How to Evaluate AI Vendors

How to Evaluate AI Vendors

Every software company is now an "AI company." Your inbox is full of demos, your LinkedIn is full of pitches, and every vendor deck promises to "revolutionize" something. Most of it is noise. Here's how to separate the real from the hype.

Red Flags That Should Kill the Deal

• "Our proprietary AI model..." Unless they're OpenAI, Anthropic, Google, or Meta, they almost certainly don't have their own model. They're wrapping someone else's API in a nice interface and charging you enterprise prices. That's not always bad — but they should be honest about it.
• No clear answer on where your data goes. Ask: "Does our data train your model? Where is it stored? Can we delete it?" If they dodge, deflect, or say "it's in our terms of service," walk away.
• ROI claims with no methodology. "Our customers see 10x productivity gains." How? Measured how? Over what time period? Compared to what? If they can't explain the math, the number is made up.
• They can't explain what the AI actually does. If the sales team can only speak in buzzwords — "machine learning," "neural networks," "intelligent automation" — but can't explain the specific mechanism in plain English, they either don't understand their own product or it doesn't do much.
• No option for a pilot or trial. Any vendor confident in their product will let you test it with real workflows before signing an annual contract. No pilot = no confidence.

Questions to Ask Every Vendor

1. "What model do you use, and what happens when it changes?" Models get updated. Performance can shift. You need to know if they're locked to a specific version or if you'll wake up one day to different behavior.
2. "What does this look like when it fails?" Every AI tool fails sometimes. Good vendors have graceful failure modes, error handling, and human-in-the-loop fallbacks. Bad vendors pretend failure doesn't happen.
3. "Can you show me a customer who looks like us?" Not a Fortune 500 case study when you're a 200-person company. A customer with your industry, your scale, your use case. If they can't produce one, you're the guinea pig.
4. "What does implementation actually require?" Time, people, integrations, data prep, training. Get the full picture. "It's plug and play" is never true for enterprise software.
5. "What happens to our data if we cancel?" Can you export it? Is it deleted? How long do they retain it? This matters more than most people realize.

What "Enterprise-Ready" Actually Means

Vendors love to call themselves enterprise-ready. Here's what that should actually mean:

• SSO and role-based access control. If every user gets the same permissions and there's no SSO integration, it's not enterprise-ready. It's a consumer app with a bigger price tag.
• SOC 2 Type II certification (at minimum). This means their security has been independently audited. SOC 2 Type I means they have policies. Type II means someone verified they actually follow them.
• Data residency options. Can you choose where your data is stored? For regulated industries, this isn't optional.
• Audit logs. Who used the tool, when, and what data was processed. If you can't audit it, you can't govern it.
• An SLA with teeth. Not just "99.9% uptime" in the marketing copy. An actual Service Level Agreement with penalties if they miss it and a clear escalation path.

The Evaluation Checklist

How to Run a Pilot That Actually Tells You Something

1. Define success before you start. "We'll know this works if [specific metric] improves by [amount] over [timeframe]." If you can't define success, you can't evaluate the tool.
2. Use real data and real workflows. Demo data gives you demo results. Test with the messy, complicated reality of your actual work.
3. Include your most skeptical team member. If the tool wins over the skeptic, it's probably good. If it only impresses the person who was already sold, you've learned nothing.
4. Document the failures. What didn't work? What was frustrating? What took more effort than expected? The failures tell you more than the successes.