Welcome to TiPS – the Topics in Product Series – a new podcast format powered by ITX and the team at Product Momentum. The TiPS mission is to engage the same important product space issues that you confront every day – but this time through the experiences of ITX product managers, UX researchers and designers, engineers, security analysts, and the rest of the team.

In this inaugural TiPS episode, Dan Sharp is joined by Sean Murray and Andrew Knoblauch to reflect on a recent Product Leaders Breakfast, hosted by Prerna Singh. Together, they draw on insights from event attendees to discuss how AI is being applied inside real organizations.

The central theme was clear: successful AI adoption depends less on hype and more on first principles and core product skills that drive disciplined product thinking, incremental progress, and strong decision-making.

Here’s what we learned:

Top-Down ‘Do AI’ Directive Is the Wrong Reason for Integrating AI

The integration of AI into software development is no longer the proverbial “hammer in search of a nail.” The days of doing AI for AI’s sake are behind us. Today’s product leaders focus on making incremental improvements tied to bona fide business problems.

As Sean points out, our response to the ‘do AI’ directive should be: “’Where do you want to see improvement? What outcomes are you looking for?’ I think back to our conversation with Teresa Torres, about applying best practices in the initiation and discovery phases of the SDLC so that when we actually get into building something, it’s gonna have some sort of relevant business value.” It’s a more grounded approach that reflects a broader industry need to align AI efforts with tangible outcomes..

Building Stakeholder Trust Through Incremental Change

Trust emerged as a critical factor in AI adoption, but not only in the technical sense. Instead, as attendees discussed, trust is built gradually through careful implementation and organizational alignment. Andrew explains that product teams build trust not by tackling the biggest, riskiest challenge – but by prioritizing low- to medium-risk opportunities while involving stakeholders early, especially those in Legal and Compliance.

“This idea of building trust among others in your organization.” Andrew continues. “We do this every day with our clients and with our own teammates. We learn about people’s concerns, what they care about.” The conversation reinforces the idea that AI should be introduced as a collaborator within workflows, not as a replacement for human judgment.

Decision Quality as the True Differentiator

One of the key threads weaving through our conversation was a return to foundational product principles – specifically, the importance of decision-making. While AI fluency is valuable, it does not replace the need for strong judgment and clear thinking. Teams that succeed will be those that consistently make informed, high-quality decisions, Sean says. “The biggest differentiator moving forward is gonna be decision quality…your ability to consistently make good decisions.” In this context, AI becomes an enabler, not the driver, of product success.

The conversation at the Product Leaders Breakfast (hosted by Prerna Singh) reinforces a familiar but essential message for all product leaders. AI does not replace core product skills; it amplifies them. Teams that stay focused on problem definition, stakeholder alignment, and disciplined execution will be best positioned to realize its full potential.

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John Haggerty brings more than 25 years of product leadership experience at companies like Datasite, Prodege, and Highway.ai. As co-founder and CEO of BiasHawk, John leverages his expertise in product management, behavioral psychology, and AI to develop an AI-powered platform that acts like a behavioral clinical psychologist to diagnose cognitive bias and heuristics in other AI models.

In this episode of Product Momentum, John joins Sean and Dan to explore how AI is reshaping product work while also introducing new risks. John’s message is clear: as AI accelerates execution, product leaders must confront the invisible risks that come with AI and double down on critical thinking, context, and judgment to deliver quality decisionmaking.

AI as an Accelerator, Not a Replacement

AI is dramatically compressing the time required to execute product work. Tasks that once took months can now be completed in hours. As we discover every day, speed does not eliminate the need for thoughtful product management. John argues that it merely shifts where product managers can and should focus their energy.

“As AI expedites the execution process,” John says, “it also allows us to automate the areas of our work where we really need to be involved in cognitive thinking, reasoning, and creativity.”

The Hidden Risk: Bias in AI Decision-Making

Large language models inherit the same cognitive biases found in human thinking, John adds. These biases influence not just outputs, but the reasoning behind decisions we make.

