How to Evaluate an AI Vendor Without Getting Sold a Demo
The questions that tell you whether an AI vendor can actually deliver — before you sign anything.
TL;DR / Key Takeaways
- A polished demo tells you almost nothing about whether an AI vendor will work for your business.
- Before signing, ask the vendor to run a live test on your actual data, not their curated examples.
- You need to know who owns your data, what happens when the AI is wrong, and how support actually works.
- Define what success looks like at 30 days before you pay, not after.
- The right vendor will welcome these questions. The wrong one will dodge them.
The Demo Is Not the Product
AI vendors are good at demos. Clean data. Smooth interface. Confident presenter. Everything works exactly right.
Then you sign up, load your actual data, and nothing looks like what you saw on the call.
That is not always the vendor's fault. Demos are built to show the best case. But it is your job to find out what the real case looks like before you hand over money.
Here are the questions I walk clients through when they are evaluating an AI vendor.
Ask for a Live Test on Your Data
This is the most important thing you can do.
Before any contract is signed, ask the vendor to run a live demonstration using a sample of your real data. Not their demo data. Not a generic industry example. Yours.
This does not have to be sensitive. You can anonymize names or numbers. But it should reflect your actual workflow — your format, your messiness, your edge cases.
If the vendor refuses, that tells you something. If they agree but the results look worse than the demo, that tells you more.
A good vendor will welcome this. They want to show you the product actually works. A vendor who hedges and steers you back to their curated example is protecting the illusion.
Who Owns Your Data?
This question makes some vendors uncomfortable. Ask it anyway.
When you run your data through their platform, who owns it? Is it stored? Is it used to train their models? Can you delete it? What happens to it after your contract ends?
Some vendors feed customer data back into model training. Some store it in ways you did not expect. Some have data residency requirements that matter if you are in a regulated industry or have customers outside the US.
You do not need to understand every technical detail. You do need a clear, written answer.
If the vendor cannot tell you plainly where your data goes and who controls it, that is a problem before you even get to the AI.
What Happens When the Output Is Wrong?
AI makes mistakes. That is not a dealbreaker. What matters is how the system handles it.
Ask the vendor: what does an error look like in your product? How often does it happen? What does the user do when the AI gives a bad answer?
Watch for vague answers here. "Our accuracy is very high" is not useful. You want to know what the failure mode is and what your team does when it shows up.
A good answer includes a real example of a failure, what caused it, and how the product handles it going forward. A bad answer is reassurance without specifics.
Also ask whether there is a human review step built into their workflow or whether the AI output goes straight into action. For anything consequential — customer communications, financial decisions, compliance-related tasks — you want a review step.
How Does Support Actually Work?
This is the question that separates vendors from software subscriptions.
Ask them exactly what happens when something breaks. Is there a support ticket? A chat bot? A phone number? A dedicated contact?
What is the response time? What is the escalation path if the issue is not resolved?
If you are running this tool in a business-critical workflow and it stops working on a Tuesday afternoon, you need to know who you are calling and how long it will take to hear back.
Some AI tools have solid software and terrible support. You only find that out after you are depending on them.
Define What Success Looks Like at 30 Days
This is the conversation most vendors do not push for, because it creates accountability.
Before you sign, ask them: what does success look like 30 days after we go live? What specific outcome should we see?
Then write it down. Not in legal language. Just a shared understanding of what you are both working toward.
Maybe success is that a task that used to take four hours now takes one. Maybe it is that a report gets generated automatically every Monday. Maybe it is that your team actually uses the tool instead of going back to the old way.
If the vendor cannot help you define that, they are selling you software, not a solution.
A Few More Questions Worth Asking
- What does the onboarding process look like, step by step?
- Who on your team has done this kind of implementation before?
- Can I talk to one of your current customers in a similar business?
- What does it take to cancel if it is not working?
- What does the pricing look like at higher usage? (Some AI tools get expensive fast as volume grows.)
None of these are adversarial. They are the same questions you would ask before hiring a contractor or switching accounting software.
You Are Allowed to Slow Down
The sales process for AI tools often has an artificial urgency to it. Limited-time pricing. Pilot program slots running out. A competitor is already using this.
None of that is a reason to skip due diligence.
A vendor who cannot answer these questions clearly is not ready to be your vendor. A good AI implementation starts with a vendor relationship built on honest expectations, not a demo that nobody can reproduce.
Take the time to ask the hard questions now. It will save you a painful conversation three months from now when the tool is not doing what you thought you were buying.
If you are working through an AI tool evaluation and want a second opinion on what you are looking at, that is exactly the kind of work I do.