How to Evaluate AI Vendors Without Getting Burned
AI vendors are everywhere right now. Every software company you talk to has an AI story. Some of those stories are real. Many are not.
The problem is that it’s getting harder to tell the difference. The demos look great. The case studies sound impressive. The pricing is bundled into your existing contract so it feels like you’re getting something for free. And by the time you realize the product doesn’t actually do what you needed, you’re locked in.
This guide is for IT Directors and CIOs who are tired of buying things that don’t work. Here’s a practical framework for evaluating AI vendors before you sign anything.
Start With Your Own Problem, Not Their Demo
The biggest mistake IT leaders make when evaluating AI is letting the vendor define the use case.
A vendor will always show you the scenario where their product looks best. That scenario may have nothing to do with your actual environment. You need to walk into every evaluation with a clear problem you’re trying to solve. Not “we want to use AI.” Something specific.
- We spend 40 hours per month manually categorizing support tickets.
- Our analysts are drowning in alerts and can’t triage fast enough.
- We need to reduce time-to-answer for common HR and IT policy questions.
When you have a specific problem, you can test whether the vendor actually solves it. When you don’t, you end up buying a solution that works great in the demo and sits unused six months later.
Write your problem statement before you take a single vendor call.
Ask How the Model Works, Not Just What It Does
You don’t need to become an AI engineer. But you do need to ask some basic questions about how the technology works. Here’s why: the same output can come from very different architectures, and those differences matter for security, compliance, and reliability.
Ask vendors these questions:
Where does the data go? When a user asks a question, does that input get sent to a third-party model? Which one? Is it retained? For how long?
Is the model trained on your data? Some tools fine-tune a model using your data to improve results. That can be valuable, but it also means your data is being used in ways you should understand and consent to.
What happens when the model is wrong? AI models hallucinate. They produce confident-sounding answers that are completely false. Ask the vendor how their product handles this. What guardrails are in place? Can users flag bad outputs? Is there a human review loop?
Is this actually AI, or is it search? Some products sold as “AI” are really just keyword search with a chatbot wrapper. That’s not necessarily bad, but you should know what you’re buying.
A vendor who can’t answer these questions clearly is a vendor you should be cautious about.
Understand the Data Access Model
AI tools that plug into your existing systems need access to your data. That’s the whole point. But the scope of that access varies enormously, and it’s one of the highest-risk areas in any AI deployment.
Before you buy, map out exactly what data the tool will touch. Ask the vendor for a data flow diagram. If they don’t have one, that’s a red flag.
Specific things to clarify:
- Does the tool need admin-level access, or can it work with scoped permissions?
- What data does it index at setup, and does it keep indexing over time?
- Can you limit which users, groups, or data sources the tool can access?
- What happens to the data when you cancel the contract?
The permission model matters too. If a tool surfaces information based on what each user can access, you need to make sure your permissions are actually clean before you turn it on. AI tools are very good at surfacing things that were technically accessible but practically hidden. That can be a problem if your SharePoint or file share permissions haven’t been audited in years.
Don’t skip the data access conversation. It’s the one that will keep you up at night if you get it wrong.
Run a Real Proof of Concept
Vendor-run demos are marketing. A proof of concept you control is information.
If an AI vendor won’t let you run a real POC in your environment before you commit to a contract, that tells you something. Most reputable vendors will give you a trial period or a limited deployment to prove out the use case.
When you run your POC, test it against real problems you already know the answer to. If you’re evaluating an AI tool for IT support tickets, feed it 50 actual tickets from last month and see what it does. If you’re evaluating a security tool, give it some alert data from a real incident you’ve already investigated.
Measure what actually matters to you. Not the vendor’s benchmark. Not their demo data. Yours.
Document what you find. You’ll need that data when you go back to negotiate or when leadership asks why you didn’t buy it.
Check the Contract Before You’re Too Far In
AI vendor contracts have gotten more complex. There are clauses you need to read carefully.
Data use rights. Does the vendor have the right to use your data to train or improve their model? This is common. Sometimes it’s opt-out. Sometimes it’s buried in the terms. Find it.
Price lock. AI pricing is changing fast. What does the renewal look like? Is there a cap on price increases?
Exit terms. Can you get your data out when you leave? How? In what format? Is there a fee?
Uptime and SLAs. What happens if the service is down? What’s the remediation? Many AI tools are cloud-dependent, and a vendor SLA of 99.5% still means roughly 44 hours of potential downtime per year.
Integration lock-in. Some vendors make it easy to connect their tool to your stack and very hard to remove it later. Understand what a migration would look like before you start.
You don’t need to be a lawyer. But you do need someone to read the contract with your interests in mind before you sign.
Evaluate the Vendor, Not Just the Product
Products change. Vendors stick around. Before you sign a multi-year deal with an AI company, spend some time evaluating the company itself.
Questions worth asking:
- How long have they been in business?
- Who are their enterprise customers? Can you talk to any of them?
- What does their support model look like? Is there a dedicated CSM or are you calling a general helpline?
- What’s their roadmap, and how much of it is real versus aspirational?
- What happens to your contract if they get acquired?
That last one matters more than it used to. The AI market is consolidating fast. A product you buy from an independent vendor today may be folded into a larger platform in 18 months. Sometimes that’s fine. Sometimes it means your tool gets deprecated and you’re starting over.
Ask about acquisition scenarios directly. Some vendors will give you contract protections around change-of-control situations.
Watch Out for AI Washing
AI washing is when a company slaps “AI-powered” on a product that hasn’t meaningfully changed. It’s widespread right now because every vendor knows AI sells.
Signals that a product might be AI-washed:
- The “AI” feature is an add-on that wasn’t part of the core product 12 months ago
- The vendor can’t explain what model powers it or where the outputs come from
- The use of “AI” in marketing is heavy but the product description is vague
- The demo shows one very specific, very polished scenario with no room for questions
None of these are definitive. But they’re worth noticing.
The best AI products are the ones where the AI is integrated into the workflow in a way that makes the work faster or better. Not products where AI is a layer on top of something that already existed, added mostly for marketing purposes.
A Simple Scorecard for Vendor Evaluation
When you’re comparing multiple vendors, it helps to score them on the same criteria. Here’s a simple starting point:
| Criteria | Weight | Notes |
|---|---|---|
| Solves our specific problem | High | Must pass this test first |
| Clear data access model | High | Non-negotiable for compliance |
| Successful POC results | High | Test with your own data |
| Reasonable contract terms | Medium | Data rights, exit, SLAs |
| Company stability | Medium | Funding, customers, support |
| Integration with existing stack | Medium | Reduces friction and cost |
| Pricing and renewal terms | Medium | Watch for escalation clauses |
Use this to structure your conversations. Use it to compare vendors side by side. Use it to make the case to leadership when you’re recommending a decision.
Bottom Line
AI vendor evaluation is not that different from any other technology evaluation. The fundamentals still apply. Know what problem you’re solving. Test it for real. Read the contract. Check the company.
What’s different about AI is the pace of hype and the speed at which vendors will tell you that you’re falling behind if you don’t buy right now. That pressure is a sales tactic. Don’t let it rush you into a decision you haven’t thought through.
The IT leaders who will get the most value from AI in the next three years are the ones who buy intentionally, not urgently.
Catch Advisors works with IT leaders at mid-market companies to cut through vendor noise and make better technology decisions. If you’re evaluating AI tools and want a second set of eyes, visit catchadvisors.com to start the conversation.