Episodi

  • AI-Powered Strategic Execution: Connecting Strategy to Results | Cliff Berg | AI Accelerator Podcast

    AI-Powered Strategic Execution: Connecting Strategy to Results | Cliff Berg | AI Accelerator Podcast
    Oct 20 2026
    How can organizations use AI to close the gap between business strategy and the work people actually do every day? In this episode of the AI Accelerator Podcast, Matt Zembruski speaks with Cliff Berg, managing partner of Agile 2 Academy, founder of the Agile 2 initiative, and architect of the Throughline AI strategic execution platform. With 30 years of experience studying how organizations operate, Cliff shares why traditional planning and task management can get in the way of agility, and how AI can help leaders connect strategy, goals, and execution. The conversation explores how AI agents become more effective when they understand an organization's intentions, why leaders should give teams problems to solve rather than simply assigning tasks, and how businesses can introduce AI while keeping humans involved in important decisions. In This Episode, Cliff Berg Discusses: Why organizational agility depends on leadership behavior, not just frameworksThe origins of Throughline and its AI-powered strategic execution platformConnecting strategy, planning, and daily workWhy solving problems can be more effective than assigning tasksLessons from SpaceX's approach to rapid experimentation and executionHow psychological safety develops when teams are empowered to try thingsWhy AI agents need access to organizational goals, intentions, and contextThe importance of human oversight in AI-powered workflowsHow AI can identify dependencies, delays, risks, and strategic misalignmentBuilding roadmaps around outcomes instead of excessive task trackingPractical ways to identify low-hanging fruit for AI adoptionUsing AI to review subcontract agreements and save timeHelping mid-market companies connect AI adoption to long-term business strategy Chapters 00:00 Introduction to the AI Accelerator Podcast 00:56 Meet Cliff Berg: Agile 2 and Throughline AI 02:28 Why Cliff Built an AI-Powered Execution Platform 03:45 Connecting Strategy, Planning, and Execution 04:51 Why Traditional Task Planning Can Get in the Way 05:46 The Problem-Centric Approach to Work 06:43 What Organizations Can Learn From SpaceX 08:02 Empowering Teams and Building Psychological Safety 09:54 Giving AI Agents the Context They Need 10:30 Understanding Organizational Intentions 11:56 Why Strategic Context Matters for AI Deployments 13:38 How Throughline Agents Understand and Prioritize Work 14:52 Keeping Humans in the Loop 15:45 Why AI Output Quality Matters More Than Speed 17:50 Connecting Strategy to Everyday Execution 18:28 Defining Mission, Vision, and Strategy 19:58 Building Flexible Roadmaps and Goals 21:37 Using AI to Identify Dependencies and Misalignment 22:20 Advice for Business Leaders Starting With AI 23:44 Finding Practical AI Use Cases 24:11 Automating Subcontract Reviews With AI 25:30 Moving From Quick Wins to Strategic AI Adoption 26:18 How to Connect With Cliff Berg 💬 Cliff Berg's Key Insights "The tools we have make it really hard to do all this." "What we need is a tool that actually creates this line of sight from strategy through planning and execution." "The behavioral norm there is to give people problems to solve, not tasks to do." "What we want is good output. We want aligned output." "The AI cannot say that's done." About Cliff Berg Cliff Berg is the managing partner of Agile 2 Academy, founder of the Agile 2 movement, and architect of the Throughline AI agent strategic execution platform. He has spent approximately 30 years studying how organizations operate and helping businesses improve agility, planning, and execution. Cliff previously co-founded Digital Focus, a technology company that grew from two people to 200 employees in five years before being acquired in 2006. In 2019, he launched the Agile 2 initiative and became the lead author of Agile 2: The Next Iteration of Agile, examining why conventional Agile approaches can fail to produce genuine organizational agility. Cliff has led more than 10 Agile and DevOps transformations, authored six books, and worked with executives and delivery teams to address organizational and technical challenges. His work combines software engineering, systems thinking, leadership, and AI to help organizations connect strategy with execution. Follow Cliff Berg Website: https://www.throughlineai.net LinkedIn: https://www.linkedin.com/in/cliffberg/ 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
    Mostra di più Mostra meno
    27 min
  • AI-Native Transformation: The 5 Stages of Becoming Hyperadaptive | Melissa Reeve | AI Accelerator Podcast

