AI Product Management

It includes deciding whether AI is appropriate, defining acceptable failures, and planning evaluation and operation. A high model score does not automatically mean that a feature helps its users.

Key takeaways

• Begin with the user problem.
• Separate model and product measurements.
• Plan failure handling and ongoing evaluation.

Start with the task and the current alternative. Identify what users are trying to complete, where they struggle, and what a successful outcome looks like. Compare a model-based approach with simpler software or a clearer process before committing to added complexity. Separate model metrics from product metrics. Prediction accuracy, retrieval recall, or output preference can help diagnose a system. Task completion, user effort, error recovery, and the cost of a useful outcome address whether the product actually improves the workflow. Define the boundaries of acceptable behavior. Include unsupported requests, uncertainty, latency, and the actions requiring review. Plan how users can correct mistakes, cancel work, or reach another route when the model cannot help. Release with a clear evaluation and monitoring plan. Record model and prompt versions, measure outcomes on representative users and tasks, and investigate regressions. Avoid turning a demonstration into a general promise before the product has evidence under real operating conditions.

1. Imagine a support assistant that closes more tickets after a change, but customers reopen many of them.
2. Measure resolved issues and repeat contact alongside closure rate.
3. Investigate whether the change improved answers or merely made it easier to mark unresolved work complete.

The constructed example separates an operational count from the user outcome it is meant to represent.