Start with Your Problem, Not Their Product
Before you talk to a single vendor, get clear on what you're actually trying to solve. Not 'we need AI'--that's the trap. Instead: Are you reducing manual work in a specific process? Improving decision-making speed? Catching errors earlier? Lowering customer acquisition costs? Vendors will always try to fit your problem to their solution. Your job is to know your problem so well that you can't be led astray. Write it down. Get stakeholder alignment on it. If you can't articulate the problem in one paragraph, you're not ready to evaluate vendors yet. And here's the uncomfortable truth: Sometimes the answer isn't AI. Sometimes it's process redesign, better hiring, or a simpler tool. A trustworthy vendor will tell you that. A vendor hunting for commission will not.
Dig into Their Data and Methodology
When a vendor presents case studies or benchmarks, ask these specific questions: What data trained this model? If they're vague, that's a red flag. You need to know whether they trained on your industry, your data types, and comparable company sizes. A model trained on healthcare data might perform terribly on your financial data. How do they measure performance? Ask for their evaluation metrics in writing. Accuracy sounds good until you realize they're measuring the wrong thing. Request specifics: precision, recall, F1 scores, whatever applies. Ask about performance on edge cases and underrepresented groups in their training data. What's their validation process? Have they tested this with customers like you? How many? What were the actual results before and after deployment? Be skeptical of theoretical improvements. You want evidence from real-world use. Don't accept hand-waving. If they can't explain how their model works at a level you can understand, they don't understand it well enough to support you.
Run a Controlled Pilot, Not a Full Rollout
This is where the audit-first approach pays off. Never go live company-wide on day one. Set a specific scope: one team, one process, one month. Give the vendor real data--your data, not sanitized test data. Measure exactly what you said you'd measure before you started. Track adoption, actual time savings, error reduction, whatever your metric was. Build in a kill switch. If performance drops below your baseline after two weeks, you need to be able to stop. Write this into the contract. Assign someone on your team to use this tool daily and report back honestly. Not the cheerleader who wanted AI funding in the first place. Someone skeptical. What's actually hard about using this? What didn't work? Those answers matter more than the vendor's success metrics. After 30 days, you'll know whether this is worth expanding. Most aren't. That's fine. You've learned at a manageable cost.
Understand the Hidden Costs and Dependencies
The vendor's price quote doesn't tell you the real cost. Ask about data preparation: How much of your data is clean enough to use out of the box? Most isn't. Budget for data engineering time--often months, not weeks. Integration and migration: How does this connect to your existing systems? Can they provide clear requirements and timelines? Pushy vendors will say 'two weeks.' Honest vendors will say 'depends on your architecture, but typically four to eight weeks.' Ongoing maintenance: Does the model degrade over time? Do they retrain? How often? Who pays for that? Is there a support SLA? What happens if performance drops six months in? Vendor lock-in: Can you extract your data and model if you leave? What's the process? What format? Ask this in writing. Get all of this in a detailed SOW before you sign anything. Surprises here will kill your project's ROI.
Trust Your Gut on Communication
After all the technical evaluation, there's one more signal: how does the vendor communicate with you? Do they listen or just pitch? In your discovery calls, do they ask about your constraints, your team's skill level, your timeline? Or do they launch into features you didn't ask about? Are they honest about limitations? Every tool has them. If a vendor claims theirs has none, they're not being straight with you. Do they acknowledge your uncertainty? Implementing AI is genuinely risky. A vendor who acts like it's simple is either inexperienced or dishonest. A vendor who says 'here's what usually goes wrong and how we help' is someone you can trust. Can you reach support when you need it? Talk to a current customer, ideally one using the tool for something similar to your use case. Ask them what surprised them. What do they wish they'd known?
Evaluating AI vendors is about doing the hard work upfront so you don't do expensive work backward later. Start with your problem. Demand evidence. Run a pilot. Understand the real costs. And work with vendors who are honest about what AI can and cannot do. That's how you avoid getting burned.