Category creation can help founders and innovation leaders stop competing in crowded markets and start defining new ones. In this episode of Inside Podcast, Ian Bergman and Layne Fawns talk with Kevin Maney, co-founder of Category Design Advisors, and author of The Category Creation Formula, about how companies create and lead new market categories.

Kevin breaks down his framework: context + missing + innovation = new category. He explains why dominant design matters more than first-mover advantage, how Netflix and Tesla shaped customer behavior, and why category strategy can align product, marketing, and leadership around a bigger opportunity.

Key Topics

🚀 Category creation for founders and innovators

📐 Context + missing + innovation

🏆 Dominant design vs. first-mover advantage

🎬 Lessons from Netflix, Tesla, Zamboni, and Chrysler

🤖 AI, creativity, and authenticity

🏢 Innovation inside large companies

Chapters

🎙️ Introduction to Kevin Maney and Category Creation 00:00

🤖 AI, Creativity, and the Human-Machine Partnership 02:11

🎨 Authenticity, AI, and the Future of Creative Work 08:24

📘 Kevin’s Journey from Tech Journalism to Category Design 15:48

🚀 What Category Design Means and Why It Matters 20:33

📐 The Category Creation Formula 21:02

🧠 What a New Category Really Is 23:15

🏆 Why Competitors Can Validate a Category 26:01

🥇 Dominant Design vs. First-Mover Advantage 28:03

🚗 The Zamboni Story and Hidden Category Creation 31:31

🔁 Market-Product Fit vs. Product-Market Fit 35:42

🧭 How to Understand What Customers Really Need 38:41

🏷️ Category vs. Brand 41:20

📣 How to Know a Category Is Catching On 43:38

🎬 Netflix, Timing, and the Adjacent Possible 46:11

⏱️ How to Know When the Market Is Ready 54:31

🏢 Category Creation Inside Large Companies 56:19

💡 Final Takeaway for Founders and Innovation Leaders 01:00:38

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How do leaders create real change when the problem has no clear answer?

In this episode, Ian Bergman and Layne Fawns sit down with John Michael Schert, founder of JMS & Company, to explore adaptive leadership, creativity, organizational change, and why the hardest part of innovation is often letting go of what used to work.

John Michael brings a rare perspective shaped by professional ballet, nonprofit leadership, entrepreneurship, Harvard-trained adaptive leadership, and years advising teams through complex change.

This conversation breaks down why creativity is not just a soft skill, but a practical tool for founders, operators, and innovation leaders facing uncertainty.

Key topics

🎯 Why people are not afraid of change, they are afraid of loss

🏔️ How success can become a trap for founders and organizations

🧩 The difference between technical problems and adaptive challenges

💡 Why creative choices are fragile before they can be proven

🛡️ How leaders can protect teams while they work through uncertainty

👂 Why listening and diagnosis matter before jumping to solutions

🤝 How to build coalitions instead of trying to drive change alone

🎭 What organizations can learn from artists without romanticizing creativity

🔄 Why innovation often starts by letting go of the 10% that no longer serves the system

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In this episode, neuroscientist, entrepreneur, and author Vivienne Ming explains why the biggest opportunity in AI is not automation, but helping people think better, ask better questions, and solve harder problems.

Vivienne breaks down the difference between well-posed and ill-posed problems, why most companies are using AI the wrong way, and how leaders can build cultures that reward exploration instead of safe answers. She also shares practical ideas on human and AI collaboration, learning, innovation, and preparing people for a future where routine work matters less and judgment matters more.

