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AI Accelerator Podcast

AI Accelerator Podcast

著者： Matt Zembruski
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The AI Accelerator Podcast is for business leaders, executives, entrepreneurs, and innovators who want to move beyond AI theory and into real-world results.


Hosted by Matt Zembruski, each episode features candid conversations with industry experts, technology leaders, founders, and AI practitioners who are transforming organizations through artificial intelligence.


From enterprise AI strategy and automation to leadership, innovation, and emerging technologies, we explore what works, what doesn't, and what leaders need to know to stay ahead.


If you're looking to turn AI into a competitive advantage, drive meaningful business outcomes, and prepare your organization for the future, this podcast is your roadmap.


New episodes every week.

© 2026 AI Accelerator Podcast マネジメント・リーダーシップ リーダーシップ 経済学
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  • AI, Scrum & the Future of AI-Augmented Teams | Dr. Jeff Sutherland | AI Accelerator Podcast

    AI, Scrum & the Future of AI-Augmented Teams | Dr. Jeff Sutherland | AI Accelerator Podcast
    2026/09/22
    What happens when you combine Scrum, artificial intelligence, and autonomous AI agents?In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Dr. Jeff Sutherland, co-creator of Scrum and Scrum@Scale and a signatory of the Agile Manifesto, to explore how Scrum is evolving for an AI-powered world.Jeff's background in AI goes back decades, including his work at the Stanford AI Lab with John McCarthy and his involvement with the MIT AI Lab, where he provided development space for Rodney Brooks as he worked on autonomous robotics.Today, Jeff is applying those lessons to AI-native teams through ScrumAI.org, exploring how humans and AI agents can work together through disciplined protocols, autonomous teams, and AI-augmented workflows.The conversation covers everything from the origins of Scrum to AI agents running software development work, AI-powered infrastructure management, and what business leaders need to consider as AI changes the way organizations operate.In This Episode, Dr. Jeff Sutherland Discusses:His early work at the Stanford AI Lab and connection to John McCarthyHis experience with Rodney Brooks and the development of autonomous roboticsHow lessons from robotics influenced the creation of ScrumWhy Jeff describes Scrum as a protocolWhy AI agents need structured processes and protocolsHow Scrum can be adapted for AI-augmented teamsThe role of AI agents in software developmentUsing AI to manage servers and technology infrastructureThe emerging model of AI-augmented Scrum teamsHow AI can amplify the capacity of a teamMeasuring AI contribution alongside human workThe changing roles of Product Owners and Scrum MastersWhat CEOs and executives need to do to adapt to AIHow organizations can begin building AI capabilitiesThe future of AI-native organizationsChapters00:00 - Welcome to the AI Accelerator Podcast01:01 - Meet Dr. Jeff Sutherland, Co-Creator of Scrum02:22 - Jeff's Early AI Journey at Stanford04:37 - From AI Databases to Autonomous Robotics06:27 - How Robotics Inspired Scrum07:27 - Scrum as a Protocol for AI Agents09:33 - Jeff's OpenClaw Project and AI Stack10:15 - Building AI-Powered Infrastructure Operations13:43 - ScrumAI and Scrum@Scale for AI Agents15:20 - Using AI to Amplify Teams16:23 - Measuring AI Contribution in Scrum17:41 - What Business Leaders Need to Know About AI19:45 - What Jeff Is Building at ScrumAI.org20:50 - How CEOs Can Start Adapting to AI22:12 - The AI Adoption Gap23:16 - How to Connect With Jeff24:12 - Closing ThoughtsThe transcript traces Jeff's AI background from Stanford and MIT through robotics and into his current work applying Scrum to AI agents.Key Learnings1. AI Doesn't Eliminate the Need for ProcessJeff argues that AI agents can make the same kinds of mistakes humans make, but they can make them much faster. His approach is to build the Scrum protocol directly into AI-driven workflows so that agents operate within defined structures.2. Scrum Can Be Applied to AI AgentsJeff describes Scrum as a protocol for creating autonomous, self-organizing teams that collaborate toward a goal. In his current work, he is exploring how that protocol can be applied to teams that include AI agents.3. AI Can Amplify Existing TeamsRather than simply replacing people, Jeff discusses using AI to augment teams and increase their capacity. He describes a goal of making a single team capable of producing the value of multiple traditional teams.4. AI Work Can Be Measured Within Existing Scrum PracticesJeff explains how his web team is tracking AI-generated work alongside human work on the Jira board, including measuring the story points completed by AI and humans during a sprint.5. Leaders Need to Build AI Capability NowJeff's advice to executives is centered on education and organizational adaptation. He describes the need for companies to identify people who understand AI deeply and begin building structured programs to help the organization adapt.6. AI Requires Continuous AdaptationA major theme of the conversation is the pace of change. Jeff argues that leaders need to continuously educate their teams and learn how to use new tools rather than treating AI as a temporary technology trend.💬 Jeff Sutherland's Key Insights“Simple rules generate really smart behavior really fast.”“Scrum is really a protocol.”“They really need Scrum more than the humans, because they screw up faster than humans.”“The goal is to augment the team and make the single team worth 10 teams.”About Dr. Jeff SutherlandDr. Jeff Sutherland is the co-creator of Scrum and Scrum@Scale and a signatory of the Agile Manifesto. His career in technology and AI spans decades, including early work at the Stanford AI Lab under John McCarthy and work connected to the MIT AI Lab and Rodney Brooks.Today, Jeff focuses on writing, consulting, and applying Scrum principles to AI through ScrumAI.org. His current work explores how humans and autonomous AI agents can operate together through ...
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    25 分
  • Is AI Killing Agile? AI, Product & the Future of Software Development with Katy Sherman

