Why real customer behaviour matters more than ever in the age of AI
Product managers and engineers can spend months shaping a feature before anyone outside the business touches it. When customers get hold of it, they might use it exactly as expected or find a completely different path. They might ignore the main feature altogether and keep coming back to something the team barely noticed during development. This is where the real learning, and the interesting decisions, begin.
Meanwhile, AI has transformed how product and engineering teams work, making it possible to turn an idea into working software much more quickly. Coding assistants and automated testing tools are accelerating the pace every day. Ideas that once took weeks of engineering effort can now become usable prototypes within days, creating far more opportunities to test and ship — and to learn from customers as they start using new features.
As development speeds up and more new ideas reach customers, product teams have to quickly learn from early user interactions and decide how to respond. As software ships, the learning and decision-making have to speed up too. If learning can’t keep pace with development, users will be drowning in new features and “improvements” that they don’t want or that may even make their experience worse.
Building Faster Changes Everything
Recent research involving more than 100,000 GitHub developers illustrates just how quickly that shift is happening. AI coding tools increased coding activity by as much as 180%, while releases rose by around 30%. The researchers also looked at four app marketplaces, where new releases were climbing, but overall usage stayed flat or fell. That means far more software is competing for the same pool of customer attention.
That’s the gap post-launch data has to close: knowing which of the many things shipped actually earned a place in the customer’s routine, and which just added to the noise.
Say an online retailer launches an AI shopping assistant. It answers product questions, recommends suitable items, and appears to work exactly as planned. The next question is what customers are actually doing with it — and why.
Do people open the assistant at the beginning of a visit or only after search has failed them? Which questions lead to a product view or a purchase? Where do conversations end abruptly? And do returning customers use it differently from first-time visitors?
The answers reveal the role the assistant is really playing. A high interaction count might look healthy, but a closer look could show customers repeating the same question because the answers aren’t clear. On the other hand, a modest adoption rate might be okay if those users convert at a higher rate or explore a broader range of products.
Product analytics show what happened once the feature went live. Did people use it? Did it help them move forward or send them somewhere unexpected? The answers give product managers something concrete to work with when deciding what to change.
Learning From What Customers Actually Do
Because AI makes it much easier to introduce new capabilities into a product, many product teams are making far more changes than they have historically, which can cause unintended friction for users. For example, one team may launch an assistant while another quickly spins up recommendations or experiments with a new interface. Before long, customers are picking their way through several competing ideas, and the experience starts to feel crowded or disjointed.
Our retailer might discover that customers are moving between an AI search tool and the shopping assistant during the same task. Product analytics can show where they lose momentum and which route more frequently leads to a completed purchase. That gives the business a clear basis for simplifying the journey and focusing its investment where it will have the greatest impact.
Better Questions, Better Product Decisions
A lot of the product builder job today is deciding which problems are worth pursuing and how far to take an idea. The difficult part of making that decision is asking the right questions when examining the evidence.
A dashboard showing lots of customer activity can be misleading, because adoption means very little on its own. The important question is what the user was trying to do, whether the feature helped them get there, and whether they came back once the initial curiosity wore off.
The question should also change over time. In the first few days post-release, the priority might be confusing interactions or technical failures. A few weeks later, the focus shifts to whether use is repeating and if it links to a commercial outcome. There is little value in asking launch-day questions six weeks later.
AI can flag an unusual shift in behaviour and help product managers explore large volumes of event data. That shortens the gap between spotting a signal and investigating it.
Of course, a change in the data does not explain itself. A drop in usage could point to a broken workflow or the successful removal of an unnecessary step. People who know the product and its users still have to work out what that change actually means.
Keeping Pace With Your Customers
The gap between releases and reviews is becoming harder to ignore. If software is changing every week, a quarterly meeting leaves a great deal of customer behaviour sitting unseen. Product managers need important changes to reach them before the context around a release fades.
That starts with people knowing the outcome they own and being able to explore the relevant behaviour easily. AI technologies like Mixpanel can direct attention toward changes worth investigating and provide a record of what customers actually did. Product managers can then focus their time on the things that deserve a closer look.
The Best Product Teams Change Their Minds
Should this feature receive further investment? Would a small change remove friction at a critical point? Has the experiment told us enough? Or is it adding complexity that customers never asked for? Each answer gives product managers a decision to make. And the best PMs are willing to change their minds when the evidence points to a better outcome from the change.
The faster software ships, the faster product teams need to learn. For anyone shipping AI-enabled products, one question matters above all: Are you willing to challenge your own assumptions and learn from the post-launch user data? Are you willing to change your mind? Decide the answer before you launch. Then make sure you’re ready to see it — and act on it.
