AI Accelerator Podcast

Matt Zembruski
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  • Entrepreneurship

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.

  1. 1d ago

    AI-Native Transformation: The 5 Stages of Becoming Hyperadaptive | Melissa Reeve | AI Accelerator Podcast

    What does it really take to become an AI-native organization? In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Melissa Reeve, creator of the Hyperadaptive Model and author of Hyperadaptive: Re-wiring the Enterprise to Become AI-Native, to explore how organizations can move beyond AI pilots and build the systems, governance, and human capabilities needed to continuously adapt. Drawing on 25 years of experience as an executive and Agile thought leader, including serving as the first VP of Marketing at Scaled Agile and co-founding the Agile Marketing Alliance, Melissa shares a practical, human-centered approach to enterprise AI transformation. The conversation explores the five stages of becoming AI-native, dynamic governance, AI Impact Hubs, the activation layer that can help organizations turn AI experiments into results, and why the companies succeeding with AI may be the ones that double down on what makes humans uniquely valuable. In This Episode, Melissa Reeve Discusses: The Hyperadaptive Model for AI transformationThe five stages of becoming an AI-native organizationWhy AI pilots often fail to translate into enterprise resultsThe importance of an organizational activation layerDynamic governance in an AI-powered enterpriseHow AI changes traditional organizational boundariesBuilding AI Impact Hubs across departmentsHow HR, Finance, and other functions may evolve alongside AIUpskilling and reskilling employees for AI-plus-human rolesLessons from Greenfield Savings Bank's AI transformationWhy AI adoption is more than a technology or licensing decisionHow leaders can identify pain points and match them with AI solutionsWhy successful AI organizations may need to double down on human capabilitiesRewiring traditional organizations to become more adaptiveChapters 00:00 Introduction 00:56 Meet Melissa Reeve, Creator of the Hyperadaptive Model 01:18 The Five Stages of Becoming AI-Native 02:02 Melissa's Journey From Japan and Toyota to Agile Transformation 06:00 From Process Excellence to AI Transformation 12:31 Breaking Organizational Silos With AI 13:00 Building AI Impact Hubs 13:24 Upskilling and Reskilling for an AI-Plus-Human Future 21:29 Why AI Success Requires Doubling Down on What Makes Us Human 26:20 A Real-World AI Transformation Story: Greenfield Savings Bank 27:00 Moving From AI Technology to Human-Centered Transformation Key Learnings 1. Becoming AI-native requires more than adding AI tools Melissa's Hyperadaptive Model focuses on the organizational transformation required to become AI-native. The challenge isn't simply choosing an AI model or purchasing licenses. Organizations need to rethink how their processes, people, governance, and operating systems work together as AI becomes part of everyday work. 2. AI transformation requires an activation layer One of the central ideas discussed in the episode is the need for an activation layer between AI pilots and actual business results. Organizations can experiment with AI, run pilots, and demonstrate impressive technology. But without the systems and organizational mechanisms required to activate those capabilities, pilots can remain disconnected from meaningful outcomes. 3. AI breaks down traditional organizational boundaries AI doesn't necessarily stay inside traditional departmental boxes. As Melissa explains, people increasingly need to work across what she calls their "adjacent competencies." Instead of thinking only about what HR does or what Finance does, organizations need to ask how AI is changing the work, roles, and capabilities within each function. 4. AI Impact Hubs can help organizations manage this transition Melissa describes the concept of AI Impact Hubs as a way to understand how AI and automation are affecting different parts of an organization. These hubs can examine questions such as: How is automation changing work within Finance?How are roles evolving?Can frontline employees be upskilled or reskilled?What new AI-plus-human roles could emerge?How can organizations support employees through the transition?This creates a more systematic approach to organizational adaptation. 5. AI adoption should start with real pain points The Greenfield Savings Bank example illustrates a practical approach to AI adoption. Rather than treating AI as simply a technology rollout, the CEO selected one or two people from each department to identify significant pain points and then match an AI solution to those problems. The shift was from focusing primarily on licensing and deploying technology to focusing on the people, problems, and outcomes that AI could actually improve. 6. The future isn't only about AI doing more A major theme of the conversation is that organizations succeeding with AI may be the ones that double down on what makes humans most valuable. As automation changes tasks and workflows, organizations need to think carefully about what humans should continue doing, what new capabilities employees need, and how people and AI can work together. 💬 Melissa Reeve's Key Insights "The future belongs to those who are willing to rewire." "There's a real mindset shift that needs to happen." "How is automation impacting the folks in Finance? What are those roles evolving into?" "The goal of this Impact Hub... is looking at the impact." "The companies succeeding with AI are the ones that are doubling down on what makes us the most human." About Melissa Reeve Melissa Reeve is the creator of the Hyperadaptive Model and author of Hyperadaptive: Re-wiring the Enterprise to Become AI-Native. She brings more than 25 years of experience as an executive and Agile thought leader. Her work includes studying the Toyota Production System in Japan, serving as the first VP of Marketing at Scaled Agile, and co-founding the Agile Marketing Alliance. Melissa developed the Hyperadaptive Model to help organizations combine process excellence, systems thinking, and the human side of AI integration. Her research-backed approach focuses on helping companies move from stalled AI pilots toward becoming truly AI-native and continuously adaptive organizations. She lives in Boulder, Colorado, with her husband, dogs, and chickens. Follow Melissa Reeve Website: http://hyperadaptive.solutions LinkedIn: https://www.linkedin.com/in/melissamreeve/ Follow Matt Zembruski Website: https://leadingaiagility.com LinkedIn: https://www.linkedin.com/in/mattzembruski/ Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

