Almost every product conversation we have now includes a list. Sometimes it is written down, sometimes it comes out over the course of an hour, but it is always there: the ten things AI could do in this product. Summarise the records. Draft the emails. Answer questions from the documents. Predict who is about to churn. Flag the anomalies. Generate the report.
All of these might be worth building eventually. The question that decides whether the product succeeds is which one you build first, and the answer is usually not the one the founder is most excited about.
We have built AI features into our own products and into products for clients, and the first feature we now recommend has a consistent shape. It scores well on four things.
It sits on data you already have and trust
The most common way a first AI feature fails is that the data underneath it is not ready. A churn prediction sounds compelling until you discover that engagement is tracked in three places, half the records have no last-activity date, and the field that would tell you the most has been free text for six years.
The right first feature works on data that is already structured, already populated, and already believed. Invoices. Support tickets. Event registrations. Documents that live in one place. If the first step in the plan is “clean up the data”, the feature is not first. Something else is.
The user can see whether it is right
AI gets things wrong. A first feature should be one where the person using it can immediately tell when that has happened and correct it cheaply.
A drafted email is a good example. The user reads it, adjusts two sentences, sends it. If the draft was bad, they see that in ten seconds and write their own. Nothing has gone out into the world uncorrected.
A prediction that silently changes which members get a renewal call is the opposite. If the model is wrong, nobody notices for a quarter, and by then the damage is in the retention numbers. That feature might be valuable, but it is not a good first one, because you cannot learn from it fast enough.
It replaces something someone does every day
The best first features are boring in the pitch and thrilling in use. They take a task a specific person does repeatedly, often reluctantly, and make it take a fraction of the time.
In one of our own products, the first AI feature we shipped was not the clever one. It was turning a rough note into a structured record so the operator did not have to fill in nine fields by hand. Nobody would put that on a slide. It is used dozens of times a day, and the people who use it would be annoyed if it went away. That is the reaction you want from the first feature, because it earns the trust you need to ship the second.
You can measure it in a week
A first feature should have a number attached to it before it is built, and that number should move within days of shipping. Time to complete a task. Number of records created. Percentage of drafts sent without editing.
If the honest answer to “how will we know it worked” is “we will see in six months”, the feature is too far from the user. Pick one where the feedback loop is short. The whole point of the first feature is to learn how your users respond to AI in your product, and you cannot learn from a signal that arrives twice a year.
What this filter rules out
Applying these four tests to a typical list is clarifying. The chatbot that answers anything usually fails on data readiness and on measurability. The prediction model fails on visibility of errors. The fully autonomous agent fails on all four.
What tends to survive is a small set of features that look modest: draft this, extract that, summarise these, classify this. They are not modest in effect. They are the features that make users say “the product understands what I am doing”, and that sentence is what the impressive features need in order to be trusted later.
Sequencing the rest of the list
Once the first feature is live and people are using it, the second becomes obvious in a way it was not before. You now know how your users react when the AI is wrong. You know whether they read the output or trust it blindly. You know which of your data is actually as good as you thought.
The list of ten does not go away. It gets reordered by evidence instead of by enthusiasm, and that is a much better way to spend a build budget.
If you have a list of AI ideas and want help working out which one to build first, that is a conversation we have most weeks. Talk to us about your product.