Product Momentum Podcast

Margie Agin, seasoned go-to-market advisor and founder of Centerboard Marketing, helps B2B tech scale-ups translate complex products into market traction. She reframes go-to-market as a continuous, cross-functional engine. Topics include who should own GTM, defining business context for product teams, GTM maturity stages, sharper targeting signals, AI’s role, and crafting distinct product messaging.

Rich Mironov, Silicon Valley veteran and author of Money Stories, helps product leaders link work to dollars. He explains why speaking in currency matters. He presents a three-number “money story” framework to quantify impact and spark business conversations. He highlights using upsell, retention, and unit-economics templates to turn features into measurable revenue.

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. Sense. Understanding the Problem/Opportunity.“Sense is where you’re really creating that sense of what is worth solving,” Bansi explains. “It’s the intersection of what the user needs, what insights we have in terms of their challenges, and the opportunities that are present. But we mustn’t stop there. We then look to see what AI can do for us. And where we see the intersection, that’s the sweet spot.” Shape. Designing the AI-Enhanced User Experience.We emerge from the Sense step with rich insights into our user’s desired experience, Bansi continues. “And as we approach Shape, we do so with an emphasis on the kind of UX challenge that we are trying to solve – from the user’s perspective. Using a storyboard, we proceed frame by frame to define the user’s journey, the problem that we are trying to accomplish.” Steer. Implementing, Evaluating, and Iterating.The Steer step comes once you have built something and you launched, Bansi says. “This is where we define and clearly articulate our AI eval criteria that we’ve said are critical for product success,” Bansi adds. “I’ve seen products make it or break it depending on whether they got their AI evals right. It’s one thing to hypothesize that your solution will work. But it’s a completely different thing when you actually try to build sophisticated agentic AI layers where there’s multiple configurations and prompts.”   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. The post 182 / How ‘Sense Shape Steer’ Helps UXers Design AI Solutions, with Bansi Mehta appeared first on ITX Corp..

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: 04:58 – Moving from chief IC to chief team builder 07:32 – Micromanaging at scale is an oxymoron 13:08 – Values: embrace them, socialize them, apply them 18:07 – Vision Doc: The anti-job description 20:21 – Start with Goals; Structure will follow 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.  The post 181 / From IC to PM: Practical Insights for Effective Leadership, with Keith Lucas appeared first on ITX Corp..

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: Readjust to the accelerated pace of product development in today’s AI-empowered world (the three-speed problem), Re-emphasize product management’s core principles that remain constant, and Reconsider what counts as essential skills for today’s product manager. 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. The post 180 / Oji Udezue: The Renaissance PM – Core Skills for Today’s AI-Driven World appeared first on ITX Corp..

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. The post 179 / Teresa Torres: Is AI Re-Prioritizing Delivery Over Discovery (Again)? appeared first on ITX Corp..

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: His 6-step process for raising the odds of making a high-quality decision. How product roadmaps reflect your team’s decision tree of what to do and when. Why strong opinions are valuable, as long as we’re open to the opinions of others. The post 178 / Phil Hornby: How To Make High-Quality Decisions That Stick appeared first on ITX Corp..

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: How her team at Netflix utilized the Balanced Portfolio Framework. The role AI can play in balancing bets that deliver user benefit and business value. Michelle’s new start-up plans for building connections and community. The post 177 / Big Bets Are Back — Why They Need a Balanced Approach, with Michelle Parsons appeared first on ITX Corp..

Axel Sooriah, product management evangelist at Atlassian who focuses on discovery and ways of working. He talks about clarifying goals to guide discovery. He explains building evidence through experiments to boost decision confidence. He shares tactics for stakeholder alignment like customer video reels and regular leadership education.

Shensi Ding is the CEO and co-founder of Merge, a unified API platform that helps companies connect existing apps and systems with AI. Integrating AI offers many benefits: enhancing performance, automating tasks, and improving decision-making. But as Shensi points out, it’s not without looming challenges. Product leaders often wonder, Are my data and systems compatible with these new tools? Is my product team well-versed in AI technology? Can my organization’s data governance framework mitigate exposure to risk? Shensi joined co-hosts Sean Murray and Dan Sharp to provide her insights in what Sean referred to as “a firehose of information and an AI integration master class.” Here’s what we learned: What Does High-Quality Data Look Like? High-quality data for AI integrations means ensuring all data is accurately synced, is normalized into consistent models across platforms, and accounts for potential edge cases. This is essential for reliable AI functionality and avoiding errors that stem from inconsistent or outdated information sources. “There’s a lot of things that can go wrong with the AI integration if you haven’t synced all your data,” Shensi explains. “You also want to make sure that it’s normalized properly. When you’re integrating with multiple platforms in a category, you need to have some kind of canonical data model that you end up normalizing it into. And that’s very difficult to do.” Integrating AI: Challenges and Roadblocks Many API providers have limitations, including outdated documentation and lack of real-time update notifications. These situations often require full data synchronization (over and over again), which leads to performance issues. Product managers need to be able to adapt to these limitations, Shensi explains. “Unfortunately,” she adds, “for each platform it becomes a little bit different, and you have to be a but hacky for how you solve it” and come up with creative, manual solutions to maintain data accuracy for AI use cases. Skills Development for Effective AI Integrations The simplest, most effective way to get comfortable with AI is to just dive in, Shensi says. “I just think the best way is just like testing it out and doing it yourself. Dive deep, actually understand how an integration might be built.” She suggests starting with hands-on testing using tools like Postman to authenticate and then explore API endpoints. Teams should experiment, set up sandboxes, add sample data, and perform load testing to build fluency with integrations and account for real-world edge cases as part of their learning process. Trends and Expectations for Widespread AI Adoption We may already be beyond the point of consumer acceptance of AI’s role in building software – these days, they expect it. Products that lack AI integration may be seen as outdated. As AI becomes normalized, both users and businesses anticipate AI-driven insights and automation as standard product features rather than optional add-ons. “I think everyone expects it now. And if it doesn’t, it’s kind of weird.” Catch the entire episode for Shensi’s thoughts on these important topics: The difference between a typical product owner and the technical product owner? Why product leaders should become comfortable with AI tools and how to use them. The new product recently launched by Merge that enables agents to safely make calls to third-party enterprise tools. Our conversation with Shensi Ding is the third of five episodes the team recorded at INDUSTRY: The Product Conference. We’ll publish the final two in-person episodes over the next few weeks, including chats with Axel Sooriah (Atlassian) and Michelle Parsons (Lex). Great insights from outstanding product leaders! The post 175 / Seamless AI Integration: Challenges and Opportunities, with Shensi Ding appeared first on ITX Corp..