“It’s not what the decision is or what the output is, it’s more about how the AI model arrived at it.” This distinction is critical for product teams. Without understanding how AI arrives at conclusions, teams risk introducing flawed logic into their products, especially in high-stakes areas like hiring, healthcare, and financial management.

Monitoring AI: A New Responsibility for Product Teams

To address these challenges, John launched BiasHawk – an AI platform designed to monitor and evaluate AI systems for cognitive bias. The goal is not just testing outputs, but continuously assessing decision quality over time.

“We all understand that these systems are designed to evolve. They’re designed to change. They’re designed to drift. But who’s monitoring that to make sure that decision quality stays where it’s supposed to be.” As AI continues to evolve, the role of the product manager becomes even more critical — not less so. Execution may be faster, but judgment, context, and ethical responsibility remain firmly within our human domain.

John Haggerty, in his own words:

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Margie Agin is a seasoned go-to-market advisor for B2B technology scale-ups. She brings deep expertise across digital marketing, IT, and cybersecurity. As Founder and Chief Strategist of Centerboard Marketing as well as a former leader at companies like Cisco and Blackboard, she has built a career translating complex technical products into effective market strategies. In this episode (which marks her second visit to Product Momentum), Margie’s message is clear: go-to-market (GTM) is not a one-time event or a siloed function – it is an ongoing, cross-functional system that must connect product teams and broader business goals.

GTM: A Shared, Continuous Responsibility

It’s time to redefine go-to-market as a shared, continuous responsibility across teams, Margie says. Product managers in particular often feel disconnected because their fellow stakeholders in the organization misunderstand go-to-market as either a launch event or solely a sales function. Margie reframes GTM as “a coordinated cross-functional engine that spans product, marketing, sales, customer success, and even finance.”  It’s a perspective that challenges product teams to actively engage in downstream outcomes and collaborate beyond traditional boundaries.

Business Context Drives Product Contribution

Fundamental to making this critical connection between product team and business outcomes is embracing the product’s fit within the broader business and portfolio strategy. Margie reiterates a message shared by recent guests that product managers need to look beyond their individual product scope and consider how their work contributes to company-wide goals like growth, positioning, and revenue. “Think about your product within the context of the business and how it fits into the whole portfolio,” Margie urges.

Know Your Targets: Clarity of Audience and Signals Improves Outcomes

Rather than trying to boil the ocean by targeting broad customer segments, teams should focus on specific attributes and behaviors that indicate a strong fit. Defining a precise ideal customer profile and identifying meaningful signals of readiness bring a level of clarity to your message that enables more effective messaging, prioritization, and sales efficiency. “It [your target] can’t just be like, everybody that has money,” Margie says. “It has to be somebody with a defined problem and defined attributes – beyond just industry or size of company.” For product leaders, this reinforces the need to deeply understand customer context and bring that insight into go-to-market planning.

In the Age of AI, a Strong Point of View Still Matters

Finally, even as AI accelerates execution, it does not – indeed, can not – replace the original thinking and nuanced messaging. Teams must still define what makes their product unique and why it matters. AI can enhance delivery, Margie adds, but it cannot generate true insight or perspective. “The difficult part is always what the difficult part has always been, which is figuring out what you have to contribute to the conversation that is unique.”

Margie Agin, in her own words:

[04:23] When I think of go-to-market, I think one of the most important aspects is that it is connected across different teams.

[08:22] Go-to-market is all about connecting the strategy to the execution to make sure everyone is on point with the strategy.

[08:53] Product teams need to think about how their product fits into the context of the organization’s whole portfolio.

[11:30] As a company matures, its go-to-market strategy lands in one of three buckets: problem-market fit, product-market fit, and platform-market fit.

[19:29] We can’t try to boil the ocean and sell to everybody, right? Target customers can’t be ‘everybody who has money.’ Customers have to have a defined problem and some defined attributes, beyond just industry or size of company.

[23:58] That type of deep, nuanced thinking…that human work…I don’t think at this point, is something that is solved by AI.

[26:40] AI can execute a lot of work on your behalf, but only you know what ultimately you want the result to be.

