Evaluating and scoring AI vendors
Score data security and handling, integration fit with your stack, transparency about model behavior and cost, and the strength of their implementation partnership. Weight partnership heavily, it is what doubles success rates.
A practical scoring rubric
Rate each vendor on security, integration, transparency, and partnership, then weight partnership most heavily because it is the strongest predictor of a successful implementation.
- Data security and handling: where your data goes, how it is stored, and whether it trains their models.
- Integration fit: native connectors to your stack versus custom plumbing.
- Transparency: model behavior, cost at scale, and a clean exit path.
- Implementation partnership: hands-on onboarding, not just a login, the 67% vs 33% factor.
Feature checklists lie because every vendor demo looks good. Scoring forces you to weigh the factors that actually determine outcomes. Partnership deserves the heaviest weight: the same research that finds a 67% success rate for vendor-partnered implementations finds only about 33% for internal-only efforts, and a vendor who abandons you at go-live effectively turns a buy into an unsupported build.
Frequently asked questions.
Should I run a paid pilot before committing?
Yes, a short paid pilot on your real data is the best predictor of fit. It reveals integration friction and support quality that no demo shows, and it tests the vendor’s willingness to partner. Treat the pilot itself as a 90-day roadmap with a clear success threshold.