For most of my career, moving fast was a liability.
In many of the rooms I was in, the expectation was that you researched before you built. A colleague might spend three weeks studying a feature: stakeholder interviews, competitor analysis, a long document.
I would usually have already built something.
By the time the research finished, we often ended up in the same place anyway. The difference was that I had skipped the process everyone else considered necessary.
Over time I learned to adapt. Slow down publicly. Produce the artifacts. Earn alignment before moving.
Only now, many years into my career, does that instinct feel like an advantage rather than something I have to manage. I suspect AI is part of the reason.
What research is actually doing
Research isn’t the problem. It exists for a reason.
If you’ve never shipped something before, studying how others solved it helps. You’re building the mental model your intuition doesn’t yet have.
For a long time I underestimated this. I assumed people doing weeks of research were just being slow. Some were, but many were doing exactly what they needed to do: constructing the experience I already had from building similar things many times before.
The difference wasn’t diligence. It was starting point.
What AI changes is the cost of acting on that intuition.
When building something took weeks, making the wrong decision carried real consequences. Spending more time researching before committing made sense.
But if implementation is cheap, the balance shifts. The penalty for trying the wrong approach becomes much smaller, while the cost of analysis stays the same.
Intuition as compressed experience
People sometimes frame intuition as the lazy option. Skip the process, trust your gut.
To me intuition isn’t guesswork but accumulated experience: grit.
Over time, patterns repeat. Certain interface choices fail in predictable ways. Certain tradeoffs surface again and again. Eventually your brain learns to recognize them quickly.
When you see a problem and immediately sense the direction it should go, you’re not skipping thinking. You’re retrieving a decision from a long history of similar problems.
For most of my career, this wasn’t always easy to rely on socially. Moving too quickly could read as dismissing the process others needed to feel confident in the decision.
The trust problem
There is also a more practical issue.
Intuition is personal context compressed into a decision. The problem is that nobody else has that context.
The person who spent weeks researching can explain their reasoning in detail. They have documentation. They can walk a client through every step that led to the conclusion.
If your answer is simply “I’ve built enough of these to know,” that rarely satisfies a room that needs to justify the decision. Research, in those cases, is creating trust.
One thing that helps bridge this gap is building early. A working prototype gives people something concrete to react to. The conversation shifts from trusting a claim to examining something real.
But it doesn’t eliminate the need to explain the thinking behind it.
Conclusion
Research-heavy processes have always been a reasonable hedge against inexperience. For people still building their internal models, they serve an important purpose.
The difficulty is when the same approach becomes the default for everyone, regardless of experience or how quickly things can now be built.
For years I had to adapt my instincts to processes that weren’t designed for them.
Now those instincts feel like they finally exist in an environment where they make sense.
Intuition built through experience isn’t the opposite of process.
For the right person working on the right problem, it may have always been the process
Frequently asked questions
How does AI change the balance between research and intuition in product work?
AI dramatically reduces the cost and time of implementation, shrinking feedback loops from weeks to hours or days. When it becomes cheap to build and rebuild, the penalty for acting on intuition is much lower, while the time cost of long research phases remains the same. This shifts the bottleneck from execution speed to judgment about what is worth building. As a result, intuition built from experience becomes more valuable, and extended research can turn into the main source of delay rather than a necessary safeguard.
When is heavy research still valuable despite faster AI-driven iteration?
Heavy research remains valuable when people lack prior experience with a problem space and need to build mental models from scratch. It is also crucial in environments where decisions must be justified to stakeholders, such as agencies or client work, where evidence, artifacts, and paper trails create trust. In these contexts, research does social and political work that pure intuition cannot, helping teams answer detailed questions, show due diligence, and align multiple parties around a direction before committing significant time or budget.
How can experienced builders make their intuition more acceptable to stakeholders?
Experienced builders can make their intuition legible by pairing fast, intuition-driven building with artifacts that others can react to. Early prototypes turn gut feelings into concrete examples, shifting conversations from "trust me" to "look at this." They should also practice explaining their reasoning in terms of patterns from past work, tradeoffs considered, and alternatives ruled out. The goal is not to abandon intuition, but to translate it into explanations, demos, and documentation that satisfy stakeholders who rely on process, evidence, and clear justification.
