AI makes product manager artifacts easy to produce
Everyone walks into meetings with “data” now
Evidence alone no longer wins decisions
Storytelling turns observation into belief
The best product managers don’t just know — they prove it
Hey folks,
You’ve been in this meeting.
A product manager presents a recommendation backed by real research. Customer interviews. Observations. Patterns. The kind of work that should make the decision obvious.
Then someone says:
“I have data that says something different.”
The room goes quiet.
Now it’s not about the customer anymore. It’s a standoff. Two datasets. Two interpretations.
And usually?
The louder voice wins. Or the more senior title.
Now add AI to that moment.
Everyone in the room can generate charts, summaries and “insights” in minutes.
So if everyone has data…
What actually wins?
This is the shift most teams haven’t internalized yet:
Data is now table stakes. Storytelling is the differentiator.
AI has made it possible for anyone to produce:
Research summaries
Personas
Competitive analysis
Prioritization frameworks
Fast.
So the question is no longer:
“Who did the work?”
It’s:
“Who made the room believe the work?”
Bad: “Users are dropping off at step 3. We should improve onboarding.”
Better:
“I sat with 6 customers last week and walked them through onboarding.
Three of them stopped at step 3.
Not because they didn’t want to continue, but because they didn’t understand what to do.
One of them literally said: ‘I don’t know what this is asking me to do.’
Another tried clicking multiple elements before giving up.
So this isn’t a feature gap.
It’s a clarity problem.”
Now the solution feels earned:
We don’t need more features here. We need to make the next step obvious.
A real story is what makes your insight credible.
But that same story has to work in different rooms, because each stakeholder is responsible for different things.
A designer is looking at the experience. An engineer is thinking about complexity and systems. A CFO is looking at revenue and efficiency.
The insight doesn’t change.
But the way you tell it has to connect to what each of them cares about.
To Designers
“In our onboarding flow, step 3 asks users to [configure their first workspace].
I watched three users get there and stall.
They hovered, scrolled and one of them said, “I don’t know what this is asking me to do.”
They didn’t fail, the interface didn’t guide them.
This is a clarity problem.”
To Engineering
“In onboarding, step 3 is where users are asked to [configure their first workspace].
We’re seeing consistent drop-off here and it’s not performance or bugs.
Users don’t understand what’s expected.
If we add more logic or complexity at this step, we’ll likely make it worse.
We need to simplify the flow before we scale it.”
To a CFO
“In our onboarding, step 3 asks new users to [configure their first workspace].
About 30% of users drop off at that point because they don’t understand what to do.
That means we’re paying to acquire customers who never activate.
Clarifying this step improves conversion without increasing acquisition spend.”
The insight doesn’t change. The story highlights why it matters to them.
Key guidelines
Don’t present data -> anchor it in real moments
Don’t generalize -> use specific observations
Don’t tell one story -> adapt it to the room
Don’t jump to solutions -> show how you got there
In a world where everyone has data, the advantage doesn’t come from having better information.
It comes from proving you understand the problem and making others see it too.
Storytelling as Competitive Advantage in the AI Era
April 7, 2026
📍Live online
Josh and I will break down how storytelling helps teams turn insight into alignment and better decisions, especially in an AI-driven world.
Register for free here
🎁 Bonus for people who join live: We’ll give 3 attendees access to our Storytelling AI Coach.
If you’re thinking about how AI is changing leadership and decision-making more broadly, Barry O’Reilly’s new book is worth a look:
Artificial Organizations
Most leaders are being asked to make faster, higher-stakes decisions with AI, but few have a system for how to actually do that.
Artificial Organizations shows leaders how to combine human judgment with machine intelligence so decisions get faster, execution gets tighter, and results compound.
It includes case studies from Slack, American Airlines, Skyscanner, and Progyny, and hit #1 on Amazon Best Sellers on day one.
Grab your copy here: https://geni.us/artificialorgs
If you want to move beyond understanding and start applying this in your day-to-day work:
Reach out to me directly if you want to bring this way of working into your team. We run these workshops in-house and tailor them to your product and context.
Or join one of our upcoming sessions:
with Chantal Botana & Maurice McGinley | 💬 English
April 16, 23 & 30, 2026
📍Live online (Zoom)
Register here
with Rich Visotcky | 💬 English
April 21, 2026
📍Live online (Zoom)
Register here
with Federico Zuppa | 💬 Spanish
April 22 & 29, 2026
📍Live online (Zoom)
Register here
with Rich Visotcky | 💬 English
April 22, 29, May 6 & 13, 2026
📍Live online (Zoom)
Register here
with Hala Saleh | 💬 English + Arabic
May 25, June 1, 8 & 15
📍 Live Online
Register here
Learn more about upcoming Sense & Respond Learning workshops here.
Interested in working together? Please reach out.
In case you need it, here's a description of what I do.
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