Product Momentum Podcast

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.

Product Momentum Podcast is a technology podcast hosted by Unknown Host, with 202 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Product Momentum Podcast, a technology show with 202 episodes published.

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  Episode #197

  197 / TiPS: Building Secure, Trustworthy Products in the Age of AI

  Sep 30, 202626 minS1

  Long before ChatGPT burst onto the scene, ITX shared a blog post that outlined The Seven Perspectives of Digital Quality – among them we included app and system security. How secure is your software in terms of data integrity, coding standards, and infrastructure? How well does it stand up to access breaches, threats, and other vulnerabilities? AI is helping product teams do research, design, and build software. But faster development doesn’t reduce the need for security or sound engineering judgment. In this latest Topics in Product Series (TiPS) episode, Jonathan Coupal , ITX Vice President and Director of Security, and Besong Tabenyang , ITX Developer and Data Engineer, bring hands-on security and AI development experience to this critical conversation. Perhaps Besong said it best: “AI hasn’t removed the need for software discipline. Instead, it increased the penalty for overlooking it.” Here are a few tips for how your team can use AI to accelerate product development while building secure, trustworthy products. Proceed with Caution To Protect Sensitive Information The first step in secure AI development is understanding what happens to the information we feed into the models we use, Besong says. For example, product teams should never assume an AI tool protects sensitive business or customer data. Instead, they need to understand its privacy posture, data retention, isolation, and administrative controls. Enterprise-grade tools can provide protections that public AI tools may not. As Besong puts it, “If you put confidential business information in a public AI tool, don’t assume it stays private.” Jonathan adds, “AI is kind of dumb, so you have to be really clear about how you state your requirements as you’re going into the design process. Telling the AI to include security features as part of your build cannot be left as an assumption, it has to be declared.” Shift Security Left To Save Cost, Deliver Faster Maybe you recall Product Momentum episode 131 / Shift Left: Integrating a Security Mindset Early in the SDLC – in which security expert Paul Connaghan urged product builders to include Security as part of the product’s design, well before QA or post-release. In this conversation, Jonathan points to an article referencing IBM research that shows how addressing security during design can significantly reduce both cost and delivery time. AI can also help product teams identify potential threats during discovery and design, he adds. “There’s two types of security problems in a product,” Jonathan continues. “One is engineering problems. We consider those bugs, but security design problems can’t be fixed as bugs because they’re not bugs. Sometimes, it’s a consequence of how someone could misuse a feature.” Apply Human Judgment To Preserve Engineering Discipline AI is really good at generating code, tests, documentation, and architectural recommendations – with remarkable speed. But it’s precisely that speed that makes it easier to skip the human reasoning that supports good engineering decisions. Teams spend less time identifying edge cases, validating assumptions, and evaluating trade-offs. “AI increases code output,” Besong says, “but if our review discipline doesn’t increase with it, we get faster production of unexamined work. AI didn’t remove the need for software discipline. Instead, it increased the penalty for skipping it.” Jonathan places the responsibility firmly on the shoulders of human decisionmakers: “Every time an AI recommends a particular design choice, it’s up to human judgment; and that’s where people with your experience still matter is that bringing that judgment to bear.” [04:13] AI is kind of dumb , so you have to be clear about declaring your security requirements as your work through the design process. Telling the AI to include security features as part of your build can’t be left as an assumption, it has to be declared. (Jonathan Coupal) [08:23] Public AI solutions are built mainly for convenience and broad adoption. But you have to be mindful of what type of data you’re putting in there. (Besong Tabenyang) [11:02] Security information about vulnerabilities is so black and white that the models have been trained on this. The level of expertise that you’re granted by using AI to do security work in the design phase is so good that there’s almost no reason not to use it. It gives you a superpower that you don’t get from a lot of other types of ideation assistance; the quality is so high. (Jonathan Coupal) [13:45] Security isn’t just a governance layer. It materially improves how you build because it enforces clear system boundaries, better failure modes, and more trustworthy products. (Besong Tabenyang) [17:43] It’s pretty easy to declare essential security requirements. It’s even as simple as take a security framework or the compliance rules of the company, feed them into the model as additional inputs as you’re doing the build and say, ‘in addition to taking into account these compliance or security constraints, let’s talk about how to build out this feature.’ So we don’t even have to say it out loud. (Jonathan Coupal) [18:48] AI has increased the volume of code output, but if our review discipline doesn’t increase with it, then all we get is faster production of unexamined work. AI didn’t remove the need for software discipline. It, instead it increased the penalty for skipping it. (Besong Tabenyang) Stay tuned for our next TiPS episode, as Sean and Dan invite ITX experts to explore the security implications that unfold when AI is integrated into the product itself as a feature or agent, granted rights and privileges as an actor in the system. The post 197 / TiPS: Building Secure, Trustworthy Products in the Age of AI appeared first on ITX Corp. .

