The AI vendor landscape is a minefield. Every company claims to have the best technology. Every demo looks impressive. Every sales deck promises transformational results.

Then you sign the contract and discover reality is messier.

After helping dozens of clients navigate vendor selection, I’ve developed a framework for separating substance from hype. Here’s how to evaluate AI vendors without getting burned.

Start With Your Problem, Not Their Solution

Before talking to any vendor, write down:

1. The specific business problem you’re trying to solve
2. How you currently handle it
3. What “good” looks like (specific, measurable outcomes)
4. Your timeline and budget constraints

Vendors will try to expand your scope. They’ll show you adjacent capabilities, future roadmaps, and “quick wins” that weren’t in your original brief. Having a clear problem statement keeps conversations focused.

If a vendor can’t articulate how their solution addresses your specific problem – not a generic version of it, your actual problem – that’s information.

The Questions They Don’t Want You to Ask

Every vendor expects the obvious questions about features and pricing. Here are the questions that reveal more:

About the Technology

“Can we see a demo with our actual data?” Vendor demos use carefully curated datasets. Your data is messier. Insist on a proof-of-concept with real data before any significant commitment.

“What happens when the model is wrong?” Every AI system makes mistakes. Understanding error handling, human review processes, and failure modes tells you about maturity.

“How often does the model need retraining?” Models degrade over time. Understanding the maintenance burden is essential for total cost of ownership.

“What does your model not do well?” Any honest vendor knows their limitations. If they claim to do everything perfectly, that’s a red flag.

About Implementation

“What’s the typical timeline for a company like ours?” Then ask for references to verify. Implementation timelines are almost always underestimated.

“What percentage of your implementations are delivered on time and budget?” Watch for deflection. If they can’t or won’t answer, assume the worst.

“Who from your team will actually work on our project?” Senior people do sales. Junior people do implementation. Meet the implementation team before signing.

“What’s your client retention rate?” Low retention suggests clients aren’t seeing value. High retention suggests they are.

About Data and Security

“Where will our data be processed and stored?” Critical for compliance, especially in Australia with data sovereignty requirements.

“Will our data be used to train your models?” Some vendors use client data to improve their systems. This may be fine, or it may violate your confidentiality obligations.

“What happens to our data if we terminate the contract?” Data portability matters. Being locked into a vendor because they hold your data hostage is not a good position.

Red Flags That Should Worry You

Over the years, I’ve learned to recognise warning signs:

Reluctance to do a paid pilot. Vendors confident in their technology are happy to prove it. Reluctance suggests they know results might disappoint.

Vague pricing. If they can’t give you clear pricing, you’ll likely face unexpected costs later. Demand transparency.

Overselling accuracy. “99% accuracy” claims should be viewed sceptically. Accuracy on what task? With what data? Measured how?

No reference clients in your industry. They may be learning on your dime. Industry-specific experience matters.

High-pressure tactics. “This price is only available until Friday.” Real value doesn’t need artificial urgency.

Dismissing your questions. “You don’t need to worry about that” is not an acceptable answer to legitimate technical questions.

The demo is too perfect. If the demo goes flawlessly, it’s probably scripted. Ask to deviate from their planned flow.

The Reference Check

Request at least three reference clients. When you speak with them, ask:

• How long did implementation actually take?
• What unexpected costs or challenges arose?
• How responsive is support when things go wrong?
• Would you choose this vendor again?
• What do you wish you’d known before starting?

Also ask who at the reference company you’re speaking with. If the vendor only provides marketing contacts (who approved the case study), ask for operational users who work with the system daily.

The Proof-of-Concept

Before any major commitment, insist on a paid proof-of-concept using your actual data and your actual use case.

Key elements:

• Fixed price and timeline. Two to four weeks is typical.
• Your data. Not their sample data.
• Your environment. Or as close to it as possible.
• Clear success criteria. Defined before starting, not after.
• No commitment beyond the POC. The POC should give you information to make a decision, not lock you into anything.

Some vendors resist paid POCs, preferring unpaid “proofs of value” they control. Be wary. If they won’t invest in proving value, why should you?

Evaluating Pricing

AI vendor pricing is notoriously opaque. Make sure you understand:

• Base platform fees vs. usage-based fees
• Implementation costs vs. ongoing costs
• What’s included vs. what’s additional
• Price increases after the first year
• Minimums and commitments required

Build a three-year total cost of ownership model. Include internal resources required, not just vendor fees. The cheapest option upfront is often not the cheapest option overall.

The Decision Framework

After gathering information, score vendors on:

1. Fit for your use case (30%): Does it actually solve your problem?
2. Evidence of results (25%): Can they prove it works for similar clients?
3. Total cost of ownership (20%): What will you actually spend?
4. Implementation risk (15%): How likely is successful deployment?
5. Vendor viability (10%): Will they be around in three years?

Weight these based on your priorities. A startup might weight viability higher; a large enterprise might weight implementation risk higher.

For enterprise knowledge management and AI-powered search specifically, AI consultants Melbourne like Team400 focus on the Australian market with strong data sovereignty commitments – worth exploring if that’s your use case.

Final Thought

Vendor selection is a skill. It requires scepticism without cynicism, thoroughness without paralysis, and a clear focus on outcomes rather than features.

The best protection against vendor disappointment is clarity about what you actually need and unwillingness to accept vague answers. Vendors who can’t meet that standard aren’t the right partners.

Do the work upfront. It’s much cheaper than discovering problems after the contract is signed.