How I Tested That

Hosted by David J Bland

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Oct 2026

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Testing your ideas against reality can be challenging. Not everything will go as planned. It’s about keeping an open mind, having a clear hypothesis and running multiple tests to see if you have enough directional evidence to keep going. This is the How I Tested That Podcast, where David J Bland connects with entrepreneurs and innovators who had the courage to test their ideas with real people, in the market, with sometimes surprising results. Join us as we explore the ups and downs of experimentation… together.

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October 14, 2026Episode 5947 min

Melissa Perri | How I Test for the AI Build Trap

Summary In 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. Takeaways AI 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 Links LinkedIn: https://www.linkedin.com/in/melissajeanperri/ Product Institute: https://productinstitute.com/

September 30, 2026Episode 5845 min

Moses Adedoyin | How I Test Before Real Patients

Summary In 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. Takeaways Testing 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 Links LinkedIn: https://www.linkedin.com/in/mosesadedoyin/

September 16, 2026Episode 5744 min

Martin Eriksson | How I Tested The Decision Stack

Summary In 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. Takeaways Speed 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 Links Martin’s Website: https://martineriksson.com/ Martin’s LinkedIn: https://www.linkedin.com/in/martineriksson/ Coherent & Wrong: https://www.thedecisionstack.com/coherent-and-wrong/

September 2, 2026Episode 5648 min

Teresa Torres | How I Tested an AI Coach

Summary In this episode, I’m joined by Teresa Torres. Teresa is an author, speaker and a product discovery coach who I’ve been a fan of for quite a long time. We chat about why assumptions look different depending on the level you’re working at, why showing your work is so important for alignment, and how using AI inside your courses can give students more opportunities to practice and receive feedback. We also dig into AI evals - how Teresa uses them to measure the quality of her AI coaching tools, identify failure modes, and systematically improve their performance instead of just trusting that an LLM will get it right on the first try. If you want to learn more about testing assumptions, designing better learning loops, and using AI without giving up your critical thinking skills, this episode is for you. Takeaways AI can create a safer space for learning. Teresa found that people are often willing to ask an AI questions they might feel embarrassed asking another person. An AI coach works best when it is grounded in a clear teaching model. Teresa’s interview coach was effective because it was built on years of refined curriculum, rubrics, and explicit feedback criteria. AI can dramatically increase opportunities for deliberate practice. Instead of waiting for an instructor, students can practice repeatedly and receive immediate, personalized feedback. Building AI tools can improve the underlying curriculum. When an agent struggles with ambiguous instructions, it exposes gaps in how the material itself is taught. Evals are simply a way to measure AI quality. Teresa uses evals to identify specific failure modes, track how often they occur, and test whether changes actually improve the agent. Domain expertise still matters. Recognizing that an AI coach has given subtly bad advice often requires deep knowledge of the subject, not just technical skill. Product teams should define what “good” looks like for AI. Generic quality metrics are not enough; teams need acceptance criteria and evals tied to the unique value their product is supposed to create. AI changes the role of the teacher, not just the tools. Teresa is moving away from being the expert who simply delivers answers and toward creating environments where people can practice, get feedback, and learn for themselves. Guest Links ProductTalk Website: https://www.producttalk.org/ Teresa’s LinkedIn: https://www.linkedin.com/in/teresatorres/

August 19, 2026Episode 5547 min

Barry O’Reilly | How I Tested an Artificial Organization

Summary In this episode, I’m joined by Barry O’Reilly, he’s an entrepreneur, advisor, author and a friend of mine who works with executives to redesign how their organizations perform. He’s also the co-founder of Nobody Studios, an AI venture studio building and launching over 100 companies. Barry and I chat about what he’s learned from years of helping corporations experiment, redesign systems, and make better decisions under extreme uncertainty. We talk about why so many AI transformations start in the wrong place, why leaders should focus on the work before the tools, and how capturing conversations can create a kind of organizational memory that gets smarter over time. Barry also shares his lessons from building Nobody Studios and how that work influenced his new book Artificial Organizations. If you want to learn more about testing with AI to redesign how work gets done while making better decisions, you’ll love this episode. Takeaways Start with the work, not the AI tools. The biggest gains come from redesigning how work and decisions happen, then choosing technology that supports that flow. Human judgment becomes more important, not less. AI can capture, synthesize, and model information at scale, but leaders still need to decide what matters and what action to take. Treat conversations and decisions as data assets. Capturing meetings, transcripts, decisions, and outcomes creates organizational memory that can be reused instead of constantly recreating context. Better systems can outperform experience alone. Deep domain expertise still matters, but rigorous decision-making systems combined with machine intelligence can challenge gut instinct as the default. AI transformations fail when they are treated as tool rollouts. Buying Copilot or another platform does not change how an organization works unless behaviors, processes, and operating systems change with it. Leaders can accelerate adoption by role-modeling experimentation. Admitting “I don’t know,” trying tools in real work, and openly sharing what works and what does not creates permission for others to learn. Start with one decision or workflow. Rather than attempting a company-wide AI transformation, pick a recurring decision or process, test a new way of working, and learn from the result. Guest Links Barry’s Website: https://barryoreilly.com/ Barry’s LinkedIn: https://www.linkedin.com/in/barryoreilly/ Artificial Organization: https://artificialorganizations.com/ Nobody Studios: https://nobodystudios.com/

