A Successful AI Demo: What Evidence Should a Buyer Ask For?

A successful AI demo is a reason to investigate, not enough evidence to buy. Ask the supplier to run tasks you choose, show any human intervention and deliver the result in the form your team would actually use. Agree on acceptance before the trial starts, while neither side knows which examples will look best.

The buying question is specific: can this agent complete the work you intend to delegate under conditions you can support? A polished presentation can answer a different question—whether a carefully prepared example is possible.

Use the following packet to make a pilot reviewable. These are proposed evidence requirements; they are not universal purchasing standards or measured predictors of commercial success.

Anthropic’s evaluation guide distinguishes a task from a trial and recommends multiple trials because outputs vary. It also separates a narrated success from the final state of the environment. Those distinctions make a useful foundation for a buyer-controlled pilot.

Choose cases that represent the work you expect to assign. Keep a record of how they were selected. A pilot made entirely of easy, clean inputs cannot answer how the system handles incomplete attachments or conflicting instructions.

Decide which failures block adoption and which are acceptable with review. A wrong recipient may be disqualifying even when most routine cases pass. Report the counts and conditions rather than turning a small, selected sample into a general reliability percentage.

Record setup, retries, manual corrections and final review separately. If an engineer quietly repairs an export, the resulting file may be excellent, but the operating model includes that engineer’s work. The buyer should know whether the proposed service includes such support.

Ask what happens when the agent encounters a new exception. Is there a named escalation owner? Is the partial result preserved? Can the buyer understand the state without replaying a long chat? The tool-error decision table can help define these cases.

Keep cost analysis grounded in the pilot’s observed inputs. Do not call the saved time or return on investment measured unless you collected a comparable baseline and accounted for review. This article supplies no invented savings estimate.

Anthropic’s harness report describes cases where code changes and limited checks did not establish working end-to-end behavior. It is a vendor engineering report, not evidence about the supplier you are evaluating.

For a content agent, open the exported artifact. For a coding agent, run the agreed user action. For a research agent, inspect consequential citations. Choose the acceptance check for the deliverable rather than asking for the same dashboard screenshot in every pilot.

The file acceptance reference makes this concrete for documents. A downloadable deck that cannot be edited may be a successful export and an unsuccessful delivery.

Write the decision in one of three forms: ready for the agreed scope, continue a limited pilot to resolve named gaps, or not suitable for this task. List the evidence supporting that decision and the conditions that would invalidate it, such as removing required human review.

A narrow successful pilot can justify a narrow purchase. It does not establish fitness for every department or a higher-risk use. Expand only when the added work has its own acceptance evidence.

For selecting trial cases, the guide to testing on your own work provides a related framework. Keep contractual and procurement review separate from the empirical claim that the agent can complete the job.