    AI-Native Transformation: The 5 Stages of Becoming Hyperadaptive | Melissa Reeve | AI Accelerator Podcast

  2. Oct 6

    AI, Revenue Growth & the Future of AI-Powered Companies | Andre Cuenin | AI Accelerator Podcast

    How can companies move beyond using AI for productivity and start using it to fundamentally change how they operate, compete, and grow? In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Andre Cuenin, Chief Revenue Officer at SolarWinds, to explore how AI is changing enterprise workflows, customer preparation, data utilization, and the future of business. With more than 20 years of experience driving revenue at technology companies including Veracode, Rapid7, Cherwell Software, Stratavia, and CA, Andre brings a practical revenue and leadership perspective to the AI transformation happening across organizations. The conversation explores the difference between simply optimizing existing processes and using AI to create entirely new ways of solving business problems. In This Episode, Andre Cuenin Discusses: How AI can optimize existing business workflowsUsing AI to prepare for customer meetingsHow AI agents can connect information across CRM, finance, and support systemsThe importance of data breadth and quality when implementing AIWhy organizations need to think beyond individual employee productivityHow leaders should approach AI adoption and transformationThe difference between AI optimization and AI-driven opportunityWhy AI may represent one of the most significant technology shifts of our lifetimeHow companies can stay competitive as AI adoption acceleratesWhat separates organizations that successfully leverage AI from those that risk being left behind Chapters 00:00 Introduction 01:03 Meet Andre Cuenin, CRO at SolarWinds 01:15 What AI Is Really Doing to Jobs Today 06:14 Applying AI to Optimize Existing Workflows 06:37 Using an AI Agent to Prepare for Customer Meetings 07:08 The Optimization Path for AI 10:50 Connecting AI to the Breadth of Enterprise Data 11:15 Data Quality and Building Better AI Workflows 14:58 Andre's Recommendations for Business Leaders 19:03 AI as a Major Technology Shift 19:48 Two Types of Companies in the Future 20:00 Optimization vs. New Business Opportunities 21:00 Staying Competitive in an AI-Driven Market Key Learnings 1. Start with optimization Andre describes one practical path for AI adoption as optimization: take something the organization already does and use AI to perform it more effectively and efficiently. 2. AI agents can connect information across the organization One of the biggest opportunities comes from connecting AI to the information already distributed throughout an organization. Customer information may exist across multiple systems. An AI agent can help bring that information together so employees don't have to manually search through different platforms to understand the full customer story. 3. Data breadth and quality matter Connecting an AI system to enterprise data is only part of the challenge. Organizations also need to consider whether the AI has access to the breadth of data that actually matters and how they can perform appropriate quality checks on that information. Better AI outcomes require organizations to think carefully about their underlying data. 4. AI creates opportunities beyond optimization Andre describes a distinction between using AI to do existing work better and using AI to solve problems in completely different ways. Optimization can make existing processes faster and more efficient. The larger opportunity may come from asking: What could we do differently if AI fundamentally changed how this problem was solved? 5. AI could reshape competitive advantage Andre believes AI represents one of the most significant technology shifts many people will experience during their lifetime. For business leaders, the challenge is not simply understanding AI. It is figuring out how to leverage it while maintaining competitiveness.mAs AI adoption accelerates, companies that successfully integrate AI into both optimization and new opportunities may gain an advantage over organizations that fail to adapt. 💬 Andre Cuenin's Key Insights "You do what you have done before, but more effectively, more efficiently." "A great example of doing something I did before, but much more effectively and efficiently." "How can I leverage it and also make sure I stay competitive?" "There’s gonna be two types of companies in the future." About Andre Cuenin Andre Cuenin serves as Chief Revenue Officer at SolarWinds, where he oversees revenue generation relating to the company's observability, database, and service management solutions. He brings more than 20 years of experience driving revenue growth strategies at technology companies including Veracode, Rapid7, Cherwell Software, Stratavia, and CA. Throughout his career, Andre has focused on building cultures of trust, candor, teamwork, and sustainable customer outcomes. He holds a Bachelor of Computer Science from the University of Applied Sciences and Arts of Western Switzerland and an MBA from Strathclyde University in Glasgow. Follow Andre Cuenin Website: https://www.solarwinds.com/ LinkedIn: https://www.linkedin.com/in/andrecuenin/ Follow Matt Zembruski Website: https://leadingaiagility.com LinkedIn: https://www.linkedin.com/in/mattzembruski/ Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

