Product Momentum Podcast

ITX Corp.

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.

  1. Oct 15

    How To Build AI Agents We Can Trust, with Alexis Zheng

    Alexis Zheng is Chief Product Officer at G2 and brings 20 years of experience in building AI systems. In this episode of Product Momentum – recorded in person at Mind The Product – Chicago – Alexis explains how product teams can build trustworthy AI agents without sacrificing their potential. Her message is clear: if we expect our agents to safely meet user needs and business objectives, we need to make deliberate decisions around defining success, establishing boundaries, and monitoring results. These responsibilities extend beyond engineering. Product managers and UX designers need to join their Developer counterparts to evaluate performance, draw a balance between autonomy and control, and deeply embed human judgment in the overarching product strategy. Here’s more of what we learned: Trust but Verify: Measuring AI Performance with Continuous Evaluation Trustworthy AI agents require more than promising outputs, Alexis says. Product teams need measurable criteria against which we can determine whether an agent achieves its intended objectives. Alexis itemizes three essential capabilities: evaluation metrics, guardrails, and observability. She explains: metrics establish what success looks like; guardrails prevent inappropriate actions; and observability helps teams monitor performance after deployment. “You have to design metrics so you know if the system is doing the right thing,” she continues. “Building the Harness”: Designing Agents for Flexibility and Control Evaluating AI performance is only one part of the challenge, Alexis adds; controlling agent behavior is another. Unlike traditional software, AI agents can produce different outcomes from similar inputs. Restricting them too tightly limits their capabilities, while allowing too much freedom often introduces unintended consequences. Alexis describes the infrastructure and practices needed to manage this tension as building the harness. “You have to find a way to let the model or let the agent be free, but at the same time also manage the unintended outcomes,” she says. “For those of us on product teams, this means making deliberate design decisions about where agents can operate independently and where constraints become necessary.” Apply Human Judgment to Enhance AI’s ‘Average Intelligence’ Even well-designed, carefully evaluated AI agents can’t replace the human judgment that shapes successful products. Teams using similar AI models often arrive at alarmingly similar solutions – what Alexis refers to as average intelligence – so product managers need to inject their own creativity, customer understanding, and strategic perspective to the development process. Rather than blindly allowing AI to dictate product decisions, PdMs should use it to advance a distinct vision. “To understand how to leverage AI, we need to put ourselves in a position that we are doing the steering, that we are using AI to accomplish our vision.” [06:30] How do you know that your AI agents are doing the right thing? Or that they’re delivering the desired outcome? That is what the Eval is trying to do, is to really understand what the agents are doing in production and to make sure they’re achieving the outcome as humans who actually want them to achieve; otherwise you may not end up with what you asked them to do. [12:30] Verifying the verifier. And that is really hard. That’s why today you have expert in the loop, which isn’t very common if you talk about it like RLHF (reinforcement learning from human feedback), to have a human expert to look at the result. [18:12] AI stands for artificial intelligence, but it also stands for average intelligence. The products we build today seem to look like our competitors’ products. We all seem to be regressing to the mean. The human element is what distinguishes us from all the rest. [20:46] Be intentional in how you deploy AI tools; don’t simply defer to the tool’s solutions. Agents are doing the work, but we humans still need to do the steering; otherwise, you don’t know what the agents are going to end up with. [22:19] Deterministic roadmaps vs. Probabilistic roadmaps. As we move through more probabilistic roadmaps, we begin to narrow the field of possible selections – this is known as converging. The key to AI is this: how fast we are converging? The post 198 / How To Build AI Agents We Can Trust, with Alexis Zheng appeared first on ITX Corp..

  2. Sep 30

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

    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..

