When AI makes building cheap, the job is knowing what's worth building

A customer discovery call on Monday. A working demo on Wednesday. What changed in between wasn't my speed.

On a Monday I sat in on a one-hour call with a customer and mostly listened. It was a discovery call, so my job was to watch them work. Three of their people took turns showing me how they solve their day-to-day problems in a different way. By Wednesday I had a 30-minute demo of our product that walked their own workflows back to them.

The 48 hours is the part people ask about. It wasn’t sprint energy or an all-nighter. The call handed me almost everything I needed, and I then used AI to automate the pain that users showed me. I didn’t have to add it to a sprint backlog, run a prioritization debate, or wait for the next planning cycle.

What the call actually gave me

I stopped thinking of it as “the customer uses tool x to do y things.” Three different people used one of our competitor’s tools, each for their own job, and none of them thought they shared it with the others.

Their marketing person had the workflow that mattered most. After launching a personalization campaign, she needed a way to confirm that their targeted users saw the personalized offer. That meant pulling a list of users, checking each one against five attributes in a separate system, and watching session replays one at a time to catch the mismatches. Minutes per user, thousands of users, weeks of work per campaign. She got the targeting right in the end, by hand, at a cost that made me wince when she described it.

Their support person and their product manager had their own routines too, both narrower. But the marketing workflow was the one I kept thinking about on the drive back, because it was weeks of a smart person doing by hand something a machine should do in seconds.

I wrote each person down as a workflow, not a feature request. A person, and the exact sequence of steps they take.

Why workflows instead of notes

The old version of me would have left that call with two pages of notes about what the customer wants, and the notes would have flattened all three of those people into one line: they need better analytics. Then I would have taken that line to the next sprint planning meeting and argued with my engineering team about where it ranked against everything else.

Writing it as a workflow is different because it keeps the steps in order and keeps the persona attached to them. Once I had “marketing: launch campaign, pull the user list, check five attributes each, watch replays for mismatches,” I didn’t have a requirement anymore. I had a script. The demo is that same sequence, run in our product, with AI collapsing the slow middle.

The part AI actually changed

When I did the demo of the prototype, I walked the marketing workflow back to her out loud. The weeks of manual checking, the five attributes, the replays one at a time. Then I showed that in our product, you can ask in plain English the same question she used to answer by hand: show me the users who saw this offer but didn’t meet the criteria. A few seconds later, there was the list.

My job on the call was to see that her weeks of manual effort were the thing worth automating. The build was fast because the hard part was noticing, not coding.

I put that moment last in the demo on purpose, because it was the one I wanted the room to feel. Before it, I matched the workflows they already trusted, step for step, so they knew we could do what they rely on today. I didn’t pitch our product as a replacement for the system they treat as their source of truth, because we aren’t one. You match what they trust first, then you show them the thing that used to be impossible.

Why it’s repeatable

The 48 hours worked because almost none of it was invented from scratch. The call gave me the people and their workflows. The workflows became the demo. AI turned the slowest workflow into a single query. If I get another discovery call next month, I run the same steps, and I’ll be about as fast, because the speed was never mine. It was in the method.

When AI drastically shrinks your build time, it is important for you to have clarity on what is worth building. As an AI product builder, I constantly find myself asking: does building this truly solve the user’s problem, and does it capture value? This is what building looks like for me now.

You sit with a real user and watch where they lose hours repeatedly. You write down those exact steps precisely enough that AI can do them. The best demo is just their own job, with the slow part taken out.

You need to build the judgement skill to decide what to build and then capture their workflows precisely before you leave the room.