    AI, Revenue Growth & the Future of AI-Powered Companies | Andre Cuenin | AI Accelerator Podcast

  3. Sep 29

    AI Strategy, Responsible AI & Moving from Pilot to Production | Swathi Young | AI Accelerator Podcast

    What does it take to move AI beyond experimentation and into real business value? In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Swathi Young, technology executive, AI strategist, and award-winning CTO, to explore what organizations need to do to turn AI pilots into production-ready solutions. With more than 20 years of experience in enterprise technology and over 100 AI, data, and transformation initiatives across healthcare, finance, transportation, and the public sector, Swathi brings a practical perspective on AI strategy, responsible AI, business value, and organizational transformation. The conversation also explores why employee productivity is advancing faster than enterprise-wide AI workflows, how organizations can build trust around AI, and what responsible AI looks like for companies that don't have dedicated compliance or AI leadership roles. In This Episode, Swathi Young Discusses: Why AI productivity gains aren't always translating into enterprise-wide transformationMoving from AI pilots to production and measurable business impactThe "art of the possible" for AI in different industriesWhy the AI model itself is rarely the biggest organizational bottleneckBuilding responsible AI without breaking customer or citizen trustLessons from deploying a generative AI citizen services assistant for the City of Virginia BeachUsing AWS Bedrock and generative AI in a production environmentThe economics of AI, including cost, ROI, and business valueAI transformation in government and the public sectorAn ethical AI framework developed for U.S. federal agenciesThe minimum viable approach to responsible AI for midsize organizationsPreparing the workforce for an AI-first futureHow leaders can move from being AI-curious to becoming AI-poweredChapters 00:00 Introduction 01:01 Meet Swathi Young: 20+ Years in Enterprise Technology 02:00 From Software Developer to AI Strategist and CTO 06:23 The AI Gap: Employee Productivity vs. Enterprise Transformation 07:00 Showing Leaders the Art of the Possible 07:27 Building Trust Through Responsible AI 13:13 Why AI Models Aren't Usually the Biggest Bottleneck 16:25 Deploying Generative AI for Citizen Services 22:45 An Ethical AI Framework for Federal Agencies 23:00 The Minimum Viable Approach to Responsible AI 24:00 AI Strategy, ROI and Business Value 25:00 Preparing Organizations for an AI-First Future Key Learnings 1. AI productivity is not the same as AI transformation Employees can already use AI to write emails, summarize documents, and accelerate individual tasks. But Swathi points out that end-to-end enterprise workflows are not changing at the same pace. The opportunity for leaders is to move beyond individual productivity and examine how AI can fundamentally improve the workflows that run the organization. 2. Leaders need to understand the art of the possible One of the questions Swathi hears most often from business and public-sector leaders is essentially: What could AI actually do in my organization? Understanding the possibilities requires looking at specific use cases within an organization's industry, workflows, customers, and operational environment. 3. Responsible AI is about maintaining trust Organizations aren't only asking what AI can do. They're also asking how they can use it responsibly without damaging the trust they've built with customers and citizens over many years. Responsible AI therefore needs to be considered alongside strategy, implementation, governance, and the customer experience. 4. The AI model isn't always the bottleneck Swathi discusses a common challenge in enterprise AI: organizations can have access to increasingly capable models, but still struggle to translate those capabilities into production outcomes. The surrounding organizational infrastructure, processes, data, governance, and workflows can be just as important as the model itself. 5. Production deployment matters Swathi highlights the launch of a citizen services virtual assistant for the City of Virginia Beach as a project she is particularly proud of. The assistant, known as BeachBoard, uses generative AI and AWS Bedrock to help answer questions about city services, including areas such as utility payments and employment opportunities. For Swathi, an important part of the achievement was getting the system into production rather than leaving it as an AI pilot. About Swathi Young Swathi Young is a technology executive, AI strategist, and award-winning CTO with more than 20 years of experience leading digital transformation across the public and private sectors. She has delivered more than 100 AI, data, and enterprise technology initiatives across healthcare, finance, transportation, and government, helping organizations move from AI experimentation toward measurable business impact. Swathi advises government agencies and enterprises on AI strategy, responsible AI, data modernization, and emerging technologies. She is also the co-author of an Ethical AI framework developed for the U.S. government and created The AI Blueprint, a strategy workshop designed to help leaders move from AI-curious to AI-powered. A graduate of Georgetown University's McDonough School of Business Executive MBA program, Swathi is passionate about making AI practical, trustworthy, and accessible. 💬 Swathi Young's Key Insights "The actual end-to-end workflows in an enterprise... that's not changing as quickly." "What is the art of the possible?" "How could we use this AI responsibly?" "Very proud that this project got deployed to production and did not stay in the pilot phase." Follow Swathi Young Website: https://theaiblueprint.net LinkedIn: https://www.linkedin.com/in/swathiyoung/ Follow Matt Zembruski Website: https://leadingaiagility.com LinkedIn: https://www.linkedin.com/in/mattzembruski/ Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