  3. Sep 15

    Arpan Podduturi

    Artificial intelligence is changing how product teams work, but not what good product management requires. As Arpan Podduturi explains, this remains true regardless of whether the AI integration involves physical products or software. Product teams in both environments face remarkably similar challenges: understanding user context, balancing trust and control, and delivering business value. Leveraging core product management principles remains critical to success. Arpan Podduturi is Vice President of Product at Samsara, where he leads strategy and development of the company’s physical AI products. The Samsara product ecosystem is vast, serving back-office workers as well as truck drivers, machine operators, and front-line laborers. His perspective is practical: regardless of context, product teams still need to understand customers, identify valuable problems, prototype possible solutions, deliver measurable outcomes, and build trust. AI hasn’t changed the job; it simply accelerates parts of the cycle. Here’s what we learned: Product Discovery (Still) Starts With Customers For Samsara’s product managers, discovery means going where users actually work – including factories, mines, airstrips, and oil refineries. Their approach to discovery in these physical environments is not much different from that in traditional software discovery; Arpan refers to it as forward-deployed engineering. “Our teams talk to users in their environment, and we work with them to figure out what problems they need help need solving,” he says. “It’s not necessarily new; in fact, it’s like the oldest song in the book.” Trust Remains Essential to the Product Experience When building tools that support physical safety, user trust is a core product requirement. The impact of AI integration is still a bit of an unknown – especially from the user perspective. Where personal safety is the desired outcome, Samsara product teams have to overcome a lot of suspicion about product performance. “We sell trust,” Arpan says, “and the product is about building trust and it’s about helping people get home safely at night.” But as AI models assume much of the responsibility for the build, it’s imperative that humans are embedded throughout the product experience – from discovery through execution. AI Hasn’t Changed the Job – Just the Set of Tools We Use To Do It AI can accelerate development, but product teams still have to demonstrate that new capabilities are worth adopting. Whether using AI or not, Arpan says “product managers still need to identify value, understand the market, prototype quickly, work with engineering, tell the right story, and share the ROI with the customer. This is still the job. AI just gives us a different set of tools.” [08:15] We sell trust. Our product is about building trust, and it’s about helping people get home safely at night.    [11:18] The software we build needs to conform to the business; the business should not conform to this software. [21:48] My highest level take on the way that product management has evolved over the last year and a half is that the PM job is the job.We still need to like identify value, understand the market, like prototype quickly, work with your engineering team, be able to tell the right story, share the ROI story of the customer. You still needed to do all those things. You just have a different set of tools. [28:33] The PM job hasn’t changed: building the right thing in the right way at the right time has always been the product person’s job. And it is now more than ever. [30:11] The product side and the craft side. So on the craft side, not a lot has changed. The tools are just better and everything is going faster. On the product side, there’s a lot that we are  discovering on the agentic front. And we are going to see these kind of massive digital transformations of physical operations companies. That’s changing rapidly as people are starting to understand that AI touching the physical world is actually the biggest opportunity before us. [31:47] The curious PM can use AI to take advantage of the “product pit stops.” Decisions come with so much context now, so if you’re a curious PM you can get the answer without having to wait for the data person to pull the thing or you don’t have the meeting with the customer to talk to them and get their feedback on a specific question. The post 196 / AI Changes the Tools, Not Product Management Fundamentals, with Arpan Podduturi appeared first on ITX Corp..

  4. Aug 25

    Barry O'Reilly

    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..

  5. Aug 11

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

    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..

  6. Jul 28

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

    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..

  7. Jul 15

    David Pereira: How Product Teams Can Deliver More Value by Doing Less Work

    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. “B******t 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] B******t management is the art of doing things that create no value, but drain your energy. The more b******t 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..

