An AI product planning session
- 1
Product
The user task, baseline and acceptable outcome
- 2
Engineering
System boundaries, implementation and verification
- 3
Data owner
Source access, quality and permitted use
- 4
Operations
Monitoring, escalation and recovery
Plan for behaviour changes
A prompt or model update can alter user-visible behaviour without a conventional code change. Put those releases through an explicit evaluation and approval process.
Include the cost of review
Measure the work needed to check, correct and support the output. A fast model response is not a useful efficiency gain if it creates more downstream work.
Make responsibilities visible
AI delivery spans more than model integration. Someone must understand the user's task, own access to source data and decide whether a result is acceptable. Assign those responsibilities explicitly. A model engineer cannot independently approve the business meaning of every answer, and a business reviewer cannot repair an unreliable retrieval pipeline without engineering support.
Product ownership should maintain priorities and success measures. Design should make uncertainty and review tasks understandable. Engineering should enforce permissions and predictable application behaviour. Operations should be able to investigate failures and control releases. One person can hold several roles in a small team, but each responsibility still needs an owner and a practical route for resolving disagreements.
Design the human review work
When approval is part of the control model, design that experience before increasing automation. Reviewers need the source evidence, proposed action and a clear explanation of what approval will do. They also need a route to reject, correct or escalate. A generic confirmation button is insufficient when the underlying action changes customer or financial records.
Measure review effort. Frequent correction may simply move work to another team. Use realistic tasks to find out whether reviewers have enough context and whether their decisions are recorded usefully. Feed recurring corrections into evaluation cases and the delivery backlog. Give reviewers an effective way to report a problem without having to understand the model's internal implementation.
Keep ownership after launch
Agree who maintains source collections, approves model changes and responds to unexpected behaviour. A provider or prompt update can change results even when the surrounding application code remains the same. Treat these changes as releases with proportionate evaluation and an identified rollback path.
Review usage, failure patterns, correction effort and operating cost together. Distinguish an adoption problem from a quality problem. Low usage may reflect a poor fit with everyday work rather than a need for another model. Keep the team focused on a task people can complete reliably. The operating model should support continued improvement and clear accountability beyond the first demonstration or pilot.
An agreed accountable owner using evidence from product, technical and operating checks. Critical constraints should have an explicit owner rather than a shared assumption.