The key points
- Narrow, task-specific features outperform general assistants.
- Each feature should solve a problem users already have.
- Customer-facing output needs a confidence threshold and human fallback.
- Estimate cost per request before launch, not after a traffic spike.
What kinds of features earn their place?
Semantic search helps people find things by meaning rather than exact keyword. Drafting and summarization save work that someone would otherwise write from nothing. Classification and routing gets an item to the right queue. Recommendations built on real usage beat guesswork. Each solves a defined problem users already have.
Which features usually go unused?
The general ones. A chat assistant bolted onto a dashboard demonstrates well and then sits idle, because users did not arrive with a question phrased for it. Test an idea by writing it in one sentence that names the user's task; if you cannot, wait.
What should you plan for before building?
Cost and error handling. Model usage is charged by use and easy to underestimate, so estimate cost per request in scoping and build in caching and rate limits. Anything customer-facing needs a confidence threshold, a human fallback and a visible way to report a bad answer. Where being wrong is expensive, keep the feature internal and use it to assist your team's judgement.
- Semantic search over your own content
- Drafting and summarizing
- Classification and routing
- Recommendations from real usage
Test an AI feature idea
- Which specific user task would this make faster?
- How sensitive is the data the feature needs to read?
- What is an acceptable cost per user per month?
- What happens, visibly, when the model is wrong?
Sources
Rules and guidance this answer relies on. Policies change, so check the current version before acting on it.