    AI Strategy, Responsible AI & Moving from Pilot to Production | Swathi Young | AI Accelerator Podcast

  4. Sep 22

    AI, Scrum & the Future of AI-Augmented Teams | Dr. Jeff Sutherland | AI Accelerator Podcast

    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 organizations Chapters 00:00 - Welcome to the AI Accelerator Podcast 01:01 - Meet Dr. Jeff Sutherland, Co-Creator of Scrum 02:22 - Jeff's Early AI Journey at Stanford 04:37 - From AI Databases to Autonomous Robotics 06:27 - How Robotics Inspired Scrum 07:27 - Scrum as a Protocol for AI Agents 09:33 - Jeff's OpenClaw Project and AI Stack 10:15 - Building AI-Powered Infrastructure Operations 13:43 - ScrumAI and Scrum@Scale for AI Agents 15:20 - Using AI to Amplify Teams 16:23 - Measuring AI Contribution in Scrum 17:41 - What Business Leaders Need to Know About AI 19:45 - What Jeff Is Building at ScrumAI.org 20:50 - How CEOs Can Start Adapting to AI 22:12 - The AI Adoption Gap 23:16 - How to Connect With Jeff 24:12 - Closing Thoughts The transcript traces Jeff's AI background from Stanford and MIT through robotics and into his current work applying Scrum to AI agents. Key Learnings 1. AI Doesn't Eliminate the Need for Process Jeff 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 Agents Jeff 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 Teams Rather 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 Practices Jeff 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 Now Jeff'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 Adaptation A 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 Sutherland Dr. 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 structured protocols, AI-augmented Scrum teams, and new approaches to organizational productivity. Follow Dr. Jeff Sutherland Website: https://scrumai.org/LinkedIn: https://www.linkedin.com/in/jeffsutherland/Follow Matt Zembruski Website: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778

    AI, Scrum & the Future of AI-Augmented Teams | Dr. Jeff Sutherland | AI Accelerator Podcast

  5. Sep 15

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

    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.

