How I Tested That

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

1. 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.

2. 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.

3. Marketplaces are brutally hard to build. Even strong solutions can struggle when adoption requires changing deeply ingrained customer behavior.

4. Sometimes the customer isn’t who you think it is. Success often comes from recognizing when the original target market needs to shift entirely.

5. Unexpected opportunities can outperform your biggest bets. Some of the strongest growth opportunities come from adjacent problems you discover while serving customers.

6. Adoption depends on reducing friction. New solutions succeed when they fit naturally into how people already behave rather than forcing entirely new habits.

7. Culture is built through actions, not slogans. Teams buy into organizations when leadership consistently demonstrates the values they claim to stand for.

8. 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/

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

1. 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.

2. Make the experience the product; sales will follow - Customers come for the events, community, and memories, which naturally drives purchases and loyalty.

3. 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.

4. 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.

5. Community and word-of-mouth outperform paid advertising - Loyal customers and local advocacy generated more sustainable growth than trying to outspend competitors on ads.

6. 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.

7. Design every customer touchpoint intentionally - Every interaction, from the greeting to the checkout experience, was crafted to create memorable moments.

8. 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/

Summary

In this episode I’m joined by Chris Butler. He’s a longtime product leader and operator whose career spans companies such as Microsoft, Google, Facebook, and now GitHub, where he works on agentic workflows across the organization.

We explore how AI is reshaping the way modern product teams think, collaborate, ship and its ripple effects on how we manage process and decision making.  Chris and I chat about the messy realities behind agentic systems such as why removing too much friction can actually hurt decision quality and why qualitative research matters more now than ever before. 

Chris gives a candid behind the scenes look into what’s working, what’s failing, and why experimentation itself may become one of the most important capabilities in the AI era.

If you’ve been wondering what testing AI Agents actually looks like inside a cutting edge company, this episode is for you.

Takeaways

1. AI is collapsing traditional product development workflows, but not necessarily eliminating the need for product managers, engineers, or designers. Instead, roles are decomposing into smaller tasks where humans and machines each handle different types of work.

2. Removing all friction from product development can actually reduce decision quality. Chris argues that tension between desirability, viability, and feasibility perspectives is still critical because reasoning often happens through human discussion, not just inside individual minds or AI systems.

3. AI-generated “rude feedback” tools can help teams improve ideas faster because people are often more receptive to harsh critique from a machine than from another human. GitHub experimented with sarcastic AI Q&A systems that surfaced weak assumptions and missing details without the reputational risk of peer criticism.

4. The future of AI inside organizations may be less about autonomous agents replacing humans and more about “process as code.” GitHub is experimenting with natural-language policy documents that both humans and agents can read to automate operational workflows, release management, and risk detection.

5. Product teams are at risk of building faster without learning faster. Chris warns that vibe coding and rapid prototyping can unintentionally reduce time spent talking to customers and conducting qualitative research, which still remains essential for understanding mental models and uncovering hidden assumptions.

6. Agentic workflows become most valuable when they reduce operational toil instead of replacing human judgment. GitHub is using AI to automate repetitive coordination tasks like release tracking, documentation generation, and status updates so teams can spend more time on strategic thinking and collaboration.

7. Internal experimentation matters just as much as customer-facing innovation. Chris emphasizes that many AI workflow experiments inside GitHub are intentionally small, lightweight tests designed to explore possibilities quickly before deciding whether to scale, modify, or abandon them.

8. The biggest long-term challenge for enterprise AI adoption may not be model capability, but integration, governance, and organizational coordination. Authentication, permissions, fragmented tooling, disconnected workflows, and siloed information remain major barriers to making agentic systems truly useful at scale.

Guest Links

LinkedIn: https://www.linkedin.com/in/chrisbu/

GitHub Next: https://githubnext.com/

Summary

In this episode I’m joined by Chad Holdorf, longtime product and technology leader whose career spans John Deere, Salesforce, Pendo, and now Demandbase, where he leads AI initiatives across the company.

We explore how AI is fundamentally reshaping the way modern product teams test, ship, and learn, from debugging customer issues directly against live codebases to product managers and support teams submitting pull requests themselves. Chad shares how tools like Cursor and Claude are collapsing traditional handoffs between product, engineering, and support, creating a much faster feedback loop between customer problems, experimentation, and shipped solutions.

