AI-assisted product and project deliverables

Name the decision before choosing the artifact

Start with what someone must decide: whether to investigate a problem, test a concept, commit capacity, accept completed work, change scope, or stop. Then choose the smallest artifact that carries the evidence for that decision. A roadmap should not quietly become a delivery promise. A research summary should not become a requirement without an owner accepting the interpretation.

The GOV.UK Service Manual advises teams to understand the full user context, use research and available data, and test assumptions early rather than begin with a preferred solution (Understand users and their needs). That supports keeping observed needs separate from proposed features and AI-generated interpretations.

Sources: [1]

Assemble the artifact packet

For each deliverable, record the decision, intended reader, source manifest, evidence date, assumptions, AI transformation, dependencies, acceptance evidence, owner, and supersession rule. Link each material statement to a source location. Mark inferred prioritisation and generated options as analysis, not user evidence.

If several artifacts express the same decision, designate one authoritative record. A slide, ticket, and roadmap item may be convenient views, but conflicting versions create false certainty. The packet should say which record governs and when the other views were last synchronized.

Preserve the AI transformation trail

Record what the AI was asked to do and which substantive changes the team accepted. A short note can identify source IDs, operation, exclusions, generated alternatives, rejected assertions, and reviewer. Avoid storing unnecessary personal or confidential material. The goal is to explain the decision path, not retain every token.

Use AI to compare versions, identify missing fields, challenge assumptions, or draft alternatives. Do not let it invent user research, acceptance evidence, estimates, approvals, or delivery status. Unknown values remain unknown until the responsible person supplies evidence.

Worked artifact: a feature decision card

Consider a synthetic request for an automated weekly summary. The card lists three support-ticket excerpts, two user interviews, and a usage report as sources. AI groups the reported problems and proposes four options. The product owner rejects a fully automated external email because the evidence does not establish acceptable error handling. The team chooses a draft-only internal summary prototype.

The card states the hypothesis, target users, excluded data classes, owner, prototype link, five research tasks, and acceptance questions. It also records a dependency on identity permissions and an unresolved accessibility question. No delivery date appears because capacity has not been committed.

Verify with acceptance evidence

The GAO Agile Assessment Guide describes acceptance criteria and a definition of done as expectations against which a user story can be validated and accepted or rejected by the product owner (GAO Agile Assessment Guide). Apply that principle by attaching observable acceptance evidence to the specific artifact. Do not accept a story merely because generated copy looks complete.

Before release, trace user claims to research, recalculate any figures, test links and dependencies, review accessibility and security assumptions with their owners, and compare the output with the stated acceptance criteria. Reopen the decision when a governing source, user need, dependency, risk, or delivery constraint changes.

Sources: [2]

Keep the claims bounded

This method supports clearer product and project decisions. It does not prove that AI improved productivity, that a roadmap will be delivered, that a control operates effectively, or that one process fits every team. Two Prune can provide task-bounded assessment evidence from a controlled work simulation. It does not certify product quality, replace user research, or make employment decisions.