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Product Momentum Podcast

Product Momentum Podcast

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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. Economie Management Management et direction
Épisodes
  • 199 / Alicia Dixon: AI Is Changing Product Management; Why Fundamentals Still Matter

    199 / Alicia Dixon: AI Is Changing Product Management; Why Fundamentals Still Matter
    Oct 27 2026
    AI’s place in product management may be well established, Alicia Dixon says, but it has not replaced the core principles that continue to serve product leaders and their teams. Alicia is Director of Product Management at Marriott and has held similar roles at Walmart and Dell. In this episode, she brings her experience to our in-person conversation at Mind the Product Conference in Chicago. Alicia explains that even as AI adoption accelerates, product managers must continue prioritizing customer needs, business outcomes, and critical thinking. By clarifying product strategy and constructively challenging assumptions, she adds, PdMs can continue to demonstrate the value of their work. Here’s what else we learned: Product Management Fundamentals Start With Customers AI is creating new opportunities for product development, but effective product management still starts with understanding customer needs and addressing the challenges that hinder their growth. “PMs need to still do the same fundamentals,” Alicia says, “but they also need to make sure they’re communicating to everybody who will listen: ‘Here’s what I’ve learned. Here’s who I’ve talked to. Here’s what it means. Here’s the outcome that we’ve gotten so far. Here’s where we still want to go.’” Product Strategy Requires Clarity and Communication When organizational priorities are unclear – or you’re presented with a “Do AI” imperative from above – product managers should take the initiative to clarify strategy, align their work with business objectives, and understand the role AI can play in the solution. Alicia first recommends developing a hypothesis about leadership’s intended direction, and then seeking clarification through a constructive conversation. “Share your ‘here’s what I think you said’ hypothesis, and let them course correct. Don’t go in and just say, ‘I don’t understand this, explain it to me.’ … And if they can’t give you an answer, then you’ve got a bigger problem. But you can’t just say, ‘No, you’re wrong. I’m doing this instead.’ That’s a good way to get fired.” AI-Powered Product Development Still Needs Critical Thinking There is no doubt that AI can help product teams accelerate experimentation, evaluate alternatives, and explore possible solutions. But faster development does not guarantee better outcomes; if you solve for the wrong problem, it doesn’t matter how fast you build the solution. The ace up the product manager’s sleeve can be found in the application of critical thinking, the exercise sound judgment, and keeping customer needs at the center of experimentation. “When I’m evaluating my teams’ performance, I’m still looking for the critical thinking and the nuanced judgment and the thought of ‘here’s the problem we’re trying to solve and here’s some options the AI is giving me to try to solve them.’” This episode with Alicia marks her second visit to Product Momentum; want more from Alicia Dixon? Check out our episode 41, No Such Thing as the Perfect Product Manager. [08:54] Do the work quietly, but then be sure to communicate what you’ve learned, the value you’ve created, why it’s important, and where we still want to go. [11:29] When you hit your objectives, celebrate those wins! Make sure everybody feels valuable because the ‘keeping-the-lights-on-work’ is what brings in the money. [12:24] In this current environment, people are offloading the responsibility of developing a sound strategy, to AI. That’s how teams end up with AI slop. [15:15] When I’m looking for product leaders, I’m still looking for the critical thinking that says, ‘here’s the problem we’re trying to solve; these are the options AI is providing to try to solve them.’ [17:56] I see a bit of AI resistance on the horizon: customers who want product builders to return to the artistry of something that’s been ‘handcrafted.’ [19:57] Most leaders welcome someone who has done the researchand presents a new way of looking at a problem; that initiative will position you well. The post 199 / Alicia Dixon: AI Is Changing Product Management; Why Fundamentals Still Matter appeared first on ITX Corp..
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    21 min
  • 198 / How To Build AI Agents We Can Trust, with Alexis Zheng

    198 / How To Build AI Agents We Can Trust, with Alexis Zheng
    Oct 15 2026
    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..
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    27 min
  • 197 / TiPS: Building Secure, Trustworthy Products in the Age of AI

    197 / TiPS: Building Secure, Trustworthy Products in the Age of AI
    Sep 30 2026
    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 ...
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    26 min
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