Key topics

🤖 AI augmentation vs automation

🧠 Well-posed vs ill-posed problems

🚀 Why better questions drive innovation

🏢 How leaders can reward productive failure

👥 Human and AI collaboration in real work

📚 How to build better thinkers, teams, and kids for the future

Chapters

00:00 🧠 Intro to Vivienne Ming

01:55 ⚡ Rapid fire and the nature of real innovation

05:37 🔍 Why facts are not enough anymore

08:16 🤖 AI, well-posed problems, and ill-posed problems

15:58 🚀 Why the future belongs to people who explore the unknown

20:26 🏢 How leaders build cultures that reward productive failure

21:51 🔥 The efficiency lie in AI

27:00 👥 AI augmentation vs automation

29:52 👶 How to prepare kids for an AI-shaped future

33:27 🗺️ Designing tools that make people better

35:56 📚 Why AI tutors should not give answers

37:14 🛠️ What leaders should do differently right now

43:36 🌍 Vivienne’s realistic view of the future of AI

47:55 📊 Using AI to uncover human behavior and hidden patterns

57:40 🧭 How people really make decisions

01:01:28 🧪 Hybrid intelligence and human + AI teams

01:07:10 💡 Why human + AI teams outperform

01:10:20 ✅ The real choice: stay shallow or go deep

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In this episode, Wendy Gonzalez, CEO of Sama, breaks down why high-quality data, human-in-the-loop systems, and clear evaluation standards are essential for building AI that actually works at scale.

Wendy shares how enterprises train, validate, and improve AI models in the real world, from autonomous vehicles to e-commerce recommendations and generative AI. She also explains why dirty data, edge cases, and weak quality standards can quietly kill AI performance, trust, and adoption.

You’ll also hear a sharp conversation on responsible AI, model bias, regulation, language inclusion, and why founders and innovation leaders need to define what “good” looks like before shipping AI products.

If you are building, buying, or leading AI initiatives, this episode offers practical insight on AI deployment, trustworthy AI, training data, model accuracy, and the human systems behind production-grade machine learning.

Key topics

🤖 How to get AI into production and keep it there

🧠 Why human-in-the-loop systems still matter in modern AI

🧹 What dirty data is and how it hurts model performance

🎯 Why edge cases define real-world AI success

📊 How enterprises think about AI quality, validation, and ROI

🚗 Lessons from autonomous vehicles, safety, and model training

🛒 How recommendation engines and search relevance depend on better data

🌍 Why language, culture, and context matter in AI models

⚖️ Responsible AI, regulation, and the tension between policy and speed

🔍 How users can think critically and decide when to trust AI outputs

👥 How Sama connects AI training work with economic opportunity and impact

💼 Leadership lessons on humility, growth, and “firing yourself” as a CEO

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In this episode, Ian Bergman sits down with Dhruv C. Patel, co-founder of Syncurrent, to explore how AI is helping local governments, Tribal Nations, and public agencies find and access funding faster, turning a process that once took months into something that can happen in minutes. Together, they unpack why so many communities are shut out of critical funding opportunities, how outdated systems create massive inefficiencies, and why GovTech may be one of the most meaningful frontiers in innovation today. Dhruv also breaks down the deeper questions shaping public sector technology, including trust, regulation, access, equity, and the responsibility that comes with building tools that influence how resources reach real people. It’s a sharp, optimistic conversation about AI, government innovation, underserved communities, and what it really looks like to build technology that helps society function better.

Topics

🤖 00:00:00 The next decade of innovation: human, chaotic, and uncomfortable

🚀 00:06:05 How Dhruv built Syncurrent from a consulting business into an AI-first GovTech company

💸 00:09:19 The hidden funding problem facing 90,000 local governments across the U.S.

🏛️ 00:12:10 How Syncurrent helps cities, towns, and Tribal Nations find grants in minutes instead of months

📈 00:14:05 Why product-led growth works in government and how Syncurrent made adoption easier

🌾 00:16:42 Why underserved rural communities may be the most important market in tech

📄 00:19:38 Why finding funding is only the beginning: applications, deadlines, and compliance

🔥 00:22:20 Why GovTech is heating up and how AI can expand public sector capacity

⚖️ 00:25:13 The debate around public funding: friction, trust, and accountability

📊 00:29:30 How easier access to funding could influence policy and future resource allocation

🧩 00:33:20 Resistance to GovTech, the status quo, and who gets left behind

🤝 00:35:18 How Syncurrent is building trust with governments through partnerships and referrals

🌍 00:39:27 The bigger vision for AI in government and why Dhruv sees technology as deeply human

📱 00:49:45 The access challenge: internet, devices, and making innovation more inclusive

❤️ 00:52:10 Why technology should help communities move forward, not leave them behind

