AI Strategy

AI Vendor Due Diligence: How to Evaluate Claims

By Jeff Wray •

AI vendors are arriving faster than most leadership teams can evaluate them. The right question is not whether the demo looks impressive. The right question is whether the vendor can deliver a maintainable, secure, measurable system inside your real business.

Start With the Business Decision

A useful AI project should connect to a specific outcome: faster intake, better support triage, cleaner reporting, safer document review, reduced manual reconciliation, or better decision support. If the vendor cannot explain the operational result in plain English, the technical conversation is starting in the wrong place.

Five Claims Worth Testing

1. "Our AI understands your business."

Ask what documents, data, rules, examples, and review process are needed before it can produce reliable work.

2. "This will save time immediately."

Ask which tasks will be reduced, which human checks remain, and how savings will be measured after launch.

3. "The data is secure."

Ask where data is stored, which providers receive it, how long it is retained, and who can access logs or outputs.

4. "It integrates with your systems."

Ask for a data-flow diagram, authentication method, failure handling, and who owns each integration credential.

5. "It is ready for production."

Ask about monitoring, cost controls, support response, versioning, rollback, and user permissions.

What Technical Depth Sounds Like

Strong vendors do not hide behind vocabulary. They can explain tradeoffs, failure modes, implementation options, and maintenance in language your leadership team can understand. They can also speak clearly with your developers, security reviewers, and operations people.

Good evaluation questions:

  • What will this system do when confidence is low?
  • What human review is required before customer-facing output?
  • How are prompts, configuration, and integrations versioned?
  • How do we control monthly usage cost?
  • What is the support model after implementation?
  • What would make you recommend not using AI here?

Proof Beats Personality

A confident salesperson may still represent a capable team. A technical founder may still underestimate operational risk. Evaluate the work product: diagrams, sample deliverables, references, implementation plan, support commitments, and ownership terms.

The Bottom Line

AI due diligence is not about being cynical. It is about protecting the business from vague scope, unmanaged data risk, hidden dependencies, and tools that look impressive in a demo but are hard to operate every month.

Need a second set of technical eyes?

A fractional CTO can review AI proposals, vendor claims, implementation risk, and ownership before you sign.

Contact Jeff