Product Momentum Podcast

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.”

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.

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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.”

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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.

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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.

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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:

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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.”

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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.”

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. 

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Product leaders often measure success in terms of retention, revenue, and growth. In this episode of Product Momentum, Nesrine Changuel explains that the most successful products achieve those outcomes by creating meaningful emotional connections with users. Nesrine’s experience includes leadership roles at Skype, Spotify, Google Meet, and Google Chrome. But these days, she is perhaps best known for her book, Product Delight.

Nesrine will explore these and other topics at the ITX Product + Design Conference, June 24-25 in Rochester NY. On conference Day 1, she will conduct a workshop that delves into her Product Delight framework, guiding attendees on how to design products that users will not only use – but will come to love, remember, and recommend to others.

During this conversation, Nesrine discusses what she has learned from product teams since the book’s release and explains why delight is not simply a design tactic, but a business strategy that transcends industries.

Product Delight Extends Beyond Design

One of Nesrine’s biggest discoveries during her global book tour was the widespread misunderstanding of what product delight actually means. Many practitioners associate delight with visual design, animations, or clever interface surprises. This view is too narrow, she says. Nesrine defines delight as the overall feeling users experience while interacting with a product.

“Product delight is the entire experience,” she adds. “It’s actually the feeling that the user enjoys while using the product.” It extends well beyond aesthetics to focus on creating experiences that improve how users feel while accomplishing their goals.

Emotional Connection Creates Measurable Outcomes

Among Nesrine’s key challenges was helping product and business leaders recognize the fact that user emotions directly influence business performance. Emotional design has long been familiar to designers and marketers, but many product organizations still prioritize operational metrics above customer sentiment.

Nesrine was able to connect the dots between delight and business performance by demonstrating that “the delight itself can actually double retention, referral and revenues.” By framing emotional connection through the lens of retention, referrals, and revenue growth, she demonstrated that delight is not a soft concept. Rather, it is a practical approach that can strengthen both customer loyalty and business results.

Adding Delight to Your AI Product – Emphasize the Human Aspect

On conference day 2 – Keynote Day – attendees will be treated to brand new content from Nesrine: how to add delight into your AI product.

“I’ve been very quiet about AI over the last couple of months,” Nesrine shared. “I’ve always believed in the humanization of product and the human aspect, but now the product is the conversation. So how can we add delight in these kinds of experiences? That’s something new that I’m going to be talking about” at the ITX Product + Design Conference. As product teams continue navigating AI-driven technological change, core product principles remain unchanged: products that make people feel understood, respected, and supported are more likely to earn user trust, loyalty, advocacy – along with long-term success.

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Mike Belsito has spent years at the center of the product management community. As the founder of Product Collective, a leader at Mind the Product, and now Head of Product Evangelism at Pendo, Mike has built a career around learning from product professionals and sharing those insights with the broader industry.

In this episode of Product Momentum, Mike joins Sean and Dan for a discussion that is absolutely top of mind for today’s product leaders today: while artificial intelligence is transforming how products are built, timeless skills such as curiosity, judgment, and taste remain essential. In fact, he argues, these capabilities will become even more valuable as technology accelerates the pace of product development.

Navigating AI Through Human-Centered Product Skills

In doing research for a new book, Mike engaged many product leaders who had experienced previous periods of technological disruption – e.g., the rise of the internet, telecommunications, and mobile computing. Now dealing with AI-driven opportunities and uncertainties, many leaders point to the same enduring qualities that helped them and their teams adapt during earlier transitions.

Rather than focusing solely on new technologies, they emphasized the importance of human-centered skills that guide decision-making and product strategy. “It’s kind of relying on the same timeless characteristics that we’ve always thought were important,” Mike says. “And even today, we still think are important, which are things like curiosity, judgment, taste.”

Balancing Output and Outcomes

Our conversation with Mike also explored a growing tension within product organizations – a theme also covered in recent Product Momentum episodes. As AI enables teams to create more content, code, and functionality faster than ever before, Mike cautions against using increased output” as a measure of success.

