Vendor Evaluation Frameworks

Evaluating AI vendors is harder than evaluating traditional software because the performance claims are more difficult to verify and the hidden dependencies are more consequential. A structured evaluation framework should cover technical capability (does it actually work for your use case, tested with your data?), integration requirements (how much effort to connect with your existing systems?), data practices (where does your data go, who can access it, how is it protected?), cost structure (not just the licence fee but the total cost including integration, training, and ongoing usage), and vendor viability (is this company likely to exist and support this product in three years?). Proof-of-concept testing with your own data is essential - vendor demos with curated datasets tell you very little about real-world performance. Pay attention to how transparent the vendor is about limitations; companies that acknowledge what their product can't do are generally more trustworthy than those that claim it can do everything. And talk to existing customers, not just the references the vendor provides but others you find independently.