How to evaluate enterprise AI vendors: 9 due diligence questions every decision maker should ask

Last quarter, a manufacturing CIO spread three vendor presentations across his desk and asked us: "These three demos look pretty similar. Who should I pick?" He had a 40-page feature comparison matrix filled with checkmarks. But scanning through it for a couple of minutes, I realized the entire sheet answered "What can this system do?" without a single cell addressing "Will anyone actually use it after go-live?"

This is the most common trap when evaluating enterprise AI vendors. Feature lists are easy to build. Demos always look good. What determines project success or failure is the questions suppliers won't bring up; the ones you have to push them to answer. The nine questions below come from our actual work during procurement meetings with clients. You can ask them directly.

1. Deployment and adoption: the reality beyond the demo

Where does your delivery responsibility end? Who's responsible for pushing the system into production? Most vendors write into their contracts that they'll "deliver a working system." Period. What they mean is they hand you a functioning environment, and everything else (implementation, integration, staff training, getting people to actually use it) is your problem. Look for an answer that specifically states something like "Our engineers will be on-site, and we're responsible for getting to production and hitting the usage targets we've agreed to."

What's the actual daily active usage rate for this system among customers in our industry and at our scale? Don't ask how many customers they have. Ask about usage rate. We once took over a deal where a client had purchased an AI customer service platform two quarters before. The contract signed quickly. Actual usage rate: 4%. Not because the product was bad, but because it was designed for typical customers, and this company wasn't typical in any way.

How do our data get in and out? What's the cost to move our data elsewhere? Data going in but not coming out is the most expensive kind of lock-in. Demand a specific answer: "If we move in three years, what format will we export our data in, and what will it cost?" Vendors who can't answer this question are usually counting on migration costs to keep you trapped.

2. Reliability and security: who takes the hit when things break

When the model gets it wrong or hallucinates, how does that get caught? Is there a human in the process reviewing outputs? Generative systems fail. What matters is whether failures get caught. Ask specifically about human-in-the-loop design, confidence score thresholds, and how errors get reported.

Who can access our data? Could it be used to train models? Get it in writing: where your data will be stored, who can access it, and exactly what the clause says about your data not being used to train general models. Financial services and healthcare clients especially should make this binding in the contract.

Who's responsible for operations after we go live? Who handles model version updates, and who pays for them? Operations don't end when the system goes live. The underlying model gets updated every few months. Sort out the operations handoff upfront so you're not stuck with a slowly degrading system in six months.

3. Results and exit: binding commitments to contract

How do we measure success? Do KPIs go into the contract? Vendors that tie their payment to outcomes operate differently than those just selling licenses. Push for jointly defined, measurable success criteria: something like "specific process handling time drops by 30% within six months."

How long is a complete implementation? How many of our people will you need? This question uncovers hidden costs. What vendors call "two weeks to go live" typically doesn't count the three engineers from your side and the countless meetings you'll have to run.

What's the exit clause if this doesn't work out three months in? Vendors confident in their delivery usually offer reasonable off-ramps.

Quick reference: all nine questions

Questions 1, 2, and 7 best filter demo-only vendors. We do field deployment work ourselves and apply one test: pretty presentations mean nothing. Production use with active adoption is what counts. That's why we embed engineers directly at customer sites and tie KPIs to contracts. These same questions work as well when evaluating us.