Putting AI on Your Product Roadmap

"Add AI" is not a roadmap item - it's a category, and most of the AI features that stall in development stall because nobody scoped them down to something a team can actually ship and evaluate in a single cycle. The teams that ship AI features quickly aren't smarter about the models; they're more disciplined about scope.

Start from the workflow, not the model

The fastest-shipping AI features we've scoped all start with a specific, painful, repetitive workflow step - not a capability ("we should have a chatbot"). Find the five-minute task a user does fifty times a week, and ask whether a model can compress it to thirty seconds. That framing produces a feature with a measurable before/after, which is what actually gets it prioritized.

Ship the eval before the feature

Before writing a single prompt, define what "good enough" output looks like and build a small set of test cases against it - real examples pulled from your own data, not synthetic ones. This does two things: it gives engineering a concrete target instead of a vibe, and it gives you an honest answer about whether the model is actually ready for this use case before you've invested weeks in it.

Design for the wrong answer

Every AI feature will occasionally be wrong, and the features that survive contact with real users are the ones designed around that fact from day one - a visible confidence signal, an easy edit or undo path, a human fallback. Bolting on error handling after launch is where "quarters, not weeks" timelines usually come from.

Timebox the first version hard

Two to three weeks for a v1, end to end, including the eval set. If it's taking longer, the scope is wrong, not the timeline - cut the feature down until it fits, ship it to a small cohort, and let real usage tell you what v2 actually needs instead of guessing upfront.