Product teams have spent years shifting their focus from user satisfaction to delivery metrics to business outcomes, Mike continues. “That mindset remains critical, even as AI changes workflows. But how do we make sure that it’s not just about the output – that we’re actually building the right things?” For product managers, designers, and engineers, the challenge is ensuring that speed does not come at the expense of delivering business value.

Curiosity as a Practiced Skill

Among Mike’s more surprising research discoveries was how often leaders highlighted curiosity as a skill that can be developed intentionally. Rather than viewing curiosity as an innate personality trait, many described it as a practice that strengthens through deliberate effort. It’s an insight that brings important implications for today’s product teams.

Learning, questioning assumptions, and seeking new perspectives become competitive advantages in times like these when the technology landscape evolves so quickly, Mike adds. “I wasn’t thinking of curiosity as a practice or as a muscle to be flexed.”

As AI continues to reshape product development, Mike offers a practical perspective for product leaders. Technology will continue to evolve, he says, but the ability to ask thoughtful questions, exercise sound judgment, and focus on meaningful outcomes remains fundamental. Those timeless capabilities may ultimately determine which teams are best equipped to thrive in an increasingly AI-driven future.

Want to hear more from Mike Belsito? Be sure to join us as he emcees the 2026 ITX Product + Design Conference, June 24 & 25 in Rochester, NY – for the fifth consecutive year!

“I’m honored to return as emcee for the fifth year in a row. This event continues to stand out because of the incredible community it brings together and the energy in the room each time we gather. I’m proud to be part of something that keeps growing in impact and connection.” – Mike Belsito

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Prerna Singh helps organizations build better products and stronger communities. As the founder of Scrappy to Scale Advisors and the former VP of Product and Design at Meetup, she has guided startups and mission-driven organizations through rapid change, customer discovery, and product strategy.

In today’s episode, Prerna explains why human connection and disciplined product thinking matter more than ever during the AI boom. While AI may accelerate product work, she says, successful teams avoid the must stay grounded in curiosity, customer insight, and authentic community building.

Sense of Community Addresses the ‘Isolation Problem’

What started as a casual gathering for fractional product leaders, Prerna’s Product Breakfasts quickly evolved into a broader support system for people navigating uncertainty and AI-driven change and the professional isolation that often comes with it. Many product professionals, she says, now feel overwhelmed by the pace of technological advancement and the pressure to keep pace.

“I don’t think there’s any catching up,” Prerna adds. “‘Catching up’ implies that there’s an end goal to this. And there isn’t. So that’s where I think Breakfast is the evolution of people coming together to share what they know and helping reduce that anxiety that isn’t just a knowledge gap. It’s also an isolation problem.”

First Principles, Supported by Human Interaction

Product professionals need environments where they can safely discuss their own vulnerabilities. The ability to openly admit uncertainty about AI and its impact, to exchange ideas, and learn together is the hallmark of authentic, in-person interaction.

“The IRL connection isn’t going anywhere,” Prerna continues. “We need that human-to-human interaction to have an outlet for where those vulnerabilities are gonna go. Otherwise, they’re just contained within us and we’re just spiraling in our own heads.”

Avoiding the Trap Starts with Better Discovery

Prerna’s extensive background in user research informs her belief in the importance of first principles in product management. AI tools, she says, make it deceptively easy to jump directly into solutioning without fully understanding the customer’s needs and the business’ problems. In her fractional product manager role, Prerna listens “for the thing that clients return to when they stop performing.”

“‘We need AI’ is a common mantra,” she says. “But what’s interesting for me is the kernel of truth that frames that statement. And it’s not what they want. It’s what they can’t circle back to – like there’s a hidden customer insight that we’ve maybe navigated around.”

Lean into Discovery, Prerna concludes. Product teams must remain disciplined about validating assumptions, conducting research, and identifying the real customer need before building anything. “Avoid the trap of jumping into solutioning.”

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