How to define AI features that deliver real user value

Product managers should resist adding AI merely to signal innovation and instead use AI value framing: validate a genuine user need, assess whether AI offers enough benefit over simpler or existing alternatives, and define reliability requirements before building. Validation should focus on observable user outcomes such as time saved, fewer errors, and continued use rather than enthusiasm alone, while due diligence should account for development and operating costs, scale of impact, data privacy, and the smallest useful implementation. An example involving event marketing materials showed that an existing Microsoft Copilot subscription could solve a small team’s drafting problem faster and more cheaply than a custom AI system. For AI features that proceed, teams should establish human approval points, anticipated failure responses, clear scope limits, and transparent communication of uncertainty, as illustrated by a job-search agent that recommends roles but does not apply on users’ behalf. These requirements can be converted into evaluations that test behavior under incomplete evidence, failed searches, and boundary conditions, helping ensure that a prototype becomes a dependable product.

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