    Is AI Killing Agile? AI, Product & the Future of Software Development with Katy Sherman
    2026/09/15

    AI is changing how software is built, tested, managed, and delivered. But are we building better systems, or just building them faster?

    In this episode of the AI Accelerator Podcast, Matt Zembruski talks with Katy Sherman, VP of Technology at BeSmartee, about how AI is reshaping Agile, product management, engineering, and technology leadership. With more than 30 years in technology, from software engineer to leading Product, Engineering, Support, and Information Security for a SaaS lending platform, Katy explains why AI is transforming Agile rather than killing it, why documenting intent is now a core engineering discipline, and how leaders can use AI to improve both speed and decision quality.

    At the center of the conversation are two questions every software organization should be asking: What is the system supposed to be doing? And what is it actually doing?

    In this episode:

    • Is AI killing Agile, or transforming it into something better?

    • Why the Agile Manifesto may need a second look in the age of AI

    • Why humans struggle to keep context across large amounts of documentation

    • Documenting intent before AI writes the code

    • Why faster development isn't automatically better development

    • Using AI to connect goals, KPIs, requirements, acceptance criteria, tests, and code

    • Why Product and Engineering are converging, and why Product Operations matters

    • How BeSmartee adopts AI while balancing security, quality, and customer expectations

    Key quote: "What is the system supposed to be doing and what is it really doing?" – Katy Sherman

    Timestamps:

    00:00 Welcome to the AI Accelerator Podcast

    00:14 Meet Katy Sherman, VP of Technology at BeSmartee

    01:27 Katy's journey from software engineer to technology and product leader

    03:36 Why combining Product and Engineering creates better business outcomes

    04:32 AI adoption in fintech: security and managing risk

    05:25 Is AI killing Agile?

    06:30 The massive transformation happening in software development

    07:40 Reexamining the Agile Manifesto in the age of AI

    08:38 Why humans struggle with massive amounts of documentation

    09:43 From comprehensive documentation to code as the durable record

    10:49 What AI changes about comprehension, context, and software development

    12:05 AI-generated code and the need to document intent

    13:09 Why successful organizations will need stronger statements of intent

    14:38 How BeSmartee is adapting its operating model

    15:07 Balancing speed, quality, and customer expectations

    16:05 The two questions at the heart of software quality

    17:40 Why knowing what a system will actually do is difficult

    18:34 The danger of simply accelerating code production

    19:28 Using AI to document and validate intent

    20:52 What is the system supposed to do, and what is it actually doing?

    21:25 Separating code generation from context and decision-making

    22:01 Using AI to connect goals, KPIs, requirements, tests, and code

    22:48 Getting the benefits of AI without losing control

    23:19 Final thoughts with Katy Sherman

    Connect with Katy Sherman:

    Website: https://www.besmartee.com/

    LinkedIn: https://www.linkedin.com/in/katy-sherman/

    Connect with Matt Zembruski:

    Website: https://leadingaiagility.com

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

    Email: matt@leadingaiagility.com

    Phone / Text / WhatsApp: +1 978-618-5778

    About the AI Accelerator Podcast: Hosted by Matt Zembruski, the AI Accelerator Podcast explores how leaders are moving beyond AI hype and putting artificial intelligence to work in real organizations, with practical perspectives on AI adoption, digital transformation, leadership, innovation, and the future of work.