    AI-Native Transformation: The 5 Stages of Becoming Hyperadaptive | Melissa Reeve | AI Accelerator Podcast
    Oct 13 2026
    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 adaptiveChapters00:00 Introduction00:56 Meet Melissa Reeve, Creator of the Hyperadaptive Model01:18 The Five Stages of Becoming AI-Native02:02 Melissa's Journey From Japan and Toyota to Agile Transformation06:00 From Process Excellence to AI Transformation12:31 Breaking Organizational Silos With AI13:00 Building AI Impact Hubs13:24 Upskilling and Reskilling for an AI-Plus-Human Future21:29 Why AI Success Requires Doubling Down on What Makes Us Human26:20 A Real-World AI Transformation Story: Greenfield Savings Bank27:00 Moving From AI Technology to Human-Centered TransformationKey Learnings1. Becoming AI-native requires more than adding AI toolsMelissa'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 layerOne 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 boundariesAI 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 transitionMelissa 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 pointsThe 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 moreA 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 ...
    Mostra di più Mostra meno
    31 min
  • AI, Revenue Growth & the Future of AI-Powered Companies | Andre Cuenin | AI Accelerator Podcast

    AI, Revenue Growth & the Future of AI-Powered Companies | Andre Cuenin | AI Accelerator Podcast
    Oct 6 2026
    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 behindChapters00:00 Introduction01:03 Meet Andre Cuenin, CRO at SolarWinds01:15 What AI Is Really Doing to Jobs Today06:14 Applying AI to Optimize Existing Workflows06:37 Using an AI Agent to Prepare for Customer Meetings07:08 The Optimization Path for AI10:50 Connecting AI to the Breadth of Enterprise Data11:15 Data Quality and Building Better AI Workflows14:58 Andre's Recommendations for Business Leaders19:03 AI as a Major Technology Shift19:48 Two Types of Companies in the Future20:00 Optimization vs. New Business Opportunities21:00 Staying Competitive in an AI-Driven MarketKey Learnings1. Start with optimizationAndre 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 organizationOne 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 matterConnecting 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 optimizationAndre 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 advantageAndre 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 CueninAndre 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 CueninWebsite: https://www.solarwinds.com/LinkedIn: https://www.linkedin.com/in/...
    Mostra di più Mostra meno
    21 min
  • AI Strategy, Responsible AI & Moving from Pilot to Production | Swathi Young | AI Accelerator Podcast

    AI Strategy, Responsible AI & Moving from Pilot to Production | Swathi Young | AI Accelerator Podcast
    Sep 29 2026
    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-powered Chapters 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 ...
    Mostra di più Mostra meno
    28 min
  • 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
    Sep 22 2026
    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 ...
    Mostra di più Mostra meno
    25 min
  • 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
    Sep 15 2026

    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.

    Mostra di più Mostra meno
    24 min
  • 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
    Sep 9 2026
    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....
    Mostra di più Mostra meno
    26 min
  • AI-Powered Service Operations: Driving ROI and Adoption in the Service Lifecycle | Ashok Kartham

    AI-Powered Service Operations: Driving ROI and Adoption in the Service Lifecycle | Ashok Kartham
    Aug 31 2026
    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 AIChapters00:00 Welcome to the AI Accelerator Podcast00:13 Introducing Ashok Kartham & Circuitry.ai00:45 Ashok’s 25+ years in service and enterprise software07:47 Using AI with proprietary service and product data14:23 What AI adoption and implementation really look like17:18 Connecting AI pricing to service outcomes22:47 Advice for business leaders adopting AI23:00 Protecting proprietary data in AI implementations24:00 Building the right AI architecture for your business25:00 Starting small with AI and improving business processes26:00 Using AI to reduce friction in service operations27:00 Final thoughts on AI adoption and ROIKey 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 KarthamWebsite: https://circuitry.aiLinkedIn: https://www.linkedin.com/in/ashokkartham/About Ashok KarthamAshok 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.aiWebsite: https://circuitry.aiLinkedIn: https://www.linkedin.com/in/ashokkartham/Follow Matt ZembruskiWebsite: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778
    Mostra di più Mostra meno
    25 min