Leadership · 4 minute read
How Executives Should Evaluate an AI Demo
An AI demo shows a system on inputs someone chose, so it proves possibility, not reliability. Executives should treat it as a starting point: ask which inputs were selected and why, what happens on awkward cases, what the system may do alone, and what it costs per task; then request an evaluation on the company's own cases.
Every AI demo works, because the person giving it chose the inputs. This is not deception; it is what a demo is. The executive error is treating a demo as evidence. This guide gives leaders a method for the room: the questions that reveal what the demo hides, the request that turns persuasion into evidence, and the tell-tale signs of a general model dressed as a product.
What does a demo actually prove?
Possibility. A demo shows that, on inputs chosen to succeed, the system can produce an impressive result. It does not show how often it succeeds on real inputs, what it does on the awkward ones, what happens when it is wrong, how it connects to your systems, what it may do without a person, or what it costs at volume. Those are the things that determine whether it works in production, and none of them fit in a demo. FISTA's AI evaluation explained for executives piece explains the alternative.
What questions expose what the demo hides?
| Question | What it reveals |
|---|---|
| Which inputs did you choose, and why these? | Whether the demo is representative or curated |
| What happens on an ambiguous, malformed, or adversarial input? | Failure behavior and whether it was designed |
| What did it get wrong in your own testing? | Whether the vendor evaluates at all, and honestly |
| What actions can it take in our systems, and which need approval? | Authority and control design |
| What is the pass rate on an evaluation set, and how was the set built? | Whether reliability is measured |
| What does it cost per task at our expected volume? | Economics beyond the license |
| What is the underlying model, and what happens when it changes? | Dependency and deprecation risk |
| What has to be built to make this work with our data and systems? | The real project behind the demo |
A vendor with a real product answers these specifically. A vendor with a demo changes the subject.
What should you request instead?
An evaluation on your own cases. Assemble a set of real inputs from the process in question, with known correct outcomes, including hard and adversarial cases. Have the vendor or your team run the system on the set and report the pass rate, the failures, the latency, and the cost per task. Set a decision date. This converts a persuasion exercise into evidence, and it is the same method used to gate any agent's release. The how to run an AI vendor bake-off guidance on vendor evaluation describes running several vendors against the same set.
A vendor who declines to be evaluated on your cases has told you what you need to know.
How do you spot a general model dressed as a product?
Many demos are a general model behind a polished interface. The product value, if any, is in what surrounds the model: integrations with your systems, permission and approval enforcement, evaluation and monitoring built in, and handling of model changes. Ask what the product does that the model alone does not. Thin wrappers have weak answers; real products have specific ones. The build vs buy AI guide helps decide whether the surrounding work is worth buying or building.
What about internal demos?
The same rules apply. Internal teams choose favorable inputs too, usually without meaning to. The response is identical: request the evaluation set, the pass rate, and the failure analysis, and ask what the agent may do alone. The questions executives should ask about AI agents guide gives the full list.
How should executives use demos?
For understanding what a category can do and for meeting the team, with the questions above ready. Not for decisions. Delegate evaluation to a team with your cases, and review scored results rather than performances. Executives who are known to ask for the evaluation set find that demos improve, because vendors and teams bring the evidence with them.
What should executives ask themselves after a demo?
- What did I see that I could not have predicted from the vendor's own description?
- What did the system do on an input nobody chose?
- What would it be allowed to do in our systems?
- What is the cost per task at our volume?
- What is the decision date, and what evidence will we have by then?
How can FISTA Solutions help?
FISTA Solutions builds evaluation sets from clients' own cases and runs vendor systems and internal prototypes against them, through its Applied division, so that decisions rest on pass rates rather than demos. Its AI agents practice then builds the integrations, controls, and monitoring that demos leave out. Since 2017, FISTA has delivered 150+ projects for 50+ companies across 12+ countries.
To convert a demo you have just seen into a scored evaluation, talk to FISTA on WhatsApp, or read how to evaluate AI vendors for the wider process.
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01Why are AI demos misleading?
Because the inputs are chosen to succeed and the failure modes are not shown. AI behavior is probabilistic, so a demo on ten selected inputs says little about ten thousand real ones. Demos also hide the work that determines outcomes: integration, permissions, evaluation, and operations. They prove possibility, not reliability.
02What questions should executives ask during an AI demo?
Which inputs were chosen and why? What happens on an ambiguous or malformed input? What did it get wrong in your testing? What actions can it take in our systems, and what needs approval? What does it cost per task at our volume? What is the pass rate on an evaluation set, and can we run our own?
03What should executives request instead of a demo?
An evaluation on the company's own cases: a set of real inputs with known correct outcomes, run by the vendor or the internal team, with the pass rate, the failures, the latency, and the cost per task reported. Add a few adversarial cases. Set a decision date. This turns persuasion into evidence.
04How do you tell a real AI product from a demo of a general model?
Ask what the product does that the underlying model does not: which integrations exist, how permissions and approvals are enforced, what evaluation and monitoring are built in, and what happens when the model provider changes. A thin wrapper has weak answers; a real product has specific ones with evidence.
05Should executives attend AI demos at all?
Sparingly, and with the questions ready. Demos are useful for understanding what a category of system can do and for meeting the team behind it. They are not useful as evidence for a decision. Delegate the evaluation to a team with the company's cases, and review the scored results rather than the performance.
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