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

  6. Sep 9

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

    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 AI Chapters 00:00 Welcome to the AI Accelerator Podcast 00:30 Introducing Johanna Rothman, the Pragmatic Manager 01:30 Johanna's journey through product development and management 03:00 Cutting through the AI hype 05:00 Clean data, small models, and practical AI 07:50 What does dirty organizational data look like? 08:20 Cost accounting vs. flow and throughput 09:00 Using AI with value stream mapping 10:00 Measuring work, wait time, WIP, throughput, and cycle time 11:00 AI readiness and organizational assessments 12:00 Why organizations need to understand their current reality 14:20 Data quality and using AI for better business insights 15:15 Understanding flow debt 15:40 Why collaborative teams should use AI together 17:00 Improving information flow across organizations 18:30 AI, decision-making, and organizational effectiveness 19:50 Why valuable decisions should be treated as experiments 21:00 Johanna's writing, books, and pragmatic management philosophy 23:50 Building adaptable organizations with shorter roadmaps 25:00 Why organizations need to experiment and pivot 26:00 The future of AI and organizational adaptability Key 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 Rothman Johanna 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. Her work focuses on helping leaders and teams understand their current reality, improve collaboration, and focus on outcomes that matter. Follow Johanna Rothman Website: Rothman Consulting Group LinkedIn: Johanna Rothman LinkedIn YouTube: Johanna Rothman YouTube Links: Johanna Rothman Linktree Follow Matt Zembruski Website: Leading AI Agility LinkedIn: Matt Zembruski LinkedIn Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

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

  7. Aug 31

    AI-Powered Service Operations: Driving ROI and Adoption in the Service Lifecycle | Ashok Kartham

    AI is transforming service operations, but the real opportunity is not simply adding AI to existing systems. It is using AI to improve service outcomes, empower the workforce, and create measurable business value. In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Ashok Kartham, CEO of Circuitry.ai, a serial entrepreneur with more than 25 years of experience building technology and enterprise software companies. Ashok shares how Circuitry.ai is using AI to support field technicians, contact center agents, manufacturers, and dealer networks. He explains why proprietary service data is so important, how organizations can approach AI adoption, and why AI solutions should be connected to tangible business outcomes. In this episode, Ashok reveals: ◼️ How AI can empower field service technicians and service workforces ◼️ Why proprietary product and service data is critical for enterprise AI ◼️ How AI can improve service outcomes across complex industries ◼️ Why AI adoption requires customization and integration ◼️ How companies can connect AI investments to measurable ROI ◼️ Why Circuitry.ai ties pricing to tangible service decisions ◼️ How manufacturers and dealer networks can approach AI adoption ◼️ Why companies should start with specific business processes ◼️ How AI can transform warranty, repair, and service workflows ◼️ What business leaders should consider before deploying AI Chapters 00:00 Welcome to the AI Accelerator Podcast 00:13 Introducing Ashok Kartham & Circuitry.ai 00:45 Ashok’s 25+ years in service and enterprise software 07:47 Using AI with proprietary service and product data 14:23 What AI adoption and implementation really look like 17:18 Connecting AI pricing to service outcomes 22:47 Advice for business leaders adopting AI 23:00 Protecting proprietary data in AI implementations 24:00 Building the right AI architecture for your business 25:00 Starting small with AI and improving business processes 26:00 Using AI to reduce friction in service operations 27:00 Final thoughts on AI adoption and ROI Key Learnings ✔ AI can empower service professionals rather than simply automate their jobs ✔ Proprietary product knowledge can create significant value for specialized AI applications ✔ Service manuals, diagnostic information, parts catalogs, and other specialized data can be critical AI inputs ✔ AI adoption requires thoughtful implementation, customization, and integration ✔ AI investments should connect to measurable business outcomes ✔ Pricing AI around tangible business transactions can make its value easier to understand ✔ Manufacturers and dealer networks need practical strategies for AI adoption ✔ Companies should identify specific processes where AI can reduce cost or friction ✔ Starting with a smaller, manageable use case can help organizations build momentum ✔ Successful AI adoption requires focusing on the business problem, not simply the technology 💬 Ashok’s Most Powerful Quotes “AI can really have a very beneficial impact on the service outcomes.” “We wanted to tie our pricing to the service decisions that we are augmenting or automating.” “AI is really about how do we train AI on very specific, unique and proprietary data.” “Just isolate that process and then try to deconstruct it with AI in mind.” “Start small and pick a process that if you break it, it's not going to be the end of the world.” Follow Ashok Kartham Website: https://circuitry.ai LinkedIn: https://www.linkedin.com/in/ashokkartham/ About Ashok Kartham Ashok Kartham is a successful serial entrepreneur with more than 25 years of experience as CEO of technology and enterprise software companies. He is the CEO of Circuitry.ai, where he focuses on using AI to empower service workforces and improve service outcomes. Ashok previously founded Mize, which merged with Syncron in 2021 to create a global leader in Connected Service Experience. He also founded 4CS, a leading warranty and service lifecycle management company that was acquired by Parametric Technology Corporation (PTC) in 2011. With a degree in Computer Science and extensive experience building technology companies, Ashok brings deep expertise in enterprise software, service operations, AI adoption, and business transformation. Follow Circuitry.ai Website: https://circuitry.ai LinkedIn: https://www.linkedin.com/in/ashokkartham/ Follow Matt Zembruski Website: https://leadingaiagility.com LinkedIn: https://www.linkedin.com/in/mattzembruski/ Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

