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Product Momentum Podcast

Product Momentum Podcast

Written by: ITX Corp.
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 Amazing digital experiences don’t just happen. They are purposefully created by artists and engineers, who strategically and creatively get to know the problem, configure a solution, and maneuver through the various dynamics, hurdles, and technicalities to make it a reality. Hosts Sean and Paul will discuss various elements that go into creating and managing software products, from building user personas to designing for trackable success. No topic is off-limits if it helps inspire and build an amazing digital experience for users – and a product people actually want. Economics Management Management & Leadership
Episodes
  • 187 / AI Native: Reimagining Product Roles and Development Cycles, with Adam Creeger

    187 / AI Native: Reimagining Product Roles and Development Cycles, with Adam Creeger
    May 13 2026
    Adam Creeger is the CTO of Slate and creator of iLoom (pronounced “il-LOOM”). His leadership experience at Meta, Greenhouse, and Frame.io not only informs Slate’s transformation into an AI-native organization, but also shapes the way AI influences product strategy, engineering workflows, and operational models. Throughout his conversation with Sean and Dan, Adam argues that becoming AI native is not about layering AI features onto existing products. Instead, it requires companies to rethink how software is designed, built, and operated – from the ground up. His perspective offers a practical framework for product leaders navigating AI-driven transformation. Here’s what else we learned: ‘AI Native’ Requires Organizational Reinvention AI native organizations are willing to rethink every layer of their business, Adam says. Rather than adding AI features superficially, AI native organizations redesign workflows, team structures, and customer experiences around AI capabilities. He emphasized that AI transformation changes not only products, but also how people contribute inside organizations. “To be AI native requires this deep exercise in re-imagination and not just imagination,” Adam continues. “In an AI native company – from the day-to-day operations to the ‘who does what’ – the roles and the owners of things are going to look very different.” AI is expanding participation across teams, enabling designers, support teams, and non-engineers to contribute directly to product delivery. That shift signals a major change for modern software organizations. AI and the Future of the Software Development Life Cycle (SDLC) Our conversation then turned to an exploration of how AI is already changing the traditional software development lifecycle. Years ago, Agile development emerged because humans had historically struggled to fully reason through complex systems before implementation. “I’ve realized that Agile was really a mitigation of a few things, mostly that we humans are limited in our abilities to reason through abstract concepts,” Adam says. “So when we thought about a software project, we didn’t have the ability to see around corners and understand the problems we’d face – until it was real, until you really started playing with it. Turns out that many of those challenges are very solvable by AI, allowing us to go much deeper into the problem space without ever writing a line of code. In addition, AI-assisted planning allows teams to revisit some waterfall-style thinking, but with dramatically faster iteration and validation cycles. Product Managers’ New Role: Communicate Context Importantly, AI is actually elevating the role of product managers, Adam offers. Rather than acting primarily as tactical decision-makers, product leaders can (and should) focus on providing context that enables teams to make informed decisions independently. “More than ever, the product manager has become a role about providing context,” he adds. “PMs should be elevated to a much more strategic role, understanding the long-term vision and helping to translate that to engineers.” Adam also feels that PMs should be using AI to communicate ideas about the product vision much more effectively. That evolution creates a faster and more collaborative product environment. Teams can evaluate real implementations earlier, gather customer feedback sooner, and align around outcomes instead of specifications alone. [05:54] What it means to be ‘AI native’. Conceptually, it’s same as digital native from when the internet was born many years ago. In the abstract sense, I see AI native being about the folks and the companies that are either just starting in the age of AI where everything they do is shaped by the existence of AI and their ability to use AI. [15:08] Is waterfall making a comeback? Oh man, this is one of my favorite topics. Growing up in the industry, waterfall was always like the evil thing. But with AI-assisted coding or agentic coding, you can go really deep, create a much bigger scope, and deliver it much more quickly…and it resembles more of a waterfall mentality. [21:51] The PM’s primary role: providing context. The product manager more than ever has become a role about providing context. The most powerful thing PMs can do in an organization is provide context to other people. [25:49] Exploring Adam’s iloom tool, and how it can help. Hear a quick story from Adam about how he used his iloom tool to create — and demo — a new product feature during a call with his customer success team. [28:47] Swarms. What are they, and how do they work? A swarm is a number of AI agents working together in a very collaborative way with the potential of real-time communication between them. [35:03] Avoiding ‘AI slop’ to defend and elevate a brand’s quality bar. Slate is creating a tool that makes it very difficult to create AI slop. This is a valuable proposition to...
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    45 mins
  • 186 / TiPS: AI-Enabled First Principles + Core Product Skills Spark Adoption

    186 / TiPS: AI-Enabled First Principles + Core Product Skills Spark Adoption
    Apr 29 2026

    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.

    The post 186 / TiPS: AI-Enabled First Principles + Core Product Skills Spark Adoption appeared first on ITX Corp..
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    24 mins
  • 185 / Confronting Cognitive Bias in AI Models, with John Haggerty

    185 / Confronting Cognitive Bias in AI Models, with John Haggerty
    Apr 23 2026
    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: [06:50] AI is compressing execution time, allowing us to automate some of the tasks that we do as product professionals: cognitive thinking, reasoning, creativity. [10:22] There’re lots of really good AI tools out there right now, but what there isn’t out there is anything that tests the fairness of our decisionmaking. [16:04] Great. You’ve used AI to improve productivity by 20%. But what happens when that breaks? What if there’s bias and heuristics in these LLMs. Who’s catching that? [17:55] Critical AI systems have the same blind spots, the same bad habits, that we as humans have. And why not? They’re built off of the flawed content we created. [21:41] I don’t think a LLM could ever get depressed. But we have standard behavioral assessments that we could administer to an LLM — to find out where it falls with these biases and with the decision-making process it’s using. [27:40] As humans, we’re make mistakes. Because AIs are built on what we know, those same mistakes are being repeated. Now we have AI learning from AI, and those mistakes are being amplified. [30:59] The ‘why’ will always need to come from a human. At the end of it all, that’s what Product is. The post 185 / Confronting Cognitive Bias in AI Models, with John Haggerty appeared first on ITX Corp..
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    34 mins
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