Let's be honest — AI sales pitches have gotten completely out of hand. Every vendor promises transformative results, jaw-dropping accuracy, and seamless integration with your existing stack. The demos are polished. The case studies sound incredible. And somewhere between the third slide deck and the second follow-up email, it gets genuinely hard to tell which tools are real and which ones are mostly vibes dressed up in a nice UI.
We've talked to a lot of people in our community who've been burned. They signed contracts, onboarded teams, and then six months later realized the product didn't actually solve their problem. So let's talk about how to actually evaluate AI vendors — not the sanitized checklist version, but the real stuff that matters.
Start With the Problem, Not the Product
This sounds obvious but almost nobody does it. Before you take a single vendor call, write down — in plain language — what problem you're trying to solve. Not "we want to use AI" but something specific: "we want to reduce the time our support team spends categorizing tickets" or "we need to flag anomalous transactions faster than our current rule-based system allows."
When you have that written down, every vendor conversation becomes easier to evaluate. You're not asking "what can your product do?" You're asking "can your product do this specific thing?" Those are very different conversations, and vendors who can't answer the second question clearly are waving a red flag.
Demand a Real Demo, Not a Scripted One
Vendor demos are theater. They're rehearsed, the data is cherry-picked, and the scenarios are designed to make the product look flawless. That's not a knock on vendors — it's just how sales works.
So push back. Ask them to run the demo on your data, or at least data that resembles your use case. If they can't or won't do that, ask why. A vendor confident in their product should be fine showing you something messy and real. If they insist on sticking to their canned demo, that tells you something.
Also ask them to show you a failure case. Seriously. Ask them: "what does the model get wrong? Where does it break down?" A good vendor will have a thoughtful answer. A bad one will dodge, pivot, or tell you the model is "continuously improving" — which is a non-answer.
Dig Into the Benchmarks
AI vendors love benchmarks. "95% accuracy!" "10x faster than the competition!" These numbers are almost always true in some narrow, carefully defined sense that may have nothing to do with your situation.
Ask these questions:
- Accuracy on what dataset? Who curated it?
- Compared to what baseline? (Beating a five-year-old model isn't impressive.)
- Measured how? Precision, recall, F1? These aren't interchangeable.
- Does performance hold up when the input data is noisy or incomplete?
If the vendor can't walk you through their evaluation methodology in plain terms, be skeptical. Real technical teams know how their models were tested. If you're only getting marketing answers to technical questions, you're probably not talking to the right person — or the right company.
Talk to Actual Customers (Not Just References)
Every vendor will give you a reference list. Those references are handpicked. The customers on that list are the happiest ones, probably given some kind of incentive to take calls, and primed to give positive feedback.
Do your own digging. Find people on LinkedIn who work at companies the vendor lists as customers. Reach out cold. Ask them what the implementation was actually like, what surprised them, what they wish they'd known. You'll get dramatically more honest answers from someone who wasn't coached.
Also ask the vendor directly: "can you give me a reference from a customer who had a rocky implementation?" If they say they don't have any, that's either a lie or a sign they're too new to have real-world complexity. Either way, useful information.
Watch for These Red Flags
After talking to a lot of people who've navigated this process, a few patterns show up consistently when a vendor is overselling:
"Our AI is explainable" — Ask them to explain a specific decision the model made on your data. Vague answers here are common and concerning.
"We use the latest LLMs" — Cool, so does everyone. What specifically have they built on top of that, and what's proprietary vs. a thin wrapper?
Pricing that's tied to "value" — Outcome-based pricing sounds fair but can get murky fast. Make sure you understand exactly how value is measured and who controls that measurement.
Resistance to a pilot — Any vendor worth working with should be willing to do a time-boxed pilot with clear success criteria. If they're pushing hard for a full annual contract before you've seen real results, that's a problem.
Set Up a Pilot With Real Success Criteria
If a vendor passes your initial screening, push for a pilot. And before the pilot starts, agree in writing on what success looks like. Not vague stuff like "we'll evaluate performance" — actual numbers. "Accuracy above 85% on our validation set" or "processing time under two seconds for 95% of requests."
This protects you and it also tells you a lot about the vendor. If they push back on specific success criteria, they're either not confident in their product or they're planning to reframe what "success" means later. Neither is good.
The Bottom Line
AI is genuinely powerful and there are real vendors building real things that can help your organization. But the hype cycle is intense right now and the pressure to "do something with AI" is real, which creates the perfect conditions for bad purchasing decisions.
Slow down. Ask hard questions. Get specific. And trust your instincts — if a vendor seems more interested in closing a deal than solving your problem, they probably are.