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  Episode #195

  195 / Barry O’Reilly: AI Should Strengthen Product Judgment, Not Replace It

  Aug 25, 202643 minS1

  The Product Momentum team reached out to Barry O’Reilly , seeking his expertise in helping product leaders rethink how organizations make decisions, innovate, and perform in the age of artificial intelligence. After a quick review of his CV – co-founder of Nobody Studios (a global Top 10 AI venture studio) and author of Artificial Organizations: Build Better Judgment, Speed, and Results with Human and Machine Intelligence (March 2026) – and we knew we had identified the ideal guest for this episode. In this conversation with Product Momentum, Barry explains that AI’s greatest value comes not in replacing human judgment. Instead, it is in building systems that give humans greater capacity, better information, and stronger decision-making capabilities. Use AI to Bolster Human Judgment As Phil Hornby shared in episode 178, “decision-making lies at the heart of the product manager job.” AI can accelerate analysis, synthesis, and execution, Barry argues, but humans must remain responsible for the decisions themselves. AI is a powerful thinking partner that can plow through data, but it must not be the autonomous decision-maker some want it to be. “Judgment is like a muscle,” Barry continues. “If you stop using it, it starts to erode.” That erosion happens gradually. To product teams that are already overwhelmed, a polished AI response can feel good enough. That’s why Barry recommends challenging AI rather than simply accepting its first response. Ask your new partner to expose blind spots, test assumptions, and provide disconfirming perspectives. The goal is not to eliminate human judgment. It is to use AI to make that judgment more informed and effective. Turn Administrative “Grunt” Work Into Product Intelligence AI can also give product teams more time for the work they find most valuable. Barry describes a common imbalance in product organizations: teams frequently spend only a small portion of their time actually solving complex problems. The rest goes toward meetings, documentation, status tracking, and other necessary administrative work. AI can reduce that burden by turning everyday interactions into useful organizational data. “Every time I’m sitting on customer calls, I transcribe them and put them into a database,” he says. My AI tool is constantly churning through customer comments to where I’m able to extract the key problems that keep coming up.” It’s an approach that changes the role of AI from simple task automation to decision infrastructure. For product teams, these conversations become searchable sources of customer insight. Prototypes ≠ Production-Ready Products No one can argue the benefit of Gen AI’s ability to dramatically lower the effort required to turn an idea into a working prototype; it creates exciting opportunities for product teams and executives. But it also introduces risk when over-anxious teams mistake prototypes for finished software. Barry has observed teams build impressive experiences quickly, only to discover serious problems when usage increases. “It’s very easy to prototype these days; but it’s still quite hard to productionize software.” Production software still requires thoughtful architecture, performance, security, and engineering discipline. For organizations operating at enterprise scale, that distinction matters even more. Responsible product development still requires the expertise needed to make the resulting system secure, reliable, and resilient. Listen to the full conversation with Barry O’Reilly to learn how product leaders can put the human-machine partnership to work. [04:58] Think about your decision-making process. Most of us never evaluate the way we make decisions; we’re unaware of the internal algorithm that we’re using to make them.   [07:08] Machines are terrible decisionmakers. Machines are great at capturing information and synthesizing it. But they’re terrible at making decisions. That’s what humans are really, really good at. [15:02] The 80-20 rule applied. Before using these tools, I spent only 20% of my time on creative problem solving; now I’m at 40%…that’s a 100% improvement. [21:31] Judgment is like a muscle: if you stop using it, it starts to atrophy. [22:06] Ceding our agency is a road to perdition. Every time I don’t actively participate in a judgment call, I’m giving up my agency. I’m deferring my responsibility. The moment you start to give up on making the decisions is it’s a road to peridition. [31:09] It’s very easy to prototype these days; but it’s still quite hard to productionize software. The post 195 / Barry O’Reilly: AI Should Strengthen Product Judgment, Not Replace It appeared first on ITX Corp. .

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  Episode #194

  194 / Ovetta Sampson: Designing AI Products Around Human Needs – Not Just Technology

  Aug 11, 202623 minS1

  Ovetta Sampson is a design researcher, AI leader, and founder of Right AI . She previously served as VP of ML and AI Platform Design at Capital One and worked at Google and IDEO . Ovetta brought that experience to the 2026 ITX Product + Design Conference , focusing her keynote on a question that often goes overlooked: what happens when people interact with increasingly powerful machines? To help us answer that question, Ovetta offers a pair of frameworks that center on human engagement risk, responsible AI, and rethinking how product teams design AI products. Building AI responsibly requires more than better models or more sophisticated tools. Product builders must also understand the cognitive, social, cultural, and physical risks that can emerge when humans interact with technology. AI inherits many of the biases embedded in the data used to build it, Ovetta says. So organizations need to rethink how disciplines collaborate; as the lines between traditional product, design, engineering, and security silos blur, responsible AI requires organizations to redesign not only their products, but also the processes used to create them. Here’s what else we learned: Human Engagement Risk Belongs in AI Product Design Ovetta’s human engagement risk (H-E-R) framework is based on a fundamental premise: technology can harm people when designers fail to account for how humans behave around machines. The H-E-R framework identifies cognitive, social, cultural, physiological, and community risks. These risks become especially important with generative AI, where people can easily attribute human qualities to systems that do not actually possess them. As a result, AI product teams must consider psychological and cognitive outcomes alongside traditional usability concerns. Ovetta cautions: “There are real dangerous risks when we engage with machines and don’t mindfully think about the outcomes that can happen when we don’t protect humans psychologically, cognitively, physically, and physiologically.” AI Strategy Starts With Executive Leadership Responsible AI also requires leadership decisions that extend beyond individual tools or experiments, Ovetta says. Many mid-sized organizations are hesitant to adopt AI because executives are concerned about intellectual property, trust, and data leaks. Meanwhile, employees may already be integrating AI solutions without an overarching organizational strategy. It’s a disconnect that creates opportunity for leadership to establish clear priorities before adoption becomes fragmented. AI strategy starts at the top, Ovetta adds, because executives have the authority to establish the conditions under which technology gets developed and used. “Once the C-suite understands the risk to their shareholders, to their products, to their employees, to their customers, it is much easier for me to bring in the implementation of how to mitigate those risks.” Dismantling Silos Is Essential for Effective AI Development AI challenges the traditional handoff model in which designers, engineers, security, legal, compliance, and other teams work separately before passing projects along. Ovetta says AI development requires continuous cross-functional input instead. Her D-C-R framework – draft, critique, revise – organizes teams around development stages, bringing the right expertise into each phase. “Instead of saying, ‘I’m a designer’ or ‘I’m a researcher,’ or ‘I’m a product manager,’ or ‘I’m an engineer,’ we say, ‘I’m in the draft mode,’” Ovetta adds. “Each skill set in that move brings what they need to get that draft ready for critiquing, right? And so it’s something that I give to organizations and teams to try to reimagine how they actually do their jobs.” Ovetta Sampson is not arguing for less innovation with AI; instead, she’s arguing for a different definition of responsible innovation – one that embeds human consequences, executive accountability, and cross-functional collaboration into the product development process itself. [03:10] Protecting the fragility of humanity. There are a lot of things us humans engage in, especially what I call the cognitive biases, that make engaging with machines and other automated systems that make it risky for us.   [05:57] The H-E-R Framework. But what it really is, is there are five dimensions. There’s the cognitive, there’s the social, there’s cultural, there the physiological. And then the overall community risks that when humans engage with machines, that can happen. [10:08] LLMs built on ‘traumatized data sets.’ Generative AI has no moral code. It does not know truth or fact. And accuracy is not in its wheelhouse. In fact, it’s not in this training and it’s in its goals. So why when we type something into chat GPT, we expect truth back? I don’t know. [13:24] Protecting my values as a creator. That’s where I really want to start, because I don’t want to be a part of that. I don’t want to part of designing something that harms people. [14:00] I observed one reoccurring truth. Whatever is happening in the basement of a company starts in the C-suite. So if there is sexism, if there’s homophobia, if there racism, if there is bad culture, if it starts at the C-suite. Because the C-suite is the person who has the ultimate authority about what occurs in every floor of an organization. [18:26] The D-C-R framework — draft, critique, revise. The D-C-R framework is something I created because I was trying to explain to designers and product and engineers how their processes would change when they’re designing for and with AI. Each one of these disciplines should go through that makes the handoffs more like a circular iterative. The post 194 / Ovetta Sampson: Designing AI Products Around Human Needs – Not Just Technology appeared first on ITX Corp. .