We also get into the messy reality behind enterprise AI adoption, including data quality, hallucinations, trust, evals, and why testing AI products inside real customer environments is much harder than most demos make it look. Chad gives us a peek into how his own workflow has changed, how his teams are learning by building in real time, and why this moment reminds him of the early days of Lean Startup, where he and I first met.

If you’ve been wondering what AI-native product development actually looks and feels like inside a real company, this episode is for you.

Takeaways

1. AI is collapsing traditional handoffs between product, engineering, and support teams. Chad described customer support teams going directly into code repositories with AI tools to investigate issues, understand root causes, and eventually submit merge requests themselves.

2. Most enterprise AI demos fall apart when connected to messy real-world customer data. Chad emphasized that “just putting Claude on top of the data” failed quickly without extensive labeling, validation, testing, and human feedback loops. Customers could detect hallucinations within a few prompts.

3. AI systems expose hidden data inconsistencies inside organizations. One example showed AI selecting a custom CRM field that technically produced better targeting results than the field support teams were trained to use, creating confusion about which “truth” was actually correct.

4. Trust has become the critical success factor for enterprise AI adoption. Chad explained that once customers encounter inaccurate outputs, confidence in the system drops immediately, which forces teams to spend enormous time improving prompts, SQL queries, evals, and validation workflows before broader rollout.

5. Product managers are increasingly becoming hands-on builders again. Instead of relying entirely on engineering handoffs, Chad now spends large portions of his week inside Cursor and AI coding agents investigating bugs, generating tickets, reviewing repos, and shaping product direction directly through code conversations.

6. AI-native workflows dramatically compress feedback loops. Problems that previously took days of back-and-forth between support, product, and engineering can now move from customer issue to deployed fix in under an hour through AI-assisted workflows and automated merge requests.

7. The biggest organizational bottleneck is shifting away from engineering speed toward enablement and adaptation. Chad compared this moment to early Agile adoption, where downstream teams like sales, support, and training struggled to keep pace with accelerated shipping cycles. AI is now amplifying that challenge even further.

8. Continuous learning and experimentation matter more than formal process mastery right now. Chad repeatedly compared the current AI moment to the early Agile movement: the people progressing fastest are the ones willing to try tools, build things, stay curious, and learn in public rather than waiting for established best practices or certifications.

Guest Links

LinkedIn: https://www.linkedin.com/in/chadholdorf/

Demandbase: https://www.demandbase.com/

Summary

In this episode, I’m joined by Büşra Coşkuner, a product management coach and trainer who helps teams move from project thinking to product thinking.

We explore how she approaches product discovery and testing. From building zero-to-one products at Doodle to coaching teams across B2B and B2C environments, Büşra shares how to actually operationalize experimentation beyond just A/B tests.

We also dig into how to test when you don’t have much data, how to combine qualitative and quantitative insights, and why many teams get stuck thinking they’re doing product work when they’re really just managing tickets.

If you’re trying to build a stronger testing culture or just want to make better decisions this episode will challenge how you think about product metrics and experimentation.

Takeaways

1. Product transformation is a leadership decision - If leadership isn’t backing the shift from projects to products, it won’t happen, bottom-up enthusiasm isn’t enough.

2. Most “product orgs” aren’t actually product orgs - Adopting Scrum and calling someone a product owner doesn’t mean you’re doing product, many teams are still just managing tickets.

3. You can’t test what you can’t measure - Without proper data instrumentation, teams fall into a “build, build, build” loop instead of build–measure–learn.

4. Metrics frameworks are a starting point, not the system - Pirate metrics (AARRR) or customer factory models help, but real insight comes from adapting them to your actual business model.

5. Qualitative data is not optional - Quant tells you what is happening, qual tells you why. In low-data environments, qual becomes your primary signal.

6. “No data” is usually an excuse - Even in B2B, you can extract directional insights, from sales teams, customer conversations, and patterns across feedback.

7. A/B testing is over-indexed and often misused - Experimentation goes beyond A/B testing. Many teams default to it even when it’s impractical or irrelevant.

8. Sometimes building is the test - For low-risk features, the fastest way to learn is to ship and observe behavior, treat the release itself as the experiment.

9. B2B testing requires creativity, not scale - From sales-assisted experiments to prototype validation and even WhatsApp groups, testing in small markets is possible if you rethink the approach.

10. AI changes the cost of being wrong - When building becomes cheap, you don’t always need heavy upfront validation, you can test the problem through the solution, as long as you’re willing to kill what doesn’t work.

