How to Evaluate an AI Vendor Without a Technical Background

You Do Not Need to Be Technical to Make a Good AI Decision

One of the most common anxieties we hear from senior business leaders is this: "I cannot evaluate whether the technology is actually any good. I have to take their word for it."

This is understandable — and it is also a trap. Delegating the entire evaluation to technical staff, or deferring entirely to vendor claims, exposes you to risks that a business-focused evaluation would catch. The good news is that the most important questions in vendor evaluation are not technical. They are about risk, fit, transparency, and track record.

Here is how to run a rigorous evaluation without needing to read the model documentation.

Start With Use Case Fit, Not Technology Claims

Every AI vendor will tell you their technology is state-of-the-art, enterprise-grade, and built to scale. These claims are nearly impossible to verify without deep technical expertise, and they are not the most important thing anyway.

What matters is whether this vendor has solved your specific problem for organizations similar to yours.

Ask:

• "How many clients in our industry are using this for the use case we are describing?"
• "What does the typical implementation look like for a company our size?"
• "What are the most common challenges your clients face, and how do you address them?"

Vague answers — "we work across many industries," "every implementation is unique" — should prompt follow-up. Confident, specific answers indicate real delivery experience.

Demand Reference Calls, Not Case Studies

Case studies are marketing materials. They feature the best outcomes, the most engaged clients, and the most favorable timelines. They are useful for understanding what is possible — not for understanding what is typical.

Ask for introductions to clients who:

• Are in your industry or a close adjacent sector
• Went live with a similar use case 12 or more months ago
• Are a comparable size to your organization

Then have a direct conversation with their operational leaders — not their IT department. Ask: Was the implementation timeline accurate? What did you not know at the start that you wish you had? Would you sign the contract again?

One honest reference call is worth more than ten polished case studies.

Evaluate the Implementation Plan

Many AI purchasing decisions focus entirely on the product. The implementation plan matters just as much — often more — because that is where most projects succeed or fail.

When a vendor presents their implementation plan, look for:

Specificity about your situation. A good plan reflects what they have learned about your environment, your data, and your constraints during the sales process. A generic template is a yellow flag.

Clear ownership of key activities. Who is responsible for data preparation? Integration testing? User training? If the plan assumes your internal team handles these without assessing whether you have the capacity, that is deferred risk.

Realistic timelines with milestones. Aggressive timelines that compress important phases (data audit, user testing, change management) often indicate the vendor is prioritizing closing the deal over realistic delivery.

A defined go-live and success criteria. What does "done" look like? When will you have a functional system, and how will you evaluate it?

Understand the Contract Terms That Matter Most

You do not need a technology background to understand these contract provisions — but you must ensure they are in writing:

Data ownership: You own your data and any outputs generated from it. The vendor may not use your data to train their models without explicit consent.

Model updates and changes: What happens when the vendor updates the underlying model? Do you get advance notice? Can an update break your implementation? Who is responsible if it does?

Performance SLAs: What is the vendor contractually committed to? Uptime, response time, accuracy thresholds? What remedies exist if they miss these?

Exit terms: How do you retrieve your data if you end the relationship? What happens to any customizations built on their platform?

Liability and indemnification: If the AI system produces an incorrect output that causes harm or a compliance violation, who is responsible?

Vendors resistant to reasonable terms in any of these categories are worth scrutinizing carefully.

The Question That Separates Good Vendors From Great Ones

Ask this directly: "What does failure look like with your system, and what do you do when it happens?"

Good vendors have a thoughtful answer. They know how their system fails, they have monitoring in place to detect it, and they have a process for addressing it. They have lived through failures with clients and learned from them.

Vendors who deflect this question — "our system doesn't fail," "we have very high accuracy" — are telling you something important about how they handle reality versus expectation.

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Vendor evaluation is a business judgment call, not a technical one. The fundamentals — fit, track record, realistic implementation, and fair contract terms — are fully accessible without a computer science degree. If you would like a second perspective on a vendor shortlist or help running a structured evaluation process, we are glad to help. We have sat on both sides of these conversations.