How I Tested That

About How I Tested That

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.

Publisher

David J Bland

Category

business · technology

Language

en

Explicit

No

First episode

20 Mar 2024

Latest episode

14 Oct 2026

Latest episodes

59 episodes in the feed.

1. 

   14 Oct 2026

   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/ (https://www.linkedin.com/in/melissajeanperri/) Product Institute: https://productinstitute.com/ (https://productinstitute.com/)

2. 

   30 Sept 2026

   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/ (https://www.linkedin.com/in/mosesadedoyin/)

3. 

   16 Sept 2026

   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/ (https://martineriksson.com/) Martin’s LinkedIn: https://www.linkedin.com/in/martineriksson/ (https://www.linkedin.com/in/martineriksson/) Coherent & Wrong: https://www.thedecisionstack.com/coherent-and-wrong/ (https://www.thedecisionstack.com/coherent-and-wrong/)

Who has been a guest on How I Tested That

Names identified in recent episode analyses. Showing up to five guests.

• Sylvia Hall
• Dylan Lam
• Matthew Davis
• Amer Abu-Khajil

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

4 ratings

Spotify

5.0 / 5

6 ratings

Podcast Authority Score: 39 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

53

Social presence

0

YouTube

0

Engagement

60

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