Your First AI Feature Should Be Boring
The AI features that survive contact with real users are rarely the flashy ones. Here is why I start with the dull jobs.
Every few weeks a founder shows me a demo where an AI agent books flights, writes the email, negotiates the refund and files the expense report. It looks magical for ninety seconds. Then I ask what happens when the airline website changes its layout, and the room goes quiet.
I build AI products for a living, and my honest advice to any team shipping its first model-powered feature is simple: pick something boring.
What boring actually means
A boring AI feature has three properties. It does one narrow job. A wrong answer is cheap. And a human already does the same job today, so you know what good looks like.
The best example from my own work was a support tool that did nothing more than suggest a category for incoming tickets. Billing, login, bug, feature request, other. Agents could accept the suggestion with one click or pick a different one. That was the whole feature.
It was not exciting. Nobody tweeted about it. But within a month it was correct about 91% of the time, agents saved roughly forty seconds per ticket, and on a team handling 3,000 tickets a week that added up to more than thirty hours of human time saved every week.
Why boring wins
- You can measure it. Ticket categories have a right answer. You can compare the model with what agents chose and get a real accuracy number instead of a feeling.
- Failure is survivable. If the model picks the wrong category, an agent fixes it in one click. Nobody loses money, nobody gets an angry email.
- It builds trust. Users who see a tool be quietly right a thousand times will try the next, more ambitious feature. Users burned by a flashy failure will not.
- It teaches your team the plumbing. Logging prompts, versioning them, monitoring cost and latency, handling timeouts. You need all of this for the ambitious stuff too, and it is far easier to learn on a low-stakes feature.
The hype tax
Ambitious AI features carry what I call a hype tax. Leadership expects the demo to be the product. The team spends months chasing the last 10% of reliability, which is usually the hardest 90% of the work. Meanwhile nobody has shipped anything.
A model that is right 95% of the time is a great assistant and a terrible autopilot.
That sentence has saved me from more bad roadmaps than any framework. If a human stays in the loop, 95% is wonderful. If nobody checks the output, 5% failure on thousands of actions per day is a disaster with a timestamp.
A practical starting list
If you are hunting for a first feature, look for these patterns inside your own product:
- Classifying incoming text into a fixed set of buckets
- Drafting a first version that a human edits before sending
- Extracting fields from messy documents into a form a person reviews
- Summarising long threads so someone can catch up faster
None of these will make a keynote. All of them will make someone's Tuesday a bit easier, and that is how real AI products get built: one boring, reliable win at a time.
Cover: Photo by Christopher Gower on Unsplash.
