How I Tested That

SummaryIn this episode I’m joined by Bobby Moesta, founder of The Re-Wired Group and the co-creator of Jobs to Be Done.I’ve been a fan of Bobby’s work for years and instead of deep diving on yet another jobs to be done conversation with him, we explore a topic he doesn’t get to chat about very often, which is how systems thinking changes the way we experiment, and why understanding trade offs is essential to making better decisions. We chat about prototyping to learn and prototyping to verify, and it draws on lessons from his mentors, including luminaries such as Deming, Taguchi, and Christensen.We discuss what happened to the R in R&D, and we wrap up with a fascinating potato chip experiment that reveals the gap between what customers say and what customers do.If you are a fan of system thinking and experimentation, then you’ll love this episode.TakeawaysPrototype to learn, not just to verify. Prototyping should help uncover what you don't know, rather than simply confirm what you already believe.Measure outcomes, not just outputs. Start with the progress customers are trying to make, then work backward to design the systems and measurements that matter.Great decisions come from understanding trade-offs. There is rarely one optimal solution. Testing helps reveal what you're willing to give up to achieve what matters most across desirability, viability, and feasibility.Tackle the biggest unknowns before building. Teams often rush into execution because it's easier to work on what they know. Addressing critical unknowns early prevents costly rework later.Observe what people do, not just what they say. Surveys and stated preferences can be misleading. Bobby's potato chip experiment showed how actual consumption and choices can provide stronger evidence than asking people what they like.Guest LinksThe Re-Wired Group: https://therewiredgroup.com/LinkedIn: https://www.linkedin.com/in/bobmoesta/

SummaryIn this episode, I’m joined by Anna Burch from SmartMouth, a family owned oral care company founded by her grandfather, Dr. Marvin Cohen.What started with a dentist questioning the root cause of bad breath eventually became a nationally distributed consumer product, but having a scientifically proven solution doesn't necessarily mean customers will understand it, buy it, or even know how to use it.Anna and I explore the challenges of testing your product design with customers, expanding into new markets, and knowing when to say no to opportunities that could undermine what makes your value proposition unique.If you want to learn more about solving root problems instead of symptoms, and balancing testing with what makes your product special, you'll love this episode.TakeawaysSolve the root cause, not just the symptoms. Dr. Marvin Cohen challenged conventional assumptions about bad breath, researching the underlying science instead of simply masking odors.Innovation doesn't mean abandoning what makes you different. SmartMouth has maintained its core activated zinc technology for decades, even resisting opportunities to create cheaper products that would compromise its differentiation.A great product can still have usability problems. SmartMouth's dual-solution technology requires an unconventional bottle design. Customer feedback revealed that even loyal customers struggled with the experience, leading to improvements in packaging.Understand the emotional and social jobs customers are hiring your product to do. Fresh breath is the functional benefit, but confidence, self-esteem, and social connection can be just as important to customers.Your go-to-market strategy must evolve with the market. SmartMouth grew from dental offices to QVC and mass retail, eventually embracing e-commerce and creator-led content to educate customers about a product that requires more explanation than traditional advertising allows.Guest LinksSmartMouth: https://smartmouth.com/

SummaryIn this episode, I’m joined by Melissa Perri. You may know her best as the author of Escaping the Build Trap and I believe she is one of the brightest minds in modern product management. I’ve been privileged to know Melissa since the early days of the Lean Startup movement and I was thrilled to chat with her about what has changed in product and testing over the past 15 years. We talk about the dangers of PM’s outsourcing their critical thinking to AI, and why experimentation with good data is maybe now more important than ever.Melissa details out how AI fundamentally transforms product operations and how companies can test these new found capabilities and actually build things customers need.If you want to learn how to make better product decisions, accelerate learning and avoid this latest AI version of the build trap, then you'll love this episode.TakeawaysAI is accelerating the build trap. Companies can build software faster and cheaper than ever, but without clear strategy and customer understanding, they're simply building the wrong things at unprecedented speed.Don't outsource judgment to AI. AI can generate specifications, analyze data, and accelerate execution, but experienced product leaders are still needed to challenge assumptions, interpret evidence, and determine what should actually be built.Better data doesn't automatically mean better decisions. Organizations struggle with fragmented systems, conflicting metrics, and unreliable information. AI creates enormous opportunities to improve decision-making, but only when teams can access and trust the underlying data.Product operations could be supercharged by AI. From synthesizing customer research to interrogating business performance, AI can dramatically reduce the time required to generate insights. Product Ops can help connect these capabilities to strategy, governance, and measurable business outcomes.AI won't fix broken organizational systems. Functional silos, disconnected strategies, rigid funding cycles, and competing departmental goals remain major obstacles. Without addressing these systemic issues, organizations risk using AI to make dysfunctional processes run faster.Guest LinksLinkedIn: https://www.linkedin.com/in/melissajeanperri/Product Institute: https://productinstitute.com/