Andrew Knoblauch leads Sales, Partnerships, and Acquisitions at ITX. He believes the best technology partnerships start with genuine relationships, and that understanding a business deeply is what turns a software engagement into lasting value. Andrew connects organizations with technologists and product leaders while remaining invested in delivering strong business outcomes.

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Product Momentum welcomes Rich Mironov back to the pod to help us drill down to the bottom line – literally. Rich is a Silicon Valley veteran and longtime product management advisor. He’s spent decades helping C-suite executives and product leaders connect their work to business outcomes. In this episode, Rich reinforces a single, powerful theme: product managers must translate their ideas into clear financial impact. It’s not enough to build great features – success comes from telling compelling “money stories” that resonate with executives and drive decisions.

Here’s what we learned:

Why Product Leaders Must Speak the Language of Money

Rich makes no bones about the yawning communication gap between product teams and executives. Product managers focus on features, processes, and operating models – while executives focus on revenue and outcomes. As he explains, “Any sentence that comes out of the mouth of a product leader that doesn’t have a currency symbol in it is one that the rest of the executive team can’t hear and doesn’t care about.”

As he reframes the role of product leadership, Rich explains that it’s not just about building the right thing – it’s about articulating how that thing makes money.

The Power of Simple, Structured “Money Stories”

At the core of Rich’s approach lies simplicity. He advocates for building “money stories” using just three numbers – two we know, one we estimate – to quantify potential impact. “A money story has no more than three numbers in it…two of the numbers you know, and one of them you’re going to reach into the air and make up or estimate.”

This framework isn’t about precision, Rich explains. It’s about enabling better conversations. By introducing even rough estimates, product leaders can engage sales, marketing, and executives in meaningful dialogue. The goal is to create alignment around opportunity size and business value, and shift the focus from abstract ideas to tangible outcomes.

From Features to Impact: Driving Better Product Decisions

On the pod, we’ve been talking a lot lately about “impact.” Rich’s approach, covered concisely in his recent book Money Stories, also highlights a critical shift in mindset: product managers must own the financial performance of their products. Without that, they risk being sidelined from strategic decisions. “If you can’t vaguely explain how the thing you do makes money, you’re just a cog in the process.”

This means knowing basics about your product – core metrics like units sold, pricing, and revenue – and using them to guide decisions. It also means prioritizing revenue-generating opportunities over less impactful work and being cautious with cost-saving narratives that may have real human consequences.

Bottom line: product leaders who connect their work to measurable outcomes are the ones who influence strategy, secure investment, and drive meaningful results.

Rich Mironov, in his own words:

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In this episode of Product Momentum, we’re joined by Bansi Mehta, founder and CEO of Koru UX Design, an enterprise healthcare UX agency supporting some of the US’s largest healthcare technology companies. We discussed the busy intersection of artificial intelligence, product management, and UX Design. Bansi’s Sense – Shape – Steer framework helps guide UX design teams as they integrate AI into their products – and avoid the trap of AI’s drive toward mediocrity that limits individual creativity and expertise.

Here’s what we learned:

Avoiding the Trap: AI Solutions’ Race to Mediocrity

AI’s ability to rapidly generate hi-fi prototypes and voluminous content brings great benefit, but also significant risk. The risk manifests in mediocrity – i.e., solutions that drive to the mean. This sense of “good enough” stifles designer creativity and diminishes the quality – the Delight – of the final product.

“The speed of AI makes it easier than ever to churn screens,” Bansi says. “But it’s designed to deliver to that average mean that allows us to say, ‘that works, that makes sense.’ And that’s really the trap….these days, there’s less patience in the industry for discovery and research.”

Introducing the Sense – Shape – Steer Framework

To combat this new reality, Bansi developed the Sense – Shape – Steer framework to help teams navigate the complexity of building AI-powered products.

Broader Insights, Future Outlook

The conversation underscores the notion that while AI accelerates development and content generation, it also requires subject matter experts in UX and Product to demonstrate greater vigilance than ever to maintain quality and relevance. The Sense – Shape – Steer framework calls on product teams to think first about user needs before considering whether and how to integrate AI.

Our episode with Bansi Mehta feels like the capstone conversation to recent episodes with Nesrine Changuel, Teresa Torres, and Oji Udezue, where we examined bringing Delight to the user experience, re-engaging Discovery in the development process, and adjusting to the Speed of today’s AI-driven development.

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Keith Lucas is a startup advisor, author, and leader who specializes in building high-impact teams. In this episode with Product Momentum, Keith delivers a master class on leadership, team building, culture, values, and motivation. Our conversation is especially relevant in the context of transitioning from a technical individual contributor to product team leader in high-tech organizations.

Here’s what we learned:

IC to Team Lead: Navigating the Mindset Shift

The transition from hands-on IC to leader of a highly technical team requires a mindset shift from “me to we.” The transition requires an adjustment of priorities from solely outcomes-based to team health, inspiration, and mental well-being.

“A simple trick I use, when I wake up every day my first thoughts are, is the team on a good path? Are they unblocked? Are they inspired and mentally healthy? Are they all in a good place to have impact? Knowing those things helps reduce friction on the team and increases the odds of our success.”

Two ‘New Leader Archetypes’ – Overcoming Team Dysfunction

Keith discusses two types of leaders who struggle in their new roles. The first is the hands-on leader who has fallen into the oxymoronic trap of trying to “micromanage at scale.”  The other is the visionary talent-oriented leader whose eagerness to succeed leads to the team’s being focused on too many things. The hands-on person is just trying to get stuff done by being effective, efficient, Keith says, while the talent person is committed to autonomy and building a team that scales.

The goal is to put both of those value sets together. For the hands-on leader, that means creating regular touch points with your team. For the talent-oriented leader, it’s about closing loops while showing the team how to go from vision to delivering real outcomes. “In both cases,” Keith adds, “use a regular cadence for when you get together to talk about progress, challenges, and course correction.” This approach creates the right kind of trust — a trust in the system that you have opportunities to contribute in a healthy way.

Value-Based Culture: The Foundation of Decisionmaking

Keith thinks about culture as “the team’s operating system.” And the foundation of that operating system is the team’s values — i.e., their standards of behavior.

“The team’s values are really the foundation of the operating system,” Keith says. “If the system is to be reinforced, then decisions about who gets hired, promoted, and retained must be informed by those values.” If that’s not happening, Keith adds, you end up with a culture that may be codified, but never truly realized.

Here’s some more key takeaways:

As the author of Impact: How to Inspire, Align, and Amplify Innovative Teams, Keith Lucas distills years of experience at the intersection of data, storytelling, and strategy into a practical framework that helps leaders move from player/coach to true team builder while avoiding common scaling pitfalls like diminishing impact, productivity loss, culture dilution, and disempowerment. 

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The Product Momentum team first met Oji Udezue following his keynote at INDUSTRY 2025. And we just knew we had to have him on the pod. Oji is a highly skilled product leader whose CV includes companies like Calendly, Atlassian, and Microsoft. In addition, he co-authored (with wife, Ezinne Udezue) Building Rocket Ships, Product Management for High-Growth Companies a manual for product leaders, product managers, and executives who want to build faster, better, and more profitably (raise your hand if that describes you!).

In this episode, we begin broadly by discussing the evolving role of product management in the era of AI. We then pivot quickly, drilling into some of the new challenges that require today’s product leaders to:

AI’s Impact and the Three-Speed Problem

Companies do three things to build great technology products, Oji says: customer science, construction/development, and go-to-market. Each step on this cycle moves at its own pace. Before AI, engineering speed was the process bottleneck. But with AI-driven automation, construction has become much faster, creating new challenges in synchronizing these three phases.

“We’re all going to spend a lot of time balancing that equation,” Oji adds, “finding the practices, the team structure, the team ratios, the new AI tools that help us keep this thing fast but then speed up customer science and GTM.“

Essential Skills for Today’s ‘Renaissance PM’

The AI transformation calls on product managers to add new arrows to their quiver of skills – e.g., curiosity, humility, agency among them. In the same way we transitioned from a pre-internet to internet environment, Oji adds, AI “requires brand new thinking.”