August 5, 2026Episode 5451 min

Mark Graban | How I Tested the Smallest Change

Summary In this episode, I’m joined by Mark Graban, he’s an expert in Lean Leadership, Psychological Safety, and Continuous Improvement. Mark and I discuss the idea of the “smallest test of change,” a principle rooted in lean thinking that can be applied far beyond manufacturing. Speaking of manufacturing, we sort of geek out on what we’ve learned from being around Toyota and seeing how they work in practice, not just in theory. We also dive into why organizations reward people for sounding certain, how leaders unintentionally punish experimentation, and why psychological safety is essential if you expect people to admit what they don’t know. Mark shares how he’s applying these ideas to his own work by testing a new book through iterative publishing. If you want to learn more about creating a culture where people feel safe to speak up and use smaller tests to drive meaningful change, this episode is for you. Takeaways Start with the smallest test of change - something quick, inexpensive, and focused on learning. Separate knowledge from assumptions by asking, “How do we know this is true?” before acting with certainty. Psychological safety is essential for experimentation because people must feel safe admitting mistakes, asking for help, and challenging ideas. Leaders shape culture through their reactions . Saying failure is acceptable means little if people are punished when a test does not work. Even experienced practitioners can overbuild . Mark’s AI coaching experiment reinforced the need to test demand and usefulness before adding more features. Guest Links Website: https://www.markgraban.com/ LinkedIn: https://www.linkedin.com/in/mgraban/

July 22, 2026Episode 5341 min

Mike Yarbrough | How I Tested a Jewelry Business

Summary In this episode, I’m joined by Mike Yarbrough, he’s the founder of Rustic & Main, a custom jewelry company that’s known for crafting unique, story-driven wedding bands and engagement rings. They specialize in combining traditional heavy metals with unconventional, nostalgic inlays. Mike shares how his handmade wooden wedding band accidentally started it all by generating unexpected customer demand, while still working his day job. We discuss how the business grew from a garage operation into a team producing handcrafted rings with deeply personal stories. We also get into the product failure that led Mike and his team to create its durability-testing process (appropriately called “the ringer”) and how he uses prototypes and limited launches to test out new revenue opportunities. This is a story about following market signals, learning from what breaks, and building a company without losing the craftsmanship and meaning that made customers care in the first place. Takeaways Customer pull can be one of the strongest early signals that an idea is worth pursuing. Starting small gave Mike time to test demand, refine the product, and understand the business before leaving software development. Product failures led Rustic & Main to create a more rigorous durability-testing process for every new design. Customer requests helped the company expand from wooden rings into titanium, gold, custom inlays, and new product categories. Testing a new business model, such as retail, requires patience because not every opportunity grows as quickly as the original business. Guest Links Rustic and Main: https://rusticandmain.com/

July 8, 2026Episode 5240 min

Maggie Baker | How I Tested Clothing Subscriptions

Summary In this episode, I’m joined by Maggie Baker, she’s the Founder of Threadeco, an eco-friendly clothing company rethinking how people discover, buy, and keep fashion. Maggie talks about how she started with a high-end fanny pack business that landed some serious celebrity traction, but still failed when the economy shifted in 2008. That experience taught her hard lessons about pricing, channel risk and adaptability. Years later, she turned those insights into Threadeco, when she was inspired by imperfect produce boxes. Maggie shares how she tested her early MVP with a Google Form, friends and family, hand-packed boxes, and no real payment system. Fast forward to today, business is thriving and we discuss how she is now blending retail, styling and subscriptions together. It is a great conversation about learning the hard way, starting scrappy, listening to customers, and building a business model that can actually adapt. Takeaways A past failure became the foundation for a better business model. Maggie’s high-end fanny pack business got retail traction and celebrity attention, but the 2008–2009 downturn exposed pricing, channel, and adaptability risks she had not fully tested. Testing does not have to be sophisticated to be useful. Threadeco started with a Google Form, friends-and-family beta testers, hand-curated clothing boxes, and manual feedback loops. The first version of the model revealed the wrong customer. Early testing showed that customers who only wanted to try on many items and return most of them were not a good fit for Threadico’s economics. Threadeco changed the model based on learning, not imitation. Instead of copying Stitch Fix, Maggie shifted to a prepaid five-piece box with limited exchanges, creating a model that better fit her inventory, cash flow, and customer behavior. The brick-and-mortar store became more than a retail channel. The store functions as a warehouse, styling space, discovery engine, and subscription acquisition channel. Location testing mattered. Moving within Old Sacramento changed the business dramatically, showing how even a two-minute difference in storefront location can create a completely different retail outcome. The next big test is integrating physical retail, subscriptions, styling, and tech. Maggie is exploring an app that connects customer style profiles, boxes, in-store experiences, and stylist recommendations into one ecosystem. Guest Links Maggie’s LinkedIn: https://www.linkedin.com/in/getajobla/ Threadeco: https://www.threadeco.com/

