Independent AI test and evaluation

What an independent AI evaluation should give you

Vendors test their own systems. That answers their question. Here is what an outside evaluation should answer for you.

By Scott Mastin · 12 November 2026

Every AI vendor tests its own system. That testing is real and useful, but it answers the vendor's question: does the model do what we built it to do? The buyer's question is different: does it do what we need, with our data, in our workflow, well enough that people will rely on it?

An independent evaluation exists to answer the buyer's question. Independent means the people judging the system don't build it, don't sell it, and don't get paid more if it passes.

What you should get back

1. A plain conclusion. Ready, ready with conditions, or not ready, in a sentence a decision-maker can act on.
2. The reasoning. What was asked for, what was delivered, and where the two differ.
3. Two independent looks. A conclusion that rests on a single test is a guess. Every finding should be checked at least two different ways before it reaches a leader.
4. Where it breaks. The conditions under which the system stops being trustworthy, so users know where the edge is.
5. Whether the problem is the model, the data or the workflow. Most failures aren't the model. They're the wrong data going in, or AI bolted onto a job without changing how the job is done.

What you should not accept

• A pile of scores with no conclusion.
• A requirements table with every row marked green, for a system nobody uses.
• A report that repeats the vendor's benchmarks back to you.

When it's worth doing

Before you buy or renew. When the vendor's numbers look better than what your users see. Before a fielding or authority-to-operate decision. And when your own team built the system and graded its own work.