Many AI features are added to answer a board meeting rather than a user need. A chat box appears in the corner, a sparkle icon is attached to a text field, and usage quietly drops after the first week. The technology works. The feature does not.
Start with a task people already find tedious
The best AI features remove work users already dislike: summarising long documents, filling repetitive forms, drafting a first version, sorting a pile of messages, finding the one record they need. The user already knows the task and can judge the result.
Features that ask users to learn a new way of working, typing instructions into an open box, need to be much better to be adopted.
Design for being wrong
Language models make mistakes, and they make them confidently. A feature that treats every output as correct will eventually embarrass the product, or worse, harm a customer.
Good AI features are designed around review. The user sees what the AI produced, can see where it came from, and can edit or reject it easily. The more serious the consequence, money, health, legal commitments, the stronger that review step needs to be.
Show the work, not the magic
Presenting AI as magic sets expectations it cannot meet. Showing sources, marking what was generated, and explaining limits builds the kind of trust that survives the first mistake.
The interface matters as much as the model. The same output can feel helpful or unreliable depending on how it is framed, when it appears and how easy it is to undo.
Test AI features like any other feature, and then some
Traditional testing assumes the same input gives the same output. AI features do not. So alongside the usual QA, they need an evaluation set: a collection of realistic inputs with known good answers, run whenever the prompt, model or data changes.
It should include the awkward cases. Ambiguous questions, missing data, attempts to make the feature say something it should not. A score on that set is how a team knows whether a change made things better or only different.
Count the cost of each answer
Every AI call costs money and time. A feature that feels instant in a demo can be slow and expensive at scale. Latency, cost per use and fallback behaviour when the service is unavailable are product decisions, not only engineering ones.
Be willing to cut it
Some AI features do not earn their place, and the evidence usually arrives within weeks of launch. Removing a feature that people do not use is not a failure. Keeping it, and the cost and risk that come with it, often is.