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What if innovation is not about moving faster, but moving with purpose? In this episode of Innovators Inside, Ian Bergman sits down with Dr. Hisham Alasad, head of innovation enablement at Qatar Airways, to unpack a human-first view of innovation shaped by fintech, academia, and a bold move to Qatar. They break down what open banking really changes, why banks fight it, and how open finance could unlock better, cheaper products for consumers. Then they go deeper: why innovation requires overcoming fear, why closed systems stall progress, and what a “Responsible Innovation” framework could look like that is ethical, inclusive, scalable, and beneficial beyond the balance sheet. They close with a big vision: using AI to help create opportunity and peace in the Middle East.

Topics & Timestamps

🍫 00:00:00 Welcome Hisham Alasad

🧠 00:04:32 The one skill future leaders cannot skip: emotional intelligence

🤖 00:06:49 AI as a permanent teammate, plus the keyboard vs voice bet

⚡ 00:13:16 Tech progress vs fragile infrastructure and reliability

🌍 00:17:10 Why Hisham moved to Qatar and how he avoids “comfort zone stagnation”

🏦 00:19:25 Open banking explained: data ownership, APIs, and breaking bank lock-in

🧾 00:23:08 How banks react to open banking: wait and see, distributor, manufacturer, or platform

🧍 00:31:08 Open finance and why digital identity is a key prerequisite

🔮 00:34:34 The “Holy Trinity” prediction: open data + AI + quantum computing by ~2030

😨 00:36:27 Why innovation is overcoming fear (and how fear hides behind “risk” and “regulation”)

🧭 00:39:36 The Responsible Innovation framework: purpose alignment, open ecosystems, governance, ESG ROI, scalability

✈️ 00:47:31 Banking vs aviation: the shared foundation is trust

🕊️ 00:49:18 “AI for Peace”: a vision to build a Silicon Valley of the Middle East

🧩 00:52:09 Closing lesson: innovation is not about speed, it’s about direction

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What does it really take to lead through constant disruption? Jim Stallings, Founder and CEO of PS27 Ventures, shares lessons from a career spanning the U.S. Marine Corps, senior leadership roles at IBM, and now early-stage investing. Jim breaks down how IBM turned Linux from free software into a multibillion-dollar business, why internal resistance is often the biggest blocker to innovation, and how leaders can use customers to force change. He also explains what he looks for in founders, why leadership matters more than a perfect plan, and how AI is collapsing product cycles from years to months.

Topics & Timestamps

📚 00:00 The Innovator’s Dilemma and why it still defines innovation today

🗓️ 02:45 AI tools, gamification, and how platforms drive behavior

🧠 13:08 The most critical skill for future leaders: iteration

🪖 15:04 From the Naval Academy to IBM leadership

💡 23:43 Turning Linux into a $7B business at IBM

🛡️ 27:25 Using the voice of the customer to overcome internal resistance

🏛️ 33:17 Why putting Linux on the mainframe changed everything

🔬 37:15 Patents, invention timing, and winning before the sale

🌍 41:06 Why emerging markets adopt innovation faster

🧑‍💼 43:22 How PS27 Ventures evaluates founders and ideas

⚡ 46:25 AI speed, Copilot, and shrinking innovation cycles

🤖 50:49 What it really means to be AI-native

🏆 56:06 Leadership vs planning and the investment that proved it

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Building an app is easy. Building an app people keep using is the hard part. In this episode, we sit down with Ghazenfer Mansoor, Founder and CEO of Technology Rivers and author of Beyond the Download. He breaks down why so many software and AI projects fail, how to design an MVP that is truly usable, and the “blueprint” process that helps teams plan the right foundation before writing code. We also talk about how AI is changing product development, why developers need to think like product engineers, and how teams can use AI tools to move faster without creating unscalable messes. Plus, Ghazenfer shares what he is seeing next in health tech, especially the rise of predictive, personalized care.

