Is AI turning your team into a feature factory?

Your team ships more than it did two years ago. Is your revenue going up as well?

Since AI coding tools took off, the request we hear most from clients is some version of "Can you add <insert 5 new random features>?" We can build them faster than ever, but most of them won't move a single number, because nobody checked whether users wanted them first.

Each day, Github reports more and more average PRs, but as you can imagine, most of those PRs never get merged or see the light of production. That gap has a name: the AI productivity paradox.

AI also changes how fast people feel. In a randomized trial by METR, experienced developers working in codebases they already knew took about 19% longer to finish tasks with AI tools. They still believed they were faster.

Judging by the fact we just stated, good software was rarely held back by typing speed. The slow and tedious part was always deciding what to build, what will bring more customers in and make existing ones stay longer.

So when building gets this cheap, building the wrong thing gets cheap too. That's how teams end up running a feature factory.

Feature factory (n.) - critical term for a product and development team that measures its success by how many features it ships (output) rather than the actual value or problem solved for users (outcome)

When development moves this fast, it's easy to get lost in retro meetings and in lists of feature ideas that came to someone while cooking or at the gym. Creativity isn't the problem. A client without it would never have built a product in the first place, the issues start when ideas were built on optimism instead of data.

Remember when AI APIs first came out? Suddenly every product's next feature was "AI.". There was no clear reason why their product needed AI, what data it could use to produce something valuable, how much it would cost, or how it would fit into the customer's workflow…I could go on, but you get the point.

It used to be different. When a complex feature meant waiting one to two months, clients took their time, they thought through the approach, checked their assumptions against real user data, and only then decided if the feature deserved the effort.

AI took away that waiting period and the “thinking” went with it. What's left is low feature adoption and what that means for the client is that they wasted money on development and gotten 0$ back.

AI made building faster for us too, AI agent code new features → our engineers review them, so less of their time goes into writing code and more goes into architecture, trade-offs, reviews and product decisions. Most of that saved time goes into feature prioritization: deciding what's worth building before we start writing code.

Here's how that works on a client project:

1. A product manager challenges the brief. Every project is led by a PM who understands the client's business. When a list of five new features lands on our desk, the first question is which customer problem each one solves, and whether it moves the product toward where it should be in two years.
2. We look at the data the product already has. Before planning anything new, we check how people use the current features: which ones they come back to, which ones they ignore, and where they drop off.
3. Every feature gets a clear spec. We write structured specs that say what the feature does, who it's for and how we'll know it worked. Writing that down is often where a weak idea falls miserably which is far cheaper than finding that out after launch.
4. We prototype before we build. We design features in Figma and use AI to turn them into clickable prototypes then use it to showcase the idea to our client and ideally a few of their customers.
5. We ship in sprints and check the results. Work goes out in sprints, if we see a feature isn't sitting right with the user base, we see it early and adjust the next sprint instead of piling more features on top.

This workflow is how we took Reggy from idea to an MVP that secured their funding, and then to a platform that landed their first customers.

Developer teams can now use AI to ship more and that won't change, but more development ≠ more customers. The teams that get real value from AI use the time it saves to look at usage data, talk to their customers and say no to most of the ideas on the list.

So the next time "Can you add these five features?" comes up, start with a question: which of them will your users actually use?

If your backlog keeps growing while new features do not get you more customers, meet the team behind our process and discuss your idea with us.