Productside Stories

AI for Product Managers with Dean Peters

Featured Guest:

Dean Peters | Principal Consultant & Trainer at Productside
21/05/2024

Summary

In this thought-provoking episode, Rina Alexin interviews Dean Peters, Principal Consultant and Trainer at Productside, about how AI is reshaping product management. Dean reflects on his 20-year journey from software engineering to product leadership and shares his philosophy on focusing less on technology and more on solving meaningful customer problems.

He explains how AI is driving a shift from what’s possible to what’s profitable, and why product managers must master the fundamentals of discovery, problem definition, and experimentation. Dean encourages PMs to use AI as a tool for amplifying human creativity and decision-making—not replacing it.

Through stories from his early experiments with AI in the 1980s and his current work training teams worldwide, Dean outlines practical advice on how product managers can integrate AI responsibly and strategically into their organizations.

Takeaways

  • Focus on customer value, not just technical capability.

  • AI should support—not replace—human judgment and creativity.

  • Product managers must shift from “what’s possible” to “what’s profitable.”

  • Start by automating low-value tasks to focus on strategic work.

  • Use AI to improve conversations, not eliminate them.

  • Avoid “hope as a strategy” when integrating AI into products.

  • Experiment continuously—small, measurable tests drive learning.

  • Collaboration between product, data science, and engineering is essential.

  • Beware of “shadow AI”—innovation often happens at the edges.

  • Adaptability, empathy, and discovery are more valuable than coding skills.

Chapters

00:00 Introduction and Dean’s Journey into Product Management
03:49 Redefining Product Management in the Age of AI
05:33 From “What’s Possible” to “What’s Profitable”
07:56 Why Every Product Manager Needs AI Literacy
10:10 Learning from the Giants — AI Use Case Analogies
12:32 Balancing Pressure from Leadership with Realistic Execution
14:36 Dean’s First AI Experiment in the 1980s
17:42 Frameworks for Understanding AI Use Cases
19:46 The Future of Work and AI Anxiety
21:49 Using AI to Automate and Reclaim Strategic Time
24:06 The Role of AI in Product Experiments
26:32 Designing Experiments to Validate AI as a Solution
29:11 Coaching PMs to Think Beyond Technology
31:45 Understanding Shadow AI and Governance Challenges
33:56 Core Product Skills Still Matter: Discovery and Customer Focus
34:47 Advice for New Product Managers in the AI Era
36:07 Where to Find Dean Peters and Learn More

Keywords

AI product management, generative AI, product strategy, discovery, Dean Peters, Rina Alexin, Productside, experimentation, customer-centric design, automation, shadow AI, leadership, problem space, innovation, outcome-driven products, data science collaboration, product operations

Introduction and Dean’s Journey into Product Management

Rina Alexin | 00:00–01:03

Hi everyone, and welcome to *Productside Stories*, the podcast where we reveal the real lessons learned from product leaders all over the world. I’m **Rina Alexin**, CEO of Productside, and today I’m thrilled to speak with **Dean Peters**, Principal Consultant and Trainer here at Productside.

Dean Peters | 01:03–03:49

I’ve been with Productside for about two years now. Before that, I spent two decades in product management after starting out as a software engineer. Honestly, I got into product management because I found myself more interested in the *why* and the *who* than the *how*. A marketing colleague once told me, “You’re already doing product management — you might as well get paid for it.” And that’s how it all started.

Redefining Product Management in the Age of AI

Dean Peters | 03:49–05:33

Today, product management is changing faster than ever — not just the role itself, but also how products, teams, and organizations function. The challenge is shifting from asking *what’s possible* to understanding *what’s profitable*. Product managers need to think beyond technical capability to focus on creating value that delights customers *and* sustains the business.

From “What’s Possible” to “What’s Profitable”

Dean Peters | 05:33–07:56

AI made technology accessible and affordable, which flooded the market with enthusiasm — but not always direction. Now we’re moving into a phase where companies are asking, “How do we make this sustainable?” The conversation isn’t just about *AI product managers* — it’s that every product manager must understand AI to stay relevant.

Why Every Product Manager Needs AI Literacy

Rina Alexin | 07:56–08:16

So where should product managers without technical backgrounds start when it comes to AI?

