From Customer Interviews to an Opportunity Solution Tree—In Minutes
Most product teams know they should be interviewing customers. And more teams than ever are actually doing it. That's the good news.
The bad news? Many teams are struggling with what comes next.
I've written before about the challenge of interview synthesis—going from a stack of recordings to a structured understanding of your opportunity space. It's cognitively demanding work. It requires you to extract the key moments from each conversation, identify the opportunities buried in those moments, and then organize those opportunities into a coherent picture of the opportunity space.
I hear from teams all the time who are interviewing regularly but can't seem to close the gap between what they're learning and what they're doing with it. I've heard countless teams say, "We need to stop interviewing so we can catch up on what we've already learned." Many of those teams stop and never start again.
They have recordings sitting in a folder. Maybe some notes in a doc. But they haven't turned any of it into an opportunity solution tree. They haven't synthesized what they've learned. Their research isn't actionable.
That gap is what I want to help close.
What If You Could Go From 3 Interviews to a Draft OST in Minutes?
In my annual letter, I shared that I'm pursuing two goals with AI: 1) build tools that help you learn discovery and 2) build tools that help you do discovery.
The learning tools are coming through our on-demand courses.
Today, I'm excited to share the first big step on the "do" side.
I'm expanding my partnership with Vistaly—the opportunity solution tree tool many of you already use—to bring AI-powered discovery tools directly into their platform.
If you've read my article on where AI helps and hurts with interview synthesis, you know that good synthesis happens in two steps: First you synthesize each interview separately, then you synthesize across interviews. Most AI tools skip the first step and go straight to cross-interview analysis—which is exactly how you lose the nuance and context that makes your research actionable.
What we're building does both steps. You upload three interviews for the same product outcome. The AI extracts the key moments and opportunities from each one separately. Then it synthesizes across those interviews and generates a first draft of your opportunity solution tree for you.
Three interviews in. A draft OST out.
What This Is (And What It Isn't)
If you listen to All Things Product, you know that I have been critical of tools that promise one-click opportunity solution trees. Those tools ask you to describe your market, click a button, and get a tree. The point of an opportunity solution tree is not to have one—it's to synthesize what you're learning from real customers so your team can align on the best path forward. A one-click tree built from made-up data is useless.
What we're building is fundamentally different. It starts with your real customer interviews. The AI does the heavy lifting of extracting key moments and opportunities from those conversations and organizing them into a draft opportunity solution tree. But it's a draft. You review it, refine it, reorganize it. You bring your judgment and context to the work.
In my synthesis article, I described a vision for what AI-aided cross-interview synthesis could look like—AI identifying common opportunities from across your interviews, suggesting a tree structure, and facilitating the team's review. I also admitted I hadn't figured out how to give AI access to an opportunity solution tree. This partnership with Vistaly solves that problem. We're building that vision directly into the tool where your tree already lives.
My own experiments with AI synthesis have shaped how we're approaching this. When I ran my interviews through Claude, it caught opportunities I missed. But I also caught things it missed. The highest-quality synthesis came from combining both perspectives. Research (see here and here) backs this up: Experts working with AI outperform both experts working alone and AI working alone. That's the model we're building toward—AI generates the draft, you bring the expertise.
If you read my annual letter, you know I have mixed feelings about AI doing discovery work for us. I still believe there's real value in doing the synthesis yourself. But I also know that a draft OST you actually refine is better than a perfect process you never get to. This is about raising the floor—helping more teams get to a structured opportunity space, even if they aren't doing every step manually.
Apply for the Alpha
We're looking for a small group of alpha partners to help us shape this product. Here's how it works:
To apply, sign up for a free Vistaly account and upload three customer interviews for the same outcome or product space.
We'll select alpha partners from the applicants. We want a range—different interview styles, different levels of experience, different product spaces. Selected partners will get access to the AI-powered synthesis tools and will work closely with us to shape the product.
Even if you aren't selected for the alpha, your application gets you to the front of the line when we enter beta. So there's no downside to applying.
A few things to know:
- The three interviews need to be for the same outcome, goal, or product space. We need related interviews to generate a meaningful OST.
- You don't need to be a Vistaly user today. The account is free.
- You don't need to be an expert interviewer to apply—we're looking for a range of experience levels. But we are particularly looking for folks who are conducting story-based customer interviews.
What's Next
This is the beginning, not the end. Our vision is to build a full AI-powered discovery suite inside Vistaly—from interview analysis to complete interview snapshots to opportunity solution trees and beyond. We'll be learning alongside our alpha partners and sharing what we discover as we go.
If you've been wanting to bridge the gap between your customer interviews and your opportunity space, this is your chance to help shape how that works.
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