But the old skills still apply: “These are worth bringing up because a lot of PMs don’t have them: communication, creativity, the ability to ship, and leadership – being the kind of person people want to follow – all of that has to do with judgment. The role is evolving into that of a “renaissance PM” who blends traditional skills with new AI-related capabilities.

Evolution of the PM Role

Has the PM role evolved so much that the skills required to perform it are now preeminent to the role itself? Is that where AI is taking us?

“I’ve always thought the skills…the mindset…was way more important,” Oji offers. “The title just gives us a way to put it in a box. The title is nothing without the skills. That’s why I wrote the book. Because I want more people to have the skillsets” required to succeed in this new world order.

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For the past 20 years, Teresa Torres has championed the cause of product discovery. We’ve made progress, she says, but there are plenty of companies and teams out there who don’t know much about their customers – and still think they have all the answers. Is AI exacerbating the problem?

In this episode, Teresa returns to Product Momentum taking us on a rollercoaster ride that begins in a pre-ChatGPT world full of hopeful optimism in which product leaders were (slowly, steadily) recognizing the value good Discovery brings – but then spirals through phases of grief as AI-powered Delivery seems to have reclaimed our attention.

As you’ll hear, Teresa remains bullish on AI. But she’s also concerned that AI is pulling us in the wrong direction, making it easier and faster to build and, thus, putting even more emphasis on Delivery. Ever the optimist, Teresa believes that product builders can use the same technology that created today’s predicament to help us course-correct, refocusing our attention on high-quality Discovery practices.

Here’s a few of our key takeaways:

AI Might Be Helping Us Build the Wrong Thing

In our earlier episode with Teresa (58 / Innovate with Product Discovery, published shortly before the release her Continuous Discovery Habits, a must read for anyone in the software space), Teresa talked about how the product community under-emphasized Discovery and over-emphasized Delivery. It was a time highlighted by a gnawing anxiety that we were building the wrong stuff. Since then, the trend was moving back toward a focus on Discovery, Teresa says, until AI changed the trajectory again.

“AI is making it easier and faster to build software,” Teresa says. “But as we do, we’re once again putting even more emphasis on Delivery and forgetting to ask whether we’re building the right thing?”

AI’s Double-Edged Sword: Broad Participation vs. Product Coherence

Teams across the organization are now contributing to software development – a positive trend that Teresa calls awesome.

“We want to empower product teams and draw people closer to the customer to impact the product in positive ways,” Teresa says. “But it’s equally terrifying: who’s creating product coherence, and how do we make sure [each team] is serving the market and not their own specific needs?”

The Opportunity Solution Tree: A Structure Of Discovery That Doesn’t Change

With the Opportunity Solution Tree, Teresa provides teams with “a simple underlying structure that gives us a mental representation” of the interaction between what success looks like (outcome), our customers and their needs (opportunity space), and impact on our customers and our business.

“Do I think that’s ever gonna change?,” Teresa asks. “I don’t. We’re always gonna have to create value for our business. We’re always going to have to create value for our customers. Hopefully, we are doing one thing to accomplish both – not doing competing things.”

As AI enables more people to be makers, teams and organizations will learn new skills and allow everyone who wants, to contribute while still delivering a coherent product that serves their users.

“I actually think that’s gonna be net positive in the long run,” Teresa concludes.

You can catch even more of Teresa’s insights by checking out her podcast, Just Now Possible, which releases new episodes every Thursday. And, beginning this month (Jan. 2026), Teresa and Product Talk are launching a new course called, Business Fundamentals.

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Phil Hornby is an experienced product leader, coach, and technologist whose mission is to help product leaders think clearly, make strong decisions, and take powerful action that drives high-impact outcomes. He’s a regular speaker at product events and is co-host of the “Talking Roadmaps” YouTube channel and podcast.

If Phil were asked to distill product management down to its core, he’d tell us, “We’re paid to have an opinion.” Not simply putting a ‘licked finger to the wind,’ but trusting your experience and intuition to make high-quality decisions. Ah, and also to remember: there’s a difference between a high-quality decision and the “right” decision. There’s no way to guarantee that we make the right decision, Phil says, but there’re plenty of ways we can improve our odds.

