I12 · Delivery teams · 5 min read
Product decisions belong in the delivery system
An AI delivery pod needs more than a backlog. It needs a visible account of the decision being made, the assumption being tested, and the evidence that would change the plan.
By LockedIn Labs Editorial · Published 2026-09-06 · Reviewed 2026-09-06
This new AISDLC adaptation develops the role model in LockedIn Labs’ AI delivery pods: how to staff, train, and evaluate a cross-functional team. Its contribution is a practical decision record for the work between discovering a problem and accepting a change.
AI can make a team unusually productive at implementing the wrong interpretation. A prompt becomes a ticket, the ticket becomes code, and the code passes tests written against the ticket. The chain looks coherent even when nobody has decided whether the proposed behavior solves the user’s problem. Product judgment has to enter the delivery system before implementation makes the initial assumption expensive to reconsider.
Name the decision before naming the solution
Consider an illustrative internal purchasing workflow. Requesters repeatedly submit incomplete forms, and reviewers spend time asking for missing information. The initial proposal is an agent that completes and submits requests. A product decision comes earlier: which missing details can be suggested reliably, which require the requester’s knowledge, and whether automatic submission is useful enough to justify additional authority.
A smaller first experiment might draft a completeness checklist without changing any purchasing record. The product manager owns the choice of problem and the evidence needed to continue. The engineer decides how to test the behavior. The forward-deployed engineer investigates the actual approval workflow. Delivery management keeps dependencies and decision deadlines visible. One person may hold several roles; that should not make the decisions disappear.
Carry the assumption through the build
Product decision record
Create one record for a consequential product choice. Keep these fields next to the experiment and link the implementation work back to it.
- Decision and owner: determine whether a draft-only completeness assistant merits a limited trial. Name the person who can accept, defer, or stop that trial and the date the decision is needed.
- User and boundary: purchasing requesters preparing one request type. The assistant can identify absent fields and suggest questions; it cannot infer an unknown amount, submit a request, or approve spending.
- Hypothesis and alternative: guidance may reduce reviewer clarification without increasing incorrect submissions. Record the simpler alternative, such as clearer form instructions, so the AI option has something meaningful to beat.
- Baseline and observation: define an eligible request, how clarification is counted, and the observation window. Keep the sampling method and exclusions visible; do not compare a selected pilot cohort with an unrelated historical average.
- Acceptance and guardrails: agree how much evidence is needed and what errors prevent expansion. An invented purchasing detail is a different failure from a suggestion the requester finds unhelpful; inspect both separately.
- Decision and follow-through: link the evidence reviewed, unresolved assumptions, selected disposition, and next owner. State what new observation would reopen the choice, including a change to permissions or supported request types.
This record belongs in version control or another system with a visible revision history. Link it to the relevant evaluation cases and candidate change. If discovery shows that most clarification concerns policy interpretation rather than missing fields, revise the hypothesis explicitly. Quietly changing the objective halfway through a pilot makes an apparently successful result difficult to interpret.
Separate direction from coordination
A dependency tracker can show whether access, data preparation, and review are on schedule. It cannot establish that the proposed behavior is worth building. Equally, a compelling product hypothesis does not resolve an unavailable reviewer or an integration dependency. Keep the product decision record and delivery plan linked, with different purposes and explicit owners. Neither needs to become a large document.
At a pod review, inspect one decision that changed because of evidence. Ask what the team learned, which implementation assumption moved, and what work it chose to stop. That discussion makes the product role visible in an engineering team without reducing it to ticket preparation. It also gives a deployed engineer a concrete way to bring field observations back into prioritization.
Connect the accepted behavior to the intent requirements in AISDLC-CQ 12.3 and carry its evidence references into the evidence kit. These references organize the engineering work; the product owner still has to judge whether its result is useful.
Primary sources
AISDLC Insights publishes source-informed editorial synthesis and implementation positions. It is reference material, not a standard, certification, legal opinion, or authorization to deploy an agent.