Your goal: AI-first

AI-First Product Teams

Decide faster, not just build faster.

One day for your product trios to redesign how they work with AI, on the work already on their plate. Then, if you want it, a 6 to 8 week sprint that measures what changed: how fast your teams learn, and which numbers move.

Book a free intro call →Assess your team
For product organizations whose teams already use AI tools and cannot yet show what they changed.
The 3W Loop: where it hurts in product analytics, why it happens from story-based interviews, whether the fix works in a scoped experiment

Worked with product teams at

  • Deviniti
  • UX Pilot
  • elyps
  • Taxfix
  • FOUND
  • desertcart
  • Syncron
  • Bayer

Why AI-First Stalls

Most product teams already use AI every day. Far fewer can show what it changed. Three reasons come up again and again:

Faster code is the small part.Writing and testing code is about 25 to 35% of the time from idea to launch. So coding assistants alone lift productivity 10 to 15%, while companies that changed the whole process report 25 to 30% (Bain, 2025). Most of the time sits around the code: deciding what to build, and learning whether it worked.
Usage targets get gamed.Companies that scored people on AI use are taking it back. Duolingo dropped AI use from performance reviews in April 2026, and Meta removed token counts from engineers' reviews in September 2026 after people inflated them to climb the leaderboards. Counting prompts measures activity, not progress.
AI amplifies the team you already have.Google's DORA research puts it plainly: AI does not fix a team, it amplifies what is already there (DORA, 2025). A team that ships features nobody uses will ship more of them, faster.

What AI-First Means Here

AI-first does not mean fewer people. It means the people you have learn faster, and spend the time AI saves on deciding what is worth building.

The trio stays. The tasks move.A product manager, a designer and an engineer still cover the four product risks between them. Marty Cagan expects teams to shrink from about eight people to three, with the three roles still distinct (SVPG, 2025). What moves is who does which task, and how fast. Tasks move, jobs do not →
More learning cycles, with rules.Interviews synthesized with AI the same day, prototypes in front of customers within days instead of sprints, and a test card for every bet. Plus written rules for where a human checks the AI's work before it shapes a decision, because a confident summary is not evidence.
Outcomes, not usage.We measure how long a question takes to become evidence, how many bets each trio tests, and the product numbers those bets were meant to move. Never prompts, tokens or seats.

Why We Are Running This as Founding Pilots

We have not published an AI-first result yet, and we will not borrow one. So this quarter we are running the workshop and the sprint with two product organizations as founding pilots, at a pilot price, and we will publish what we learn with their permission. Here is what the pilots build on:

The loopThe 3W Loop, with public resultsThe workshop and the sprint run the 3W Loop: where it hurts in the data, why it happens, whether the fix works. On conversion its results are public, like UX Pilot's 44.7% lift in free-to-paid from one experiment on 120,000 users. These are the loop's results, not AI-first ones. How the loop works →
In the roomAI in the workshop alreadyAt the 3W Loop masterclass at WaysConf 2026, 28 participants rehearsed customer interviews against an AI interview simulator. A rehearsal for the reflex, not research: the real interviews still happen with real people. How that morning ran →
From the insideAn AI product, built and grownAleksander was the first hire and Head of Product at UX Pilot, an AI design tool with more than a million users, where monthly recurring revenue grew from $300k to over $450k during the engagement. Read the case →

Common Problems We Solve

If AI-first is your goal, some of these will sound familiar:

Everyone uses AI. Nobody can say what it changed.The licenses are bought, the usage dashboard is green, and the roadmap looks like last year's.
Specs got faster. Decisions did not.AI drafts the PRD in minutes, and the PRD still waits two weeks for anyone to decide whether to build it at all.
Prototypes multiply. Evidence does not.Every idea gets a clickable prototype by Friday, and none of them has been in front of a customer.
Each role learned AI on its own.Product managers took one course, designers another, and engineers got a coding assistant. The trio never agreed how they work together now.
Leadership wants an AI strategy. Teams want rules.Which data can go into which tool, who checks the output, and what counts as evidence are answered differently on every team.
“AI-first” sounded like fewer people.So the people you most need to change how they work are quietly protecting their jobs instead.

How It Works

Workshop · One day

Redesign the loop with AI, on live work

Your trios bring the work already on their plate. In the morning we map where AI fits at each step of the 3W Loop, from reading the data to synthesizing interviews to prototyping the fix, and where a human has to check it. In the afternoon each trio runs one real bet through the new loop. You leave with your team's working rules and a test card for every trio.

Sprint · Weeks 1 to 2

Baseline: measure before anything changes

We measure where you start: how long a question takes to become evidence, how many bets each trio tests in a month, and the product numbers those bets are meant to move. Without a baseline, an AI program can only ever report usage.

Sprint · Weeks 2 to 6

Bets every week, with AI in the loop

Each trio runs a bet a week: interviews synthesized the same day, prototypes in front of customers within days, the test card written before the result comes in. Weekly working sessions in the flow of work, and the rules updated as the team learns where AI helps and where it misleads.