June 24, 2026Episode 5137 min

Eric Lowe | How I Tested Mental Hardening

Summary In this episode, I’m joined by Eric Lowe. He’s the CEO and Co-founder of Aptiva Health, an outpatient group that offers specialized care in orthopedics, physical therapy, pain management, and mental wellness. Eric shares how solving one problem at a time helped him scale to 14 locations and why he believes great businesses are built by developing people first. We dig into launching an ambitious healthcare marketplace that had all the ingredients of a great product, only to run headfirst into the brutal realities of marketplace dynamics, timing and customer education. Eric also details how his team uncovered a breakthrough business by integrating mental health directly into orthopedic recovery. He shares how they experiment with new healthcare models before overinvesting, and why listening closely to patients and providers often reveals opportunities you never could have imagined. If you’re building in healthcare and trying to figure out how to test bold ideas, this episode is for you. Takeaways Great businesses come from solving the next problem . Long-term growth often compounds from consistently identifying and solving the most pressing issue in front of you. Not every customer problem is yours to solve . Sustainable businesses focus on problems they are uniquely positioned to solve while aligning value across all stakeholders. Marketplaces are brutally hard to build . Even strong solutions can struggle when adoption requires changing deeply ingrained customer behavior. Sometimes the customer isn’t who you think it is . Success often comes from recognizing when the original target market needs to shift entirely. Unexpected opportunities can outperform your biggest bets . Some of the strongest growth opportunities come from adjacent problems you discover while serving customers. Adoption depends on reducing friction . New solutions succeed when they fit naturally into how people already behave rather than forcing entirely new habits. Culture is built through actions, not slogans . Teams buy into organizations when leadership consistently demonstrates the values they claim to stand for. Big investments require proving the economics first . When capital commitments are high, getting the financial assumptions wrong can become incredibly expensive. Guest Links Eric’s LinkedIn: https://www.linkedin.com/in/ericloweaptiva Aptiva Health: https://www.aptivahealth.com/ Broker Driven: https://brokerdriven.com/

June 10, 2026Episode 5041 min

Courtney Honda and Slava Borisov | How I Tested Pet Retail

Summary In this episode I’m joined by Courtney Honda and Slava Borisov co-founders of Puptqe. What started as a deeply personal response to a health scare involving their dog Champ has grown into a unique retail experience built around community and creating memorable moments for dogs and their owners. We explore how they tested their way through one of the most competitive retail categories imaginable, discovering that success wasn’t about competing with big-box stores on price or selection. Instead, they focused on creating experiences that customers couldn’t get anywhere else, from dog-friendly events and memberships to immersive in-store moments designed to turn visitors into loyal advocates. We also get into the realities of building a brick-and-mortar business, including lessons around site selection, customer retention, community-driven marketing, and the surprising acquisition channels they tested to help them grow. Courtney and Slava share how they learned to stop trying to serve every pet owner, focus on their ideal customer, and transform retail from a transaction into an ongoing relationship. If you’ve ever wondered how to test a physical retail store in a crowded market, this episode is for you. Takeaways Differentiate by owning a niche, not by competing on price - Puptqe succeeded by becoming a destination for dog experiences and hospitality instead of trying to out-discount larger pet retailers. Make the experience the product; sales will follow - Customers come for the events, community, and memories, which naturally drives purchases and loyalty. Stop marketing to everyone and focus on your ideal customer - Growth accelerated once they identified who their best customers were and tailored their offerings around them. Retention matters more than acquisition - Long-term success came from creating reasons for customers to return again and again, not just making the first sale. Community and word-of-mouth outperform paid advertising - Loyal customers and local advocacy generated more sustainable growth than trying to outspend competitors on ads. Understand customer behavior, not just demographics - Knowing how customers spend their time and make decisions proved more valuable than basic age, income, or location data. Design every customer touchpoint intentionally - Every interaction, from the greeting to the checkout experience, was crafted to create memorable moments. Consistency beats constantly chasing new tactics - Small improvements executed repeatedly created stronger results than jumping from one growth idea to the next. Guest Links Courtney’s LinkedIn: https://www.linkedin.com/in/courtney-honda/ Slava’s LinkedIn: https://www.linkedin.com/in/slavaborisov/ Puptqe Website: https://puptqe.com/

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