SummaryIn this episode, I’m joined by Moses Adedoyin. Moses is leading Venture Design & Innovation over at GuideWell, where he’s testing new healthcare solutions to help patients.We discuss what testing looks like when you work in one of the most heavily regulated environments imaginable. Moses shares how his background in engineering and simulation helped shape the way he approaches testing and how his team built a lab where they could test new ideas safely before introducing them into a live healthcare environment.We deep dive into the real world practicality of using synthetic data to increase the fidelity of experiments and how that can dramatically reduce the time and cost required to evaluate new solutions. Moses and I explore digital twins, AI-assisted simulation, and how technology enables healthcare teams to spend less time on administrative work and more time actually helping patients in need.If you want to learn more about testing in highly regulated healthcare, using AI and synthetic data, then you’ll love this episode.TakeawaysTesting in regulated environments requires a different path. In healthcare, teams can’t always “just talk to customers” or run a quick experiment because PHI, consent, security, procurement, and regulation all shape what is possible.Synthetic data can help teams build evidence earlier. Moses’ team uses synthetic data to test concepts, technology, and early value before involving real patient data, which helps reduce both cost and cycle time.Increase data fidelity as the evidence gets stronger. Their progression moves from little or no data, to synthetic data, to de-identified data, and eventually to real PHI only when the experiment justifies it. The goal is faster, higher-quality decisions, not just faster experiments. Synthetic data can help eliminate weak technologies or partners before teams spend months navigating legal, security, and procurement requirements.Synthetic data does not replace real patients. It can work well for feasibility, proof of technology, and early proof of value, but real people are still essential when testing behavior, trust, experience, or clinical outcomes.A safe sandbox makes experimentation possible. Creating an environment outside the core production system allowed Moses’ team to fail faster, learn safely, and generate evidence before asking the organization to make larger investments.AI should free people to do more of the work only humans can do. In healthcare, Moses sees the opportunity in automating administrative work so clinicians and care managers can spend more time directly helping patients.Guest LinksLinkedIn: https://www.linkedin.com/in/mosesadedoyin/

SummaryIn this episode, I’m joined by Martin Eriksson, founder of Mind the Product and author of The Decision Stack, to talk about how organizations can make better decisions from strategy all the way down to everyday execution.We talk about the difference between speed and velocity, why empowered teams still need strategic context, and how assumptions exist at every layer of an organization, not just inside individual products.We also explore what AI changes and, maybe even more importantly, what it doesn’t. Building has become drastically faster and cheaper, but that doesn’t eliminate the need to understand the customer, the business, and whether or not you should be building something in the first place.If you want to learn more about how strategic choices connect to everyday decisions and how assumptions can be tested throughout your organization you’ll love this episode.TakeawaysSpeed without direction is not enough. Teams can build, ship, and test faster than ever, but without strategic alignment they may simply move in different directions more quickly. Martin frames this as the difference between speed, velocity, and organizational momentum.Empowered teams still need strategic context. Autonomy works best when teams understand where the business is going, how it plans to get there, what matters now, and how their work connects to those choices.Assumptions exist at every layer of the organization. They are not limited to products or experiments. Vision, strategy, objectives, opportunities, and execution all rest on assumptions that can become dangerous when they remain unspoken.Strategy is a series of connected choices. The value of a decision stack is not just having vision, strategy, objectives, opportunities, and principles—it is making sure those decisions connect from the top down and can be traced back from everyday work.Use testing and discovery on strategic decisions, not only product decisions. Teams have become good at de-risking what is directly in front of them, but the same tools can be applied to bigger bets around strategy, markets, and the future of the business.Not every assumption can be eliminated, but it should be named. At the strategy and vision level, some choices will always remain bets on the future. Making those assumptions explicit allows leaders and boards to monitor when conditions change and decide whether the risk is still acceptable.AI changes the economics of building, not the fundamentals of product. Faster and cheaper delivery reduces feasibility risk, but teams still need to understand customer value, business value, usability, distribution, pricing, and whether something should be built at all.Guest LinksMartin’s Website: https://martineriksson.com/Martin’s LinkedIn: https://www.linkedin.com/in/martineriksson/Coherent & Wrong: https://www.thedecisionstack.com/coherent-and-wrong/