AI vendor due diligence checklist for SMB

After watching 30+ founders try to fix this, my conclusion is: most AI vendor evaluations skip the operating reality and focus only on demos.

How to structure your AI vendor due diligence

Start with the contract. Look for three things: who owns the data you input, what happens if you want to leave, and what the vendor is liable for if the tool fails or leaks data. Many SMB contracts silently grant the vendor broad rights to use your inputs for model training. Push back. Ask for a clause that says your data stays yours and is deleted on request.

Next, assess change management. A vendor that only offers login credentials and a knowledge base will leave you stuck at 20% adoption. Ask: What training do you provide? How many hours of live support are included in the first 90 days? Do you help map our current workflows to the tool, or do we figure that out alone? The best vendors treat adoption as part of the sale, not an afterthought.

Tool tip (AIAdvisoryBoard.me):

When reviewing an AI vendor, use the Plan → Fact → Gap lens: What do they promise in the contract (Plan)? What have other SMBs actually experienced in the first 30 days (Fact)? Where is the mismatch (Gap)? This turns vague promises into observable risks.

Data security and compliance check

Before signing, run a 7-day check: Request the vendor's data processing agreement (DPA). Confirm where data is stored (EU, US, etc.) and whether sub-processors are listed. Ask for their ISO 27001 or SOC 2 report — or a summary if the full report is under NDA. Test a small data upload: Can you delete it fully and get confirmation? If the vendor hesitates or says deletion isn't possible, walk away.

Micro-case (what changes after 7–14 days)

A 40-person logistics company used this checklist to evaluate two AI routing vendors. One offered a lower price but refused to share its DPA or clarify data residency. The other provided a clear DPA, EU-based storage, and a change management plan that included two live workflow workshops. After 10 days, the team had mapped their current dispatch process and identified where the AI would plug in — not replace — their planners. The founder reported feeling confident in the decision, not because of the demo, but because the operating realities were visible upfront.

Note on this case: This example is illustrative — based on typical patterns we observe with companies of 30–500 employees, not a single named client. Specific numbers are rounded approximations of common ranges, not guarantees.

FAQ

What if the vendor won't share their DPA? Treat it as a red flag. A refusal to share a data processing agreement often means they either don't have one or don't want you to see how data is handled.

How much change management support should I expect? For a team of 20–50, look for at least 4–6 hours of live training or workflow mapping in the first month, plus office hours for questions.

Should I involve legal in the review? Yes — but only after you've checked the operational fit. Legal can spot liability risks; you spot whether the tool actually matches your workflow.

Conclusion

AI vendor due diligence isn't about finding the perfect tool — it's about avoiding the predictable pitfalls that turn pilots into sunk costs. Focus on contracts, change management, and data security before you fall in love with a demo.

Run your own 7-day vendor check using the Plan → Fact → Gap framework — see how the diagnostic works.