Sprint · Weeks 6 to 8

Readout: what changed

A learning card for leadership: the baseline against the end of the sprint, which bets moved a product number, what AI sped up and what it did not, and the rules worth keeping. Then the next loop, already scoped, run by your team.

What's Included

AI-First Trio Workshop (1 day) - the loop, redesigned with AI
  • A map of your discovery loop with where AI helps at each step, and where a human checks
  • Working rules for your trios: which data goes into which tool, who reviews AI output, and what counts as evidence
  • A test card for every trio, on a real bet from its current work
  • The 3W Loop AI kit set up in the AI tools you already use: Copilot, ChatGPT, Claude or Gemini
  • Whole trios, not single roles: up to 16 people, online or on-site
AI-First Product Team Sprint (6 to 8 weeks, optional) - measured on outcomes
  • A baseline of learning speed and of the product numbers your bets target, taken before anything changes
  • A bet a week per trio, with AI in the loop, on live work
  • Weekly working sessions in the flow of work, with the rules updated as you learn
  • A readout every two weeks for the leader who owns the outcome
  • End of the sprint: a learning card with the numbers, the rules worth keeping, and the next loop scoped
Format
Workshop: one day · Sprint: 6 to 8 weeks, a bet a week per trio · Remote or on-site · Runs in your own AI tools, so we host none of your data
Recommended involvement
Whole product trios (product manager, designer, engineer or tech lead), the leader who owns the outcome, and whoever approves your AI tools

What We Need From You

AI-first only works on real work. Before we start, we agree on five things:

  • Whole trios: a product manager, a designer and an engineer or tech lead from each team taking part
  • Live work: a bet each trio is already working on, not an exercise
  • AI tools your company already allows for this work, even if only one. We work inside them, and your data stays with you
  • Access to customers for interviews and prototype tests
  • For the sprint, a product number each trio owns, and a leader who wants to see it move

FAQs

Which AI tools do we need?
The ones your company already allows. The workshop works with Copilot, ChatGPT, Claude or Gemini, and the 3W Loop AI kit is set up in whichever you use. If you are still choosing, we can run it in the tools you are piloting.
Do you host our data?
No. Everything runs in your own AI tools and your own workspace, so ProductTrio hosts none of your data and there is no new vendor for your security team to review.
Is this about cutting headcount?
No. We do not run headcount programs, and some companies that cut first have had to walk part of it back: Klarna started hiring people again after leaning on AI for customer service. The aim is that the people you have spend less time producing documents and more time deciding what is worth building.
How is this different from an AI course for product managers?
A course trains one role at a time, on tools. This works with whole trios, on their live work, and ends with rules your team keeps and numbers your leadership can read. Take a course to learn a tool; use this to change how your teams decide.
What if our teams barely use AI yet?
Then the workshop starts with where AI is safe and useful in your loop, and sets the rules before habits form. That is easier than undoing habits later.
What do you measure?
How long a question takes to become evidence, how many bets each trio tests, how often a bet changes a decision, and the product numbers the bets target. We do not count prompts, tokens or seats: Duolingo and Meta both scored people on AI use and have since dropped it.
What does a founding pilot cost?
Less than the program will, in exchange for being one of the first two and letting us publish what we learn, with your approval. We agree the price on the intro call, once we know how many trios take part.
Do we have to continue to the sprint?
No. The workshop stands on its own: your trios keep the rules, the AI kit and their test cards. You decide on the sprint afterwards, with the first bets already running.

Reading for AI-First Teams

Six field notes on the thinking behind this program:

Product Discovery

Building got 10x cheaper. Building the right thing didn't.

AI collapsed the cost of shipping and left the cost of being wrong exactly where it was.

Product Roles

AI made everyone think they can do everyone else's job

PMs design, designers ship, engineers own product. Tasks moved. Accountability didn't.

Product Discovery

Stop building MVPs. Build a prototype that answers one question.

Pick the prototype from the assumption, not the roadmap, and know when to build the real thing.

Product Roles

Are you a Product Builder or a Product Architect?

Ravi Mehta's 12 competencies split most PMs into two shapes, and the team version matters more.

Product Discovery

Velocity isn't the point. The 4 product risks that sink launches.

Teams ship fast and still fail, because they efficiently build the wrong thing.

Product Discovery

What is the 3W Loop? Where, Why, Whether

Three questions, asked in order, each with its own instrument.

Service Area

Designed for product organizations with several product teams that already use AI tools. Available remotely (global) and on-site across Europe. Aleksander is based in Wrocław, Poland.

Is your goal a specific number? Conversion and retention have their own pages: the Funnel Conversion Sprint and the Retention Diagnostic & Sprint.

Product Model Maturity Assessment

Before the call, see where your teams stand.

AI makes whatever your teams already do faster, so it pays to know what that is. Fifteen questions, about three minutes: your Maturity Index on screen and by email, with the three principles to improve first.

Assess your team
Free · 3 minutes · Your Maturity Index on screen and by email

Decide faster with AI, not just build faster.

Book a free intro call →