cases. Insist on a pilot using your own real (or realistically representative) data before committing, and pressure-test the system with the messy, ambiguous inputs your business actually produces, not the clean examples in the sales deck.
Interrogate Data Handling and Security
Ask precisely where your data goes, whether it’s used to train the vendor’s underlying models, how long it’s retained, and what happens to it if you terminate the contract. Enterprise buyers should require clear, contractual answers here, not verbal assurances — this is the area where vague answers create the most downstream risk.
Evaluate Total Cost and Exit Flexibility
Factor in integration, training, and ongoing management costs beyond the license fee, and assess how portable your data and workflows would be if you needed to switch vendors later. Heavy proprietary lock-in isn’t automatically disqualifying, but it should be priced into the decision explicitly.
Rigorous vendor due diligence takes longer upfront, but it’s substantially cheaper than unwinding a bad enterprise AI contract a year in.