Here’s what we learned:

Empowerment Is ‘Making Decisions that Stick’

At its most fundamental level, doing Product is decision making, Phil says. “That that’s like the whole heart of it. When we use the term empowered to describe product teams, we talk about their ability to make decisions that stick. If you can’t make a decision stick, then you’re not empowered. It’s really boiled down to this: empowerment is at the heart of all product work.

Trust = Character + Competence

Trust is an essential component in any relationship. Perhaps even more so in the often high-stakes world of product management. Phil’s hypothesis is that high-quality decision making cannot occur in its absence.

“Trust comes down to two core components,” he says, “character and competence. You want others to look at you and say, ‘That’s someone I can trust.’ That’s the character…. Then there’s competence: ‘Do I think you can make it? Have you got the skills to make that high-quality decision? And those two things combine to provide trust.

‘We’re Paid to Have an Opinion’ – Evidence-informed Decisionmaking

Phil talks about being evidence informed versus data driven, because “data can tell you anything. As product managers,” he adds, “I can massage the data to show whatever the heck I want it to do.

“We absolutely need to bring data into our decision-making process,” Phil continues. “Data is a form of quantitative evidence, but then we need the anecdotes and other feedback to complete the equation. But we’re humans, we also have intuition. And, dare I say it, we are paid to have an opinion – to understand our markets, to bring that tacit knowledge, which some people call product sense, and apply it to the context of the situation we’re in.”

Catch the entire episode with Phil Hornby and learn even more about:

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Michelle Parsons is a dynamic product leader who has led high-growth teams at Kayak, Spotify, Netflix, Hinge, and Lex. Her passion for building community and embracing new challenges has recently brought her to a leadership role in a new startup that helps people reconnect to themselves and one another.

In this episode of Product Momentum, Michelle joins Sean and Dan to talk about “making bets” – not just the seductive big bets that promise game-changing innovation – but also the smaller bets and quick hits that also play important roles in delivering value, validating assumptions, and mitigating risk.

Here’s what we learned:

The Balanced Portfolio Framework

At the heart of Michelle’s thinking is the notion of the Balanced Portfolio Framework – an idea she developed while leading product for kids’ content at Netflix. Under this framework, your roadmap is divided into three buckets – big bets, smaller bets, and quick hits – that help you pursue transformational innovation while delivering consistent value.

As you’ll hear, connecting the dots between them helps to ensure that product work is driven by the value delivered to users and your business.

Big bets start with user insights and clear hypotheses

Big bets are the bold, strategic moves that are super-impactful, but also come with a great deal of uncertainty. They start with user insights and clear hypotheses that address the following questions: What need are we trying to solve? Why does it matter to our users? What metric will this move – and why does that metric matter for business impact?

“These are the things that everyone wants to work on,” Michelle adds. “But they’re never just ‘cool ideas.’ They’re the big innovative features that bring your strategy to life. But they come with a ton of unknowns. Super impactful, but really, really risky.”

Small bets preserve resources and de-risk the big bet

Think of small bets as the “meat and potatoes” of your roadmap – incremental improvements like polishing UX, refining workflows, or optimizing metrics.

Here’s what Michelle says: “The small bets are really about the optimizations and enhancements, the things that consistently create incremental impact for your users. Not only do they touch on macro metrics like retention, engagement, and delight, but they also help to de-risk the big bets.”

Quick hits are the targeted work that accelerate learning

We’re all familiar with those small, fast, low-cost experiments or enhancements. These are the low-hanging fruit that support rapid learning.

“A quick hit is a learning task,” Michelle adds – “not to be confused with a quick win.” Certainly, they can also be quick wins, but “quick hits are really this body of work, discreetly tied a hypothesis or a data point that you want to prove out further.”

Use storytelling to align stakeholders around ‘why’

Michelle emphasizes that roadmap planning is not just an exercise in listing features, but a storytelling exercise. Because many stakeholders – executives, founders, investors –don’t live in the product trenches. To get buy-in, you need to clearly articulate: What problem we’re solving, for whom, why it matters, and how this work moves the needle.

Be sure to watch/listen to our entire conversation with Michelle, so that you can catch her thoughts about:

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