Guest Links

LinkedIn: https://www.linkedin.com/in/busra-coskuner/
Website: https://busra.co/

Summary

In this episode I’m joined by Gil Vaisman. He’s the founder of GoADU, a construction company focused on building accessory dwelling units.

We explore how he went from a 15-year career in film editing to building a construction business that helps homeowners unlock equity and create new living spaces. 

What started out as a personal project in his own backyard turned into a growing business built through trial, error, and constant iteration.

Gil shares how he tested his way into the market, from helping friends navigate permitting to evolving into a guaranteed fixed pricing model. We also dig into how he qualifies customers, avoids costly mistakes, and thinks about what to test next in an industry that’s rapidly changing.

If you’re trying to turn a personal pain point into something scalable, this episode is a great look at how testing can lead to a viable business.

Enjoy my conversation with Gil Vaisman.

Takeaways

1. Great businesses often start as personal pain points - Gil’s ADU company emerged from building one in his own backyard and helping friends navigate the same confusing process. 

2. Transferable skills matter more than industry experience - His background in film production translated directly into construction, both require coordination, budgeting, timelines, and managing complex teams. 

3. Early traction came from education, not selling - In the beginning, most customers didn’t even know what an ADU was, growth required teaching the market before capturing it. 

4. Trial and error built the real expertise - Navigating difficult permitting processes and making costly mistakes early on became the foundation for a repeatable, refined system. 

5. Pre-qualification is critical in complex services - Gil now asks 20–25 upfront questions before taking on a client, ensuring alignment and reducing downstream risk. 

6. 
   Competing on price is a starting point, not a strategy - The business initially won work by being the cheapest, but evolved into a premium, top-20% offering focused on quality and service. 

7. A strong value proposition can reduce industry fear - Guaranteed fixed pricing became a key differentiator, directly addressing customer anxiety around hidden costs and change orders. 

8. Future innovation is constrained by feasibility, not demand - Customers clearly want faster, cheaper builds (prefab, SIPs), but adoption is limited by execution risk, expertise gaps, and inconsistent quality. 

Guest Links

GoADU: https://www.goadu.com/

Vaisman Construction: https://www.vaismanconstruction.com/

In this episode I’m joined by Bill Fienup. He’s the co-founder of mHUB, one of the world’s leading hardtech innovation centers, located in Chicago, IL.

We explore how he went from building Nerf gun prototypes at MIT to creating a space where thousands of hardware founders can prototype, test, and scale physical products. What started out as a meetup group and a spreadsheet, grew into a full ecosystem with millions of dollars in equipment and billions of dollars in economic impact.

Bill shares how to test hardware ideas without burning capital, why most teams over-focus on feasibility instead of desirability, and how to validate what people will actually pay for before you build.

If you’re working on physical products, or funding them, this episode is a masterclass in how to test before you invest.

Enjoy my conversation with Bill Fienup.

Takeaways

1. Start with the problem, not the solution. The biggest risk isn’t building something, it’s solving a problem that customers don’t care enough about to act on.

2. Desirability and willingness to pay matter more than feasibility early. Teams often over-focus on building, but the real uncertainty is whether customers value the solution enough to pay.

3. Test demand before investing in development. Simple experiments like landing pages or fake purchase flows can validate real interest before committing resources.

4. Iterate in spirals, not stages. Move across desirability, feasibility, and viability repeatedly, increasing investment only as uncertainty is reduced.

5. Avoid building the wrong thing the right way. Strong execution can’t fix a fundamentally misaligned product, validation must come before scale.

6. Use competition as validation. Existing solutions signal real demand and confirm the problem is worth solving.

7. Focus on the majority, not edge cases. Designing for the loud minority can increase cost and complexity without improving overall product-market fit.

8. Community can be a powerful starting point. MHub began as a meetup and shared spreadsheet, showing how real user pain can evolve into a scalable ecosystem.

Guest Links

mHub’s Website: https://www.mhubchicago.com/

LinkedIn Profile: https://www.linkedin.com/in/fienup/

Summary

In this episode I’m joined by Dr. Akvile Ignotaite, a data scientist and founder building AI-powered skin health technology used by more than 800,000 people around the world.

We explore how her team combines data science, health tech, and creative marketing to rethink skincare for Gen Z and Gen Alpha. From building a vast skin care dataset to launching a TikTok influencer pimple called Pimsy that has almost 40k followers, Akvile shares how cultural insights and small tests drive their product strategy.

We also get into the challenges of building health technology for younger audiences, how to test ideas across different global markets, and why treating skin as a health problem, changes how you design products and measure success.