Dean Peters | 08:16–10:10

First, keep calm and focus on the problem space. It’s easy to panic when leadership says “add AI to the roadmap.” Instead, go back to fundamentals — understand the customer’s pain, gain, and job to be done. Learn enough about AI to ask smart questions, not to build the models yourself.

Learning from the Giants — AI Use Case Analogies

Dean Peters | 10:10–12:32

Stand on the shoulders of giants. Look at how companies like **Uber** use AI for decision-making or how **Precision Lender** leverages it for financial insights. Analogous cases help PMs visualize realistic applications of AI without reinventing the wheel.

Balancing Pressure from Leadership with Realistic Execution

Rina Alexin & Dean Peters | 12:32–14:36

**Rina:** Many leaders are under pressure to include AI in their plans — even if they don’t fully understand it. **Dean:** Exactly. Leadership isn’t trying to be difficult — they’re under market pressure. The key is not to sprinkle AI on everything like parmesan on pizza, but to stay focused on real, validated problems worth solving.

Dean’s First AI Experiment in the 1980s

Dean Peters | 14:36–17:42

My first AI experiment was back in 1987. I wrote a Lisp-based program for a digital music tool to make compositions sound more “human.” I didn’t realize it at the time, but it was early AI — using rules and orchestration patterns to simulate creative performance. Half the music community loved it; half hated it. That’s when I learned disruption always brings emotion.

Frameworks for Understanding AI Use Cases

Dean Peters | 17:42–19:46

You can classify AI plays across four levels: 1. **Automation** – Tactical, task-based efficiencies. 2. **Workflow Intelligence** – Broader system-level insights. 3. **Assisted Experience** – Personalized support for users. 4. **Augmented Intelligence** – Humans and AI collaborating, like a coach and quarterback. That last category is where the real magic happens.

The Future of Work and AI Anxiety

Dean Peters | 19:46–21:49

AI will replace the *tactical* parts of product management — writing tickets, updating backlogs, etc. PMs who focus only on frameworks risk being outcompeted. The winners will be those who use AI to free time for strategy, customer discovery, and stakeholder communication.

Using AI to Automate and Reclaim Strategic Time

Dean Peters | 21:49–24:06

Automate the busywork so you can focus on what matters. If AI can handle the paperwork, you can spend more time talking to customers, leaders, and peers. Let AI handle the *what* — you focus on the *why*.

The Role of AI in Product Experiments

Rina Alexin & Dean Peters | 24:06–26:32

AI helps PMs move faster — generating user stories, wireframes, or hypotheses. But treat AI output as **conversation starters**, not sources of truth. Use them to facilitate deeper team discussions, not to replace them.

Designing Experiments to Validate AI as a Solution

Dean Peters | 26:32–29:11

Start small. Run weekly experiments to validate assumptions. Involve a **product quartet** — PM, engineer, designer, and data scientist — to keep testing grounded and iterative. Always bring real users into the conversation.

Coaching PMs to Think Beyond Technology

Dean Peters | 29:11–31:45

Leaders should coach PMs to stay strategic — don’t chase shiny tools. Before you build an AI model, ask: could we buy or integrate one instead? Talk to your **change management**, **security**, and **compliance** teams early to avoid governance surprises.

Understanding Shadow AI and Governance Challenges

Dean Peters | 31:45–33:56

“Shadow AI” happens when employees secretly use generative tools in banned environments. Instead of punishing it, study it. Those cases often reveal unmet needs — opportunities to create safer, official solutions.

Core Product Skills Still Matter: Discovery and Customer Focus

Dean Peters | 33:56–34:47

Despite the hype, the core skills of product management haven’t changed: discovery, empathy, customer focus, and outcome obsession. AI just amplifies them.

Advice for New Product Managers in the AI Era

Dean Peters | 34:47–36:07

Stay calm, stay curious, and use AI every day — not just for work, but for learning. Experiment with tools, question results, and apply that curiosity to your product.

Where to Find Dean Peters and Learn More

Rina Alexin & Dean Peters | 36:07–End

**Rina:** How can listeners find you, Dean? **Dean:** Easy — LinkedIn, Twitter, anywhere you type `/DeanPeters`. I love hearing how people are applying AI in their products. **Rina:** And for those wanting to go deeper, check out our *AI Innovation for Product Managers* course at Productside. Visit **Productside.com** for templates, webinars, and more resources.