How to Evaluate AI Vendors Without Getting Burned

Somebody pitched you an AI solution last week. Maybe it was a software vendor at a conference, a LinkedIn message from a "digital transformation consultant," or your nephew who watched a few YouTube tutorials and now offers AI services.

They all sound convincing. They all have case studies. And they all promise results that sound almost too good to be true.

Some of them are legitimate. Some of them will waste your money. The hard part is telling the difference before you've already signed a contract.

The Warning Signs

Before we get into what to look for, here are the red flags that should make you pause:

• They can't explain what they'll build in plain English - if you leave a meeting confused about what you're actually getting, that's a problem
• Everything is "proprietary" or "custom AI" - this often means they're wrapping a basic tool in marketing language
• They promise specific ROI numbers before understanding your business - "We'll save you 40 hours a month" sounds great until you realize they said that to everyone
• No mention of ongoing maintenance or support - AI systems need tuning. If the proposal is "build and walk away," you'll be stuck when things break
• They push urgency - "AI is moving fast, you need to act now" is a sales tactic, not a business case

Questions Worth Asking

When you're evaluating someone who wants to build or sell you an AI solution, these questions will separate the serious vendors from the noise.

About the solution

• What specific problem does this solve? You want a concrete answer, not "it makes your business smarter."
• What does it need from us to work? Good vendors are upfront about data requirements, staff involvement, and setup time.
• What does the first month look like? This reveals whether they have an actual implementation plan or just a pitch deck.
• What are the limitations? Any vendor who says "none" is lying. Every tool has boundaries.

About their experience

• Have you done this for a business like mine? Size matters. A solution built for a 500-person company might be overkill (and overpriced) for a team of 12.
• Can I talk to a current client? References should be easy to provide if the work is solid.
• How long have your implementations been running? Building something is one thing. Keeping it running for a year is another.

About what happens after

• Who maintains this? If the answer is "you," make sure you have the capability.
• What does support look like? Response times, availability, cost.
• What if we want to change it later? You need to know whether you own the solution or you're locked into their platform.
• What's the exit plan? If the relationship doesn't work out, can you take your data and move on?

The Proof of Concept Test

Smart vendors offer a proof of concept (POC) before you commit to a full build. This is a small, contained test that demonstrates whether their approach works for your specific situation.

A good POC should:

• Focus on one process or workflow
• Take days or weeks, not months
• Use your actual data (or a realistic sample)
• Have clear success criteria defined upfront
• Cost a fraction of the full project

If a vendor wants to jump straight to a six-month contract without proving anything first, that's a signal. They might be confident, or they might be hoping you won't notice when results fall short.

The Price Question

AI services range from surprisingly affordable to shockingly expensive, and price alone doesn't tell you much about quality.

What to watch for:

• Extremely cheap - might mean they're reselling a generic tool with minimal customization. Could work for simple needs, but don't expect tailored solutions.
• Extremely expensive - might mean enterprise-grade overkill for a small business. Make sure you're paying for value, not overhead.
• No clear pricing until after meetings - this isn't always a red flag (custom work is hard to price upfront), but you should be able to get a ballpark range early.

The best indicator of fair pricing is scope clarity. If both you and the vendor agree on exactly what's being delivered, the price should make sense relative to the value it creates.

What Good Vendors Do Differently

After seeing enough of both sides, patterns emerge. Vendors who deliver real value tend to:

• Ask more questions than they answer in early conversations. They want to understand your business before proposing anything.
• Say no to bad fits. A good vendor will tell you if their solution isn't right for your situation. They'd rather lose a sale than deal with a disappointed client.
• Show you boring case studies. Not "we 10x'd revenue overnight" but "we reduced invoice processing from 8 hours to 2 hours over three months." Boring results are usually real results.
• Talk about tradeoffs. Every solution has them. Vendors who acknowledge downsides are being honest with you.
• Start small. They're willing to prove value on a limited scope before expanding.

A Simple Scoring Framework

When comparing vendors, rate each one on these five dimensions (1-5 scale):

1. Clarity - Can you explain what they're offering to someone else in one sentence?
2. Evidence - Do they have proof this works for businesses like yours?
3. Honesty - Did they acknowledge limitations and tradeoffs?
4. Support - Is there a clear plan for what happens after delivery?
5. Risk - How much are you committing before you see results?

Any vendor scoring below 3 on more than one dimension deserves extra scrutiny.

Your Next Step

Finding the right AI partner doesn't have to feel like a gamble. Here's how to move forward with confidence:

1. Define the problem first - before you talk to any vendor, write down the specific process you want to improve and what success looks like.
2. Use the questions above - bring them to your next vendor conversation. The answers will tell you a lot.
3. Get a second opinion - CoreAgentic offers free assessments where we look at your workflows and give you an honest picture of what's worth automating and what isn't. We'll also help you evaluate proposals from other vendors if you've already received them. No pitch, just perspective.

The best investment you can make before spending money on AI is spending time understanding what you actually need. Everything else follows from that.