If you’re interested in experimentation and AI in health you’ll enjoy my chat with Akvile.

Takeaways

1. Start small and imperfect to learn faster. The team prioritizes quick MVPs, sometimes built in days, to test ideas before investing heavily in development, branding, or marketing.

2. Customer language and psychology matter. The original millennial-focused “compliance app” failed because it sounded too technical; shifting to Gen Z language, emojis, and storytelling dramatically improved adoption.

3. Meet users where they already are. Channels like TikTok became critical for reaching younger audiences, even though the team initially resisted the platform.

4. Creative experimentation can unlock growth. The “Pimsy” influencer pimple character started as a small test and quickly grew to tens of thousands of followers, proving unconventional ideas can resonate strongly with audiences.

5. Micro-learning can drive high engagement. A simple, quickly built “myths vs. facts” quiz feature created massive engagement and generated valuable behavioral data about user beliefs.

6. User feedback is a competitive advantage. Hiring a developer who criticized the Android experience highlighted the importance of listening closely to real user complaints and improving where customers actually are.

7. Cultural assumptions can mislead founders. Expanding into India revealed how preconceived ideas about markets, healthcare practices, and culture can be wrong, reinforcing the need for curiosity and humility.

8. Structured programs don’t always fit real user behavior. Highly designed 6- or 8-week skincare programs failed because users resisted rigid routines, showing how human behavior often breaks logical product design.

9. Gen Z and Gen Alpha are forming a global digital culture. The app’s success without localization suggests younger generations increasingly share common digital behaviors and language across regions.

Guest Links

System Akvile: https://systemakvile.com/
LinkedIn Profile: https://www.linkedin.com/in/dr-akvile-ignotaite/
Pimsy TikTok: https://www.tiktok.com/@i.am.pimsy

In this episode I’m joined by Jim Morris.  We chat about the wake-up call that pushed him from building first to testing first. Jim and I discuss loyalty programs no one wanted, roadmaps filled with sequenced risk, AI prototypes that hallucinate and the uncomfortable reality that confidence often replaces evidence.

We also dig into something deeper: why smart teams ignore data, why leaders fall in love once an idea hits the roadmap, and why testing isn’t about better UX,  it’s about real value.

Jim shares how he even tests his own teaching process for students at Berkeley.

Because as he puts it:

Enjoy my conversation with Jim Morris.

Takeaways

• Testing is crucial to ensure product effectiveness and user engagement.

• Data analysis can reveal the true usage of product features.

• Mindset plays a significant role in how product ideas are perceived and developed.

• Not all ideas will succeed; testing helps identify the viable ones.

• User motivation is key to the success of features and programs.

• Prototyping tools can enhance the testing process but require careful implementation.

• Learning from failures in testing is essential for growth and improvement.

• Roadmaps should be flexible to adapt to changing priorities and evidence.

• It's important to focus on the core value proposition of a product.

• Continuous experimentation and adaptation are vital in product management.

Guest Links

Website: https://productdiscoverygroup.com/

LinkedIn Profile: https://www.linkedin.com/in/jimmorrisstanford/

Summary

In this conversation, David J Bland and Dan Olsen discuss the evolution of product management, the impact of vibe coding, and the importance of cross-functional collaboration. They explore the challenges of prototyping, user research, and the role of AI in product development. The discussion emphasizes the need for strong product management fundamentals and the future of product management in a rapidly changing landscape.

Takeaways

• The awareness of product management has significantly increased over the years.
• Vibe coding allows for rapid prototyping and testing without heavy technical resources.
• Cross-functional collaboration is essential for successful product development.
• User research is becoming more valued in product management.
• Prototyping should focus on learning rather than just building.
• AI can assist in generating ideas but lacks judgment in prioritization.
• The pace of innovation in product tools is accelerating rapidly.
• Understanding customer problems is crucial for product success.
• Rushing to high fidelity prototypes can lead to missed opportunities in the problem space.
• Product management fundamentals will be key in differentiating successful products.

Guest Links

Website: https://dan-olsen.com/

LinkedIn Profile: https://www.linkedin.com/in/danolsen98/

YouTube Channel: https://www.youtube.com/danolsen

Lean Product Meetup: https://www.meetup.com/lean-product/ 

Vibe Coding Product Brief: https://dan-olsen.com/vibe-coding/

Vibe Coding Spectrum: https://dan-olsen.com/vibe-coding/

The Lean Product Playbook: https://amzn.to/1EYCUdP