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    24 分
  • AI, Clean Data & the Future of Product Development | Johanna Rothman | AI Accelerator Podcast

    AI, Clean Data & the Future of Product Development | Johanna Rothman | AI Accelerator Podcast
    2026/09/09
    AI is everywhere, but more AI does not automatically mean better results.In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Johanna Rothman, known as the Pragmatic Manager, to explore what practical AI adoption really looks like inside product development organizations.Johanna Rothman has worked in software and product development since 1977 and has written 21 books covering management, leadership, project management, and product development. Her approach is straightforward: understand your current reality, focus on what actually works, and avoid adopting technology simply because everyone else is doing it.The conversation explores why clean organizational data may matter more than massive datasets, how AI can improve the flow of information across teams, and why organizations need to rethink how they make decisions in an increasingly AI-enabled workplace.Johanna also explains why teams should use AI together, rather than treating AI as an individual productivity tool. Effective collaboration, better information flow, shorter feedback loops, and faster decision-making can all play a role in creating more adaptable organizations.In This Episode, Johanna Rothman Discusses:◼️ Why AI hype makes pragmatic thinking more important than ever◼️ Why clean data can be more valuable than enormous datasets◼️ How organizations can use AI to uncover insights from their existing data◼️ The difference between cost accounting and measuring organizational flow◼️ Why value stream mapping can help companies understand how work actually moves◼️ How AI can improve the flow of information and features across an organization◼️ Why product development teams need to use AI collaboratively◼️ The concept of flow debt and how accelerating one piece of work can affect everything else◼️ Why organizations often need fewer features delivered faster rather than more features◼️ How AI can support better decision-making without removing human judgment◼️ Why important business decisions should be treated as experiments◼️ How shorter roadmaps and faster iterations can make organizations more adaptable◼️ Why finance, HR, sales, and marketing also need to become more adaptable◼️ How leaders can think differently about AI adoption and organizational change◼️ The importance of protecting sensitive and proprietary company data when using AIChapters00:00 Welcome to the AI Accelerator Podcast00:30 Introducing Johanna Rothman, the Pragmatic Manager01:30 Johanna's journey through product development and management03:00 Cutting through the AI hype05:00 Clean data, small models, and practical AI07:50 What does dirty organizational data look like?08:20 Cost accounting vs. flow and throughput09:00 Using AI with value stream mapping10:00 Measuring work, wait time, WIP, throughput, and cycle time11:00 AI readiness and organizational assessments12:00 Why organizations need to understand their current reality14:20 Data quality and using AI for better business insights15:15 Understanding flow debt15:40 Why collaborative teams should use AI together17:00 Improving information flow across organizations18:30 AI, decision-making, and organizational effectiveness19:50 Why valuable decisions should be treated as experiments21:00 Johanna's writing, books, and pragmatic management philosophy23:50 Building adaptable organizations with shorter roadmaps25:00 Why organizations need to experiment and pivot26:00 The future of AI and organizational adaptabilityKey Learnings✔ AI adoption should begin with understanding the organization's real problems✔ Clean, useful data can be more valuable than simply having more data✔ AI can help organizations analyze flow, throughput, cycle time, and work-in-progress✔ Product development is fundamentally collaborative work✔ Teams can get more value from AI when they use it together✔ Faster information flow can lead to better and faster decisions✔ Organizations often need fewer features delivered more effectively✔ Important decisions should remain flexible and be treated as experiments✔ Shorter roadmaps allow organizations to respond to changing conditions✔ AI should strengthen human decision-making rather than eliminate it✔ Organizations need to build a culture of experimentation and adaptability💬 Johanna's Key Insights“The hype cycle is unbelievable.”“What we need is effective flow of information and flow of features.”“The faster you can make that flow of information move, the faster people can then make better decisions.”“The most valuable decisions need to be able to be thought of as experiments.”About Johanna RothmanJohanna Rothman, known as the Pragmatic Manager, is an author, consultant, and expert in product development, project management, program management, portfolio management, leadership, and modern management. She has written 21 books along with hundreds of articles and thousands of blog posts....
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    26 分
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