AI Accelerator Podcast

AI is transforming customer experience, but separating genuine business value from industry hype remains a challenge for leaders. In this episode of the AI Accelerator Podcast, Matt Zembruski speaks with Andy Bird, CPTO of IzzyLabs, about how AI is changing customer service, contact centers, and the way companies build technology platforms.
Drawing on more than two decades of experience in customer experience technology, Andy shares lessons from building cloud contact center platforms and developing AI-enabled solutions. He explains how AI can improve the customer journey, support human agents, streamline interactions, and make sophisticated customer experience technology more accessible to mid-market businesses.
The conversation also explores AI agents, knowledge bases, retrieval-augmented generation (RAG), real-time voice interactions, shared AI agents, and the architectural decisions that can determine whether an AI platform remains adaptable as technology evolves.
Andy also challenges the narrative that AI should simply replace people. He discusses the risks of overhyping automation, why human oversight remains essential, and how business leaders can identify practical opportunities for AI adoption without getting caught up in the latest headlines.
In This Episode, Andy Bird Discusses
How AI is transforming customer experience and contact center technology.
The three stages of the customer journey and where AI can make a difference.
Why an integrated approach to AI is more valuable than implementing disconnected tools.
How knowledge bases, RAG, and AI agents can improve customer interactions.
Why human involvement remains important in AI-powered customer service.
How shared AI agents can support multiple businesses while reducing costs.
The importance of natural conversations, low latency, and context-aware AI.
Why adaptable architecture matters in a rapidly evolving AI landscape.
What companies can learn from highly publicized attempts to replace workers with AI.
Where AI adoption may be underestimated, including legal and government applications.
How mid-market businesses can identify their biggest problems and evaluate AI solutions.
Why proof-of-concept projects can help leaders validate value before committing to a larger investment.
Chapters
00:00 Introduction to Andy Bird and AI in customer experience01:54 Andy’s career journey into product and technology leadership04:56 Building AI-powered customer experience platforms05:10 The three stages of the customer journey06:39 Why customer experience technology needs a holistic approach08:22 Introducing Izzy and customizable AI agents10:00 Identifying the ideal customers for AI-powered CX11:48 Self-service AI and keeping humans in the loop12:18 How business process outsourcing is evolving12:48 Understanding shared AI agents14:13 Improving the end-customer experience15:00 Natural conversations and knowledge-grounded AI19:02 Latency, transparency, and the AI customer experience22:20 Choosing AI models based on performance, cost, and use case23:43 Optimizing costs while improving customer satisfaction24:07 Why legacy systems and architectural decisions create challenges28:32 The most overhyped and underrated areas of AI29:16 Why replacing people with AI can backfire31:00 Why human support still matters in customer service32:06 Practical advice for business leaders adopting AI33:03 Start with your three biggest business problems34:21 Finding a trusted partner and validating AI solutions
💬 Andy Bird’s Key Insights
“My job is optimization of cost for my client.”
Andy’s perspective is that AI should improve the economics of customer experience while supporting better service, rather than simply shifting costs or eliminating jobs.
“AI is moving so rapidly you have to be able to adapt.”
The technology landscape changes quickly, making flexibility an important consideration when choosing models, vendors, and platform architectures.
“You identify your top three problems that you have.”
His advice to business leaders is to begin with the problems that matter most, research solutions that address them, and validate results before expanding adoption.
About Andy Bird
Andy Bird is the CPTO of IzzyLabs, a company developing AI-powered customer experience solutions through its Izzy platform. He brings more than two decades of experience in customer experience technology, product management, and platform development.
His work centers on customer satisfaction, practical AI adoption, cost optimization, and using technology to strengthen the capabilities of people.
Follow Andy Bird
Company: IzzyLabs
Website: https://izzyops.ai
Email: bird.andy@gmail.com
LinkedIn: https://www.linkedin.com/in/birdandy/
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

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 frameworks
The origins of Throughline and its AI-powered strategic execution platform
Connecting strategy, planning, and daily work
Why solving problems can be more effective than assigning tasks
Lessons from SpaceX's approach to rapid experimentation and execution
How psychological safety develops when teams are empowered to try things
Why AI agents need access to organizational goals, intentions, and context
The importance of human oversight in AI-powered workflows
How AI can identify dependencies, delays, risks, and strategic misalignment
Building roadmaps around outcomes instead of excessive task tracking
Practical ways to identify low-hanging fruit for AI adoