How to Evaluate an AI Vendor Without Getting Sold To

Most AI vendor evaluations in biopharma are structured to be won. The deck is polished, the case study is anonymized, and the reference call is with a champion who has already committed. This is not a screening process; it is a ratification process. What follows is a rubric designed to make ratification harder.

1. Ask what the model does, then ask what decision it changes

Any vendor can describe a model. Fewer can name the specific decision — the meeting, the person, the artifact — where the model's output enters the workflow. If the answer is vague ("informs research strategy"), the deployment will be too.

2. Demand a failure mode inventory

Serious teams know where their model is weak. Ask directly: "What are the three chemotypes / disease areas / process conditions where your model performs worst, and why?" A vendor who cannot answer this either does not know their model or does not want you to.

3. Data lineage, not data size

"We trained on 500 million compounds" is not a specification. The right questions are: What was the deduplication strategy? What proportion is public vs. licensed vs. proprietary? What is the temporal cutoff, and how do you handle post-cutoff data leakage in your benchmarks? If a vendor cannot produce a data card, the model is not audit-ready.

4. Benchmarks that reflect your problem

Public benchmarks are useful for capability screening and useless for procurement. Insist on a paid pilot on your held-out data, with success criteria written before the pilot starts. If the vendor resists a blinded evaluation, that is your answer.

5. The integration question

Ask to see the actual user surface. If the "product" is a Jupyter notebook a data scientist runs on request, you are buying a service, not a product — price and staff accordingly. Real products have SSO, audit logs, and version pinning.

6. Uncertainty quantification, in writing

Every prediction should ship with a calibrated confidence. Ask how the model expresses uncertainty, how that uncertainty was validated, and what happens in the UI when confidence is low. "The scientist decides" is not an answer; it is a liability transfer.

7. The exit clause

What happens to your data, your trained instances, and your workflow if the vendor is acquired, pivots, or shuts down? A vendor who has not thought about this is not a serious enterprise partner.

A note on price

The best AI vendors in this space are not cheap. They should not be. But price should track measurable value — a per-decision, per-cycle, or per-outcome model — not seat counts. If the pricing conversation cannot connect to the value conversation, walk away.

Sources

1. AI Risk Management Framework (AI RMF 1.0) — National Institute of Standards and Technology.
2. General Principles of Software Validation — U.S. Food and Drug Administration.
3. Regulation (EU) 2024/1689 laying down harmonized rules on artificial intelligence — Official Journal of the European Union.