  8. Jul 7

    TiPS: ITX 2026 P+D Conference – Converging Around Better Problem Solving

    For the past 5 years, the ITX Product + Design Conference stands at the top of the company’s calendar. In this Topics in Product Series (TiPS) episode, Product Momentum co-host Sean Murray called the 2-day conference event “my favorite two working days of the year,” complete with workshops, keynotes, spotlight sessions, and live podcast recordings. Sean (Director of Product Management) is joined by ITX colleagues Shannon Baird (Lead UX Designer), Kyle Psaty (VP of Business Development), and Dan Sharp (Product Manager, Product Momentum co-host). With conference excitement still fresh on their minds and drawing from their experiences across product management, UX design, and business development, the panel explored the conference’s most relevant takeaways. As you’ll see and hear, one theme emerged: the boundaries that once separated the roles of Product and UX are rapidly fading. As AI changes how teams work, their success depends less on tools and more on the collaborative problem solving that grows from a deeper understanding of our products’ users. Here’s a look at what they learned: AI Amplifies Existing Product Practices Not surprisingly, Conference speakers discussed artificial intelligence and its impacts – but not as the primary topic. Instead, they focused on ways AI can be used to strengthen good product thinking, not to replace it. David Pereira, author of Untrapping Product Teams, explained that AI is most valuable when it enhances sound discovery, user research, and decision-making. Teams that already practice strong product fundamentals will benefit the most, he added. “AI accelerates what you are already doing,” David says. Leadership Is Something You Do, Not Someone You Are Leadership emerged as another major theme during the Day 1 workshops. In a Product track session guided by Rachel Kohman, attendees explored how leadership happens through everyday actions. Rather than being defined by job title or authority, leadership is revealed at all levels of the organization by speaking up, creating space for others, and helping teams navigate difficult problems. As Dan points out, “It’s really easy to think about leadership as a thing that happens when ‘somebody’s in charge.’ But that’s not always the case. Leading is a verb that you do, not an overall overarching principle. I think there’s a lot of power in that.” Product and UX Deliver Stronger Results Together Our TiPS panelists believe that the Conference’s most actionable takeaway is the growing convergence of Product and UX. Even though AI is enabling professionals to work across traditional role boundaries, a focus on shared customer and business outcomes are bringing the disciplines even closer together. “In past years,” Kyle shares, “guests like Jesse James Garrett, Jared Spool, and Rich Mironov really celebrated our conference because it brings together product and design. Historically, the impetus has been to actively bring those departments together. Now, those roles are converging in this kind of natural way; the game board is tilting us all together toward unified problem solving and shared solutioning.” [04:11] The ITX Product + Design Conference is my favorite two working days of the year! — Sean Murray, Director of Product Management, ITX.   [13:28] I’m now building my own AI design expertise, and it’s important to stop for a beat and make sure I’m asking the right questions as I design these tools. — Shannon Baird, Lead UX Designer, ITX. [18:51] One thing is clear: the work we’re doing – designers, engineers, product managers – is figuring out what brings value. It’s adaptive problem solving, right? — Dan Sharp, Product Manager, ITX. [19:50] The Product and Design roles are converging in this natural, authentic way that is tilting us all toward unified problem solving and shared solutioning. — Kyle Psaty, VP of Business Development, ITX. [21:05] That’s why this Product + UX Conference was killer for me. With AI technology becoming readily available, we’re seeing designers coding their own prototypes and product owners designing their own solutions. Our roles are more closely aligned and offering more opportunities to cross-collaborate. — Shannon Baird, Lead UX Designer, ITX. [22:33] I think we’ve lost a little bit of the in-person career development. And we wouldn’t have been able to make the observations that we were making about the blending of these worlds. There’s so much value for both distinct role sets; the opportunity to be there in person and the opportunity to talk with other people is really unique these days. — Sean Murray, Director of Product Development, ITX. Sean Murray. Sean is the Director of Product Management at ITX. His expertise lies in aligning teams with product goals, business objectives, and user value, all while creating a fun and collaborative environment where everyone can thrive. Dan Sharp. Dan is a Product Manager at ITX focusing on enterprise level clients and technology solutions. He enjoys shaping product strategy and turning a strategy into valuable solutions. Kyle Psaty. Kyle is ITX’s VP of Business Development. In this role, Kyle is the executive sponsor of the annual ITX Product + Design Conference. Shannon Baird. Shannon is a Lead UX Designer at ITX. She thrives on solving challenging problems with a user-centered design process, taking problems through research, ideation, prototyping, and testing design phases.  The post 191 / TiPS: ITX 2026 P+D Conference – Converging Around Better Problem Solving appeared first on 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.

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