Topics & Timestamps

🚀 00:00 Intro to Ghazenfer, Technology Rivers, and building AI-driven products

🍎 01:07 Rapid fire: disruption, food innovation, and longevity

📚 05:24 Favorite book and the people skills founders still need

💥 09:14 Failure as learning and why persistence wins

🧠 11:12 From Pakistan to startups to building products that users want

🧱 15:38 Why “build it and they will come” fails

🏗️ 16:20 MVP that is usable, not a demo (the studio apartment metaphor)

🧩 19:02 The Blueprint process: plan enough, then iterate fast

📲 23:18 Beyond the Download: building mobile apps people remember

❤️ 28:06 How to know if your app is remarkable (the dating profile test)

🍏 30:13 Getting featured by Apple and what drives visibility

🤖 34:12 How AI is changing software teams and product building

⚡ 37:31 Using AI tools to create a POC in hours before a sales call

🧑‍💻 39:43 Why developers must think like product engineers now

🧭 43:14 Building an AI-forward culture, even with resistance

🧪 47:32 Weekly AI learning rituals, goals, and tool budgets

🏥 53:22 Health tech trends: prediction and personalized insights

🔗 55:04 Where to find Ghazenfer and Technology Rivers

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Startups and corporates need each other. But most partnerships fall apart. In this episode, Ian Bergman talks with Noga Tal about what actually makes these relationships work. They break down why thinking big early is not hype, it is planning. How founders can back up a bold vision with data and steps. Why corporate bureaucracy kills momentum. How to align on the real why, set shared metrics, and define an exit plan that protects the relationship. They also dig into emerging markets, what AI changes, why AI creates more noise than ever, and how to keep the human element alive while moving fast.

Topics & Timestamps

🚀 00:00 Welcome and who is Noga Tal

🧭 01:07 The accidental techie path from nonprofits to Microsoft

🌍 04:17 Why founders should think global from day one

🧱 05:33 The hidden product and ops decisions that block scale

🎯 07:02 How to pitch big vision with real data and steps

🤝 11:23 Why corporates and startups need each other but struggle to win together

⚠️ 14:05 The real reasons partnerships fail, misalignment, speed, and unclear problems

🧰 18:06 How to de risk collaboration with goals, checkpoints, and an exit strategy

✅ 19:58 How to “fail with success” and keep the relationship intact

🔁 21:26 The story of pivoting a massive partnership when nobody was happy

🌐 26:26 Emerging markets and why new innovation models will matter more

🤖 31:21 AI is changing partnerships and creating a flood of look alike startups

🏎️ 33:20 Standing out with messaging, trust, and speed

🧑‍🤝‍🧑 35:55 Keeping the human element as AI reshapes teams and work

🩺 39:50 A real example of using AI to advocate for better healthcare outcomes

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In this episode, Marcelo Calbucci breaks down the innovation habits that shaped his career at Microsoft, Amazon, and across six startups. He explains why small, fast iterations beat big plans, how narrative driven strategy unlocks clarity, and why today’s founders should embrace niche, hyper specialized solutions. Marcelo also shares how Amazon’s PR FAQ framework can help any innovator inside a startup or a large company make better decisions, avoid wasted cycles, and build products that truly solve customer problems. We also dive into regional and global innovation, community as a catalyst, the future of AI, and the universal truths every builder should adopt.

Topics & Timestamps

🧠 00:00 Marcelo’s background in innovation and why tech pulled him in

🚀 08:00 Why tech enables instant iteration and faster learning

💸 11:20 How the VC model is shifting toward profitable smaller scale companies

🌐 15:05 Why community accelerates innovation and how Marcelo builds founder circles

📘 17:45 Inside Marcelo’s book and the PR FAQ framework

📝 20:00 Universal truths of innovation and why they matter

🏢 25:30 Why innovation is harder in big companies and how to make it work

🧩 28:50 Simple definitions of innovation that actually matter

🛠️ 31:40 How anyone can start using the PR FAQ method today

🏛️ 36:00 How Amazon avoids consensus thinking and mediocrity

✍️ 43:00 Why narrative combined with data is a superpower

🏗️ 47:55 Where startup and corporate innovation should diverge

📞 58:15 Marcelo’s experience at Hiya and lessons on scaling teams

💡 1:15:12 Marcelo’s final advice start small and learn fast

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