Evaluating AI Vendors: What Leaders Should Ask First

AI vendors are everywhere. Every pitch promises transformation, acceleration, and competitive advantage. Demos are slick. Case studies are impressive. Roadmaps are ambitious.

And yet, many organisations discover too late that the vendor they chose cannot deliver in their environment, at their scale, or under their constraints.

Evaluating AI vendors is not primarily a technical exercise. It is a risk-management exercise. Leaders who get it wrong inherit fragile systems, hidden costs, and long-term dependency. Leaders who get it right build leverage.

Here is how to evaluate AI vendors properly — and the questions that should be asked before anything is signed.

Start With the Problem, Not the Vendor

The biggest mistake organisations make is evaluating vendors before defining the problem clearly.

If you cannot answer:

What decision are we improving?
What outcome do we expect to change?
How do we know if this worked?

…then every vendor will sound plausible.

Vendors are incentivised to expand scope, not narrow it. Your job is to be ruthless about focus.

Before any demo, write a one-page problem statement covering:

The decision or process being improved
Current pain points and costs
Constraints (data, latency, regulation, integration)
What success looks like in business terms

Vendors that cannot engage meaningfully with this document should not progress.

Ask Where the Model Actually Runs

Many AI failures begin with an unexamined assumption about deployment.

Critical questions include:

Does the model run in our environment or only theirs?
Is inference cloud-only, or can it run on-premise or at the edge?
What are the latency and availability guarantees?
What happens if connectivity is lost?

Some vendors sell “AI platforms” that are effectively black boxes hosted entirely outside your control. That may be acceptable — or it may be a strategic risk.

If deployment constraints are discovered late, costs and compromises multiply.

Demand Clarity on Data Ownership and Usage

Data is the real asset. Vendors know this.

You must be explicit about:

Who owns the data used for training and inference
Whether your data is used to train models for other clients
How long data is retained
How data can be exported if you leave

Vague answers here are a red flag.

If a vendor cannot clearly articulate data boundaries, assume the boundaries favour them, not you.

Separate Model Capability From System Capability

Vendors love talking about models. Leaders should care about systems.

Key questions include:

How is data validated before inference?
How are errors detected and handled?
How is performance monitored over time?
How does the system respond to drift?

A strong model wrapped in a weak system will fail in production.

Ask to see:

Monitoring dashboards
Alerting strategies
Incident response processes
Update and rollback mechanisms

If these do not exist, you are buying a prototype, not a production system.

Ask How Humans Interact With the System

AI that ignores humans creates friction.

Evaluate:

How outputs are presented to users
Whether confidence or uncertainty is shown
How users can challenge or override decisions
How feedback is captured and used

If the vendor treats “human-in-the-loop” as a slide rather than a design principle, adoption will suffer.

AI should support decisions, not dictate them blindly.

Be Wary of Accuracy Without Context

Accuracy numbers without context are meaningless.

You should ask:

Accuracy on what data?
Measured how?
Against what baseline?
In which operational conditions?

More importantly:

What is the cost of false positives?
What is the cost of false negatives?
How does the system behave when unsure?

Vendors who push headline metrics without discussing trade-offs are optimising for sales, not outcomes.

Understand the True Cost Structure

AI vendor pricing is often opaque by design.

Look beyond headline costs and ask about:

Usage-based fees that scale unpredictably
Costs for retraining or fine-tuning
Charges for additional data sources
Support and maintenance fees
Exit costs

A solution that looks affordable at pilot scale may become prohibitively expensive at production scale.

Insist on realistic cost projections under expected usage.

Evaluate Vendor Dependency Risk

Vendor lock-in is not inherently bad. Unexamined lock-in is.

Key questions include:

How portable are the models and data?
Can we run this system without the vendor?
How specialised is the vendor’s proprietary tooling?
What happens if the vendor is acquired or shuts down?

You do not need full independence. You do need leverage.

If your organisation cannot operate or transition away from the system, you have accepted long-term dependency — knowingly or not.

Probe Their Understanding of Your Domain

AI is not domain-agnostic.

Vendors who succeed long-term demonstrate:

Familiarity with your industry’s constraints
Awareness of regulatory realities
Understanding of edge cases and failure modes
Respect for operational complexity

Generic AI pitches often collapse when confronted with domain nuance.

Ask vendors to talk through real scenarios, not idealised workflows. The quality of their questions is often more revealing than their answers.

Ask About What Went Wrong Before

One of the most powerful questions you can ask a vendor is:

“Tell us about a deployment that didn’t go well.”

Vendors who cannot answer this convincingly are either inexperienced or evasive.

Strong vendors can articulate:

What failed
Why it failed
What they changed as a result

This demonstrates maturity and learning — far more valuable than a flawless sales narrative.

Insist on Shared Accountability

AI vendors often frame themselves as technology providers, not outcome partners.

That distinction matters.

Clarify:

What the vendor is accountable for
What your organisation is accountable for
How disputes over performance are resolved
What support looks like post-deployment

If accountability is ambiguous, you will carry the risk when things go wrong.

When to Walk Away

You should walk away if:

The vendor avoids specifics
Data ownership is unclear
Deployment constraints are glossed over
Human factors are ignored
Pricing becomes evasive
Everything sounds too easy

AI is hard. Vendors who pretend otherwise are selling aspiration, not capability.

Choosing an AI vendor is not about finding the smartest model. It is about finding a partner whose system, incentives, and constraints align with yours.

Leaders who ask hard questions early avoid painful lessons later. Those who chase polished demos often discover too late that they bought complexity, not value.

In AI, due diligence is not pessimism.

It is professionalism.