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  Episode #193

  193 / How Adaptive Leadership Helps Product Teams Navigate Complexity, with Rachel Kohman

  Jul 28, 202623 minS1

  In our current environment of rapidly changing customer needs and expectations, evolving technologies, and unpredictable market swings, the best ideas rarely come from a single leader. Instead, as attendees of the 2026 ITX Product + Design Conference learned, they emerge when product teams collaborate, challenge assumptions, and adapt together. In this episode of Product Momentum, Rachel Kohman, Ed.D. joined Sean and Dan for an in-person chat, discussing why adaptive leadership helps organizations solve today’s toughest product and technology problems. Rachel, Assistant Dean of Entrepreneurship Education at Missouri S&T , draws from her experience developing future engineers, entrepreneurs, and leaders, to offer practical guidance for creating teams where everyone contributes to better decisions. Here’s what we learned: Leadership Is Everyone’s Responsibility Adaptive leadership challenges the long-held assumption that the person with authority has all the answers. Today’s problems are too complex for any one individual to solve alone, Rachel says. Product managers, UX designers, engineers, and researchers each bring their own unique perspectives to deliver desired outcomes. Not surprisingly, she adds, organizational performance improves when leadership becomes a shared responsibility. “And that’s what’s so interesting with adaptive leadership; it actually starts to change the narrative. “It’s like, ‘wait a second, the challenges we’re facing are too complex to have a single right solution.’” For product teams, the adaptive leadership mindset creates an environment where product team members contribute new ideas because they see opportunities – not because they’ve been directed to. Psychological Safety Drives Better Decision Making Echoing the sentiments of episode 78 guest Dr. Timothy Clark , Rachel says that too many good ideas remain trapped in hallway discussions or private chat messages, mostly because team members are hesitant to challenge authority. Psychological safety, what Dr. Clark refers to as the great enabler, requires people in positions of authority to model and reward everyday acts of vulnerability. Beginning with respectful communication (as opposed to public confrontation), team members should present their concerns early on with an eye toward improving the product instead of proving someone wrong. “It’s that curiosity,” Rachel continues, “it’s asking those questions of others and being willing to not have all the answers yourself.” This approach is not without risk, but its benefits are every bit as real: teams surface issues sooner, discovery improves, and teams emerge with better decisions before costly mistakes reach customers. Take a Step Back To See the Whole System Adaptive leadership also requires “stepping away” from daily execution, Rachel says. She describes the practice as “going to the balcony” – intentionally creating distance to recognize broader patterns instead of reacting only to immediate tasks. Teams who cannot see a world beyond the sprint work, feature requests, tech debt, and deadlines fail to observe market trends, learn from adjacent teams, and become aware of changes across the organization. “If you don’t take the opportunity to take a step back and pause the noise, you’re losing out on an opportunity to perceive the world around you.” Leadership Is Every Team Member’s Responsibility Adaptive leadership is not just another management framework; it’s a practical way of helping organizations respond to uncertainty. Grounded in psychological safety, product teams become more resilient when people feel comfortable sharing new ideas, questioning assumptions, and learning together. Rachel Kohman, in her own words: [03:35] Changing the narrative of leader as ‘only the person in authority.’ The term leader has actually been the noun synonymous with the person in the top spots, right? And so what’s interesting with adaptive leadership is that it actually starts to kind of change that narrative and say, ‘wait a second, the challenges we’re facing do not have like a single right solution.’   [04:38] Not for the risk-averse. It is a little bit risky for everyone; we’re asking people who are in a position of authority to become comfortable with ideas coming from others, and they actually need to be willing to listen. At the same time though, the people who aren’t in the position of authority have to be willing to step up and not just say, ‘I’ll do whatever the boss says.’ [09:53] Adaptive leadership can reduce our volume of work. I do think that as we start to integrate it into our work, it becomes part of that culture of everybody is bringing their perspective and their expertise; it actually eases up on the amount of work that you have to do. [11:20] Entrepreneurship requires curiosity. I love when different schools of thought come together. So I don’t have to be an ardent follower of only one thing. So with entrepreneurship there’s also this idea of ‘where’s your curiosity?’ And with leadership I think it’s the same way. It’s that curiosity, it’s asking those questions of other people and being willing to not have all the answers yourself. [17:29] The adaptability of adaptive leadership. I have found ways to utilize it all across my life. I practice this in my home, in my work, with my friends, which I think is really cool. Yeah, my next book is actually how being a parent helps you be a better leader, not the other way around. It’s amazing! [18:13] The view from the balcony. Sometimes we need to step back a bit, away from the busy-ness and noise of the day-to-day tasks. For illustration: if you’ve ever seen a marching band, like on a football field, but you’re actually down on the field at the same level as the band. It can look just really busy and there’s a lot happening. But when you move way up to the top of the stands, suddenly you’re seeing what is evolving in these beautiful intricate movements. Gives you a whole different like vantage point, right? The post 193 / How Adaptive Leadership Helps Product Teams Navigate Complexity, with Rachel Kohman appeared first on ITX Corp. .

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  Episode #192

  192 / David Pereira: How Product Teams Can Deliver More Value By Doing Less Work

  Jul 15, 202623 minS1

  Product teams often struggle to deliver meaningful outcomes. Why? Because they confuse activity with progress. Understanding that challenge is the focus of our chat with David Pereira , recorded in person at the 2026 ITX Product + Design Conference , in Rochester, NY. David is a product management coach, speaker, and author of Untrapping Product Teams: Simplify the Complexity of Creating Digital Products . He has spent that past 17 years helping organizations create more value by eliminating busy work . In his conference workshop and keynote, David drew from his work with product teams around the world, explaining how eliminating unnecessary work, validating assumptions early, and staying close to customers enable teams to make better decisions and build products that solve real problems. Eliminate Busy Work to Create More Value Many organizations unintentionally create systems that reward activity instead of outcomes, David argues. Excessive meetings, backlog maintenance, and decision making based on conjecture rather than evidence consume valuable time without moving products forward. Rather than accepting these practices as status quo, David believes that product leaders should ask whether these activities contribute to customer or business value. Set clear boundaries around focus time and make every meeting earn its place on the calendar, he adds. “Bullshit management is an art,” Davids says. “It’s the art of doing things that create no value, but drain everyone’s energy.” Build To Learn: Testing Assumptions Before Crafting Solutions David’s central message in his day 1 conference workshop is that successful product teams learn before they build. Teams naturally become attached to their ideas, making it easy to seek evidence that confirms existing beliefs instead of challenging them. By identifying assumptions, prioritizing the most critical risks, and defining success criteria before running experiments, teams can quickly separate fact from opinion. AI can accelerate experiment design, but only when it supports disciplined learning – not when it replaces critical thinking. “You will build to learn, because you’ll learn from reality,” David continues. “If you don’t do that, you’re gonna make something that only works in fantasy.” Customer Reality Should Drive Every Product Decision In a return to timeless product principles, David reinforces the mantra that decisions should be grounded in real customer behavior rather than internal assumptions. Using examples drawn from his own work, David reveals customer friction that internal discussions had completely overlooked. For product managers and UX professionals alike, direct observation remains among the most effective ways to uncover opportunities that analytics and meetings often miss. “You need to go and check the users’ reality,” David says. “Because if you limit your discovery to the clinical environment of your cozy office, you’ll quickly learn that that is not how users work. You need to get out there to truly understand their reality.” [03:29] Bullshit management is the art of doing things that create no value, but drain your energy. The more bullshit you do, the less value you create.   [04:47] [As a product manager,] your job is to simplify what others complicate. So many times we have meanings because it’s the default way of collaborating. So we need to step back and say, what on earth am I trying to achieve? [05:51] We all need time for focus. So I block my morning; nobody can reach out to me in the morning because it’s the time I’m going to do deep work. As for meetings, they need to have a few things: what is the objective? What is the context? And what is agenda? If the meeting invitation can’t answer those questions, I immediately reject. [09:56] In order to test our assumptions, we need to understand the following: If my assumption is proven false, is my idea still valid? If yes, you can ignore this assumption because your idea will still survive. But if the assumption is proven false, my idea is worthless and I start looking at things differently. [17:05] When [AI-driven] execution becomes cheap, where do you put your energy? Your energy goes to thinking, decision making. But thinking and decision making should be a collaborative exercise, not a siloed one. [20:49] What is NIHITO? NIHITO is ‘nothing important happens in the office.’ It’s an important lesson about how teams and organizations make product decisions. We need to get out of the office and into our users’ environment if we are ever to understand their reality. The post 192 / David Pereira: How Product Teams Can Deliver More Value By Doing Less Work appeared first on ITX Corp. .

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