Pitfalls of AI Vendor Selection
AI vendors are numerous and demos look attractive. Pin down selection criteria to not fail on the gap between demo and production operation.
7 Axes to Evaluate
- Accuracy proof: PoC results on your data (don't judge by vendor-provided cherry-picked examples)
- Data handling: whether input data is used for training, storage location/period
- Security certifications: presence of ISMS / SOC2 etc.
- Operational support: SLA on incidents, inquiry structure
- Pricing: usage-billing overshoot risk, minimum charge
- Lock-in: ease of migration, data export possibility
- Business continuity: vendor finances/continuity (startup-dependence risk)
Common Pitfalls
- The demo-environment illusion: works on clean data but accuracy plummets on field data
- Hidden costs: API usage, extra licenses, customization fees
- Vagueness of "AI can do it": ambiguous what's automatic vs manual
- Data takeout: a contract where confidential data goes to training
Selection-Process Pattern
(1) Define problem and evaluation metrics first → (2) same-condition PoC with 2–3 companies → (3) measure accuracy/cost/operating load on your data → (4) legal-check contract terms (data, cancellation, SLA). The trick is don't watch demos before deciding metrics.
Use AI in Selection
Having AI pre-read "surface this proposal's concerns from data-handling, cost, lock-in perspectives" reduces oversights. The next chapter is the in-house break-even.