    AI-Powered Service Operations: Driving ROI and Adoption in the Service Lifecycle | Ashok Kartham

  8. Aug 24

    AI Adoption in China vs. the United States | Erick Watson | AI Accelerator Podcast

    AI adoption isn't happening the same way everywhere. In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Erick Watson, fractional CPO at SproutVest, to explore the differences between AI adoption in China and the United States. Erick shares his perspective on how cultural, economic, and business differences influence AI adoption, where the U.S.-China AI conversation gets misunderstood, and how companies should think about building AI systems that create a real competitive advantage. The conversation also explores open-source AI models, proprietary data, AI strategy, and a practical approach for companies looking to begin their AI transformation. In this episode, we discuss: ◼️ AI adoption patterns in China vs. the United States ◼️ Why AI adoption looks different across markets ◼️ The most overhyped and underreported stories in U.S.-China AI ◼️ When companies should build proprietary AI solutions ◼️ When businesses should simply use existing AI models ◼️ Open-source AI and the changing global AI landscape ◼️ Data, security, and AI implementation ◼️ How companies can start small with AI ◼️ Reinventing business processes with AI ◼️ Turning frontier AI research into products people actually use Chapters 00:00 Welcome to the AI Accelerator Podcast 01:17 Erick’s journey into computer science and technology 07:36 AI adoption in China vs. the United States 14:37 Build proprietary AI or use existing models? 22:40 The most overhyped and underreported U.S.-China AI stories 25:53 How businesses should start their AI journey Key Learnings ✔ AI adoption needs to be understood within its cultural and economic context. ✔ Companies should determine whether AI can create a genuine competitive advantage before building proprietary systems. ✔ Existing AI models can be the better choice for common business problems. ✔ Open-source models are becoming increasingly important in the AI landscape. ✔ Businesses should start AI adoption with a manageable process rather than trying to transform everything at once. ✔ The best AI opportunities often come from eliminating friction in existing business processes. 💬 Erick’s Key Insights “There's two decisions you have to make right up front.” “Is that solution going to be unique to my business?” “Start small.” “Reinvent it from the ground up.” About Erick Watson Erick Watson helps AI labs, Web3 protocols, and venture studios turn frontier research into products people use. Through SproutVest, he works as a fractional CPO, embedding with founding teams for 6–12 months to define product lines, build go-to-market strategies, and ship products. Follow Erick Watson Website: https://sproutvest.com LinkedIn: https://www.linkedin.com/company/sproutvest X: https://x.com/sproutvest Bluesky: https://bsky.app/profile/sproutvest.bsky.social Telegram: https://t.me/ErickWa GitHub: https://github.com/ErickWa Follow Matt Zembruski Website: https://leadingaiagility.com LinkedIn: https://www.linkedin.com/in/mattzembruski/ Email: matt@leadingaiagility.com Phone / Text / WhatsApp: +1 978-618-5778

    AI Adoption in China vs. the United States | Erick Watson | AI Accelerator Podcast
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About

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.

Information

  • Creator
    Matt Zembruski
  • Years Active
    2026
  • Episodes
    36
  • Rating
    Explicit
  • Copyright
    © 2026 AI Accelerator Podcast
  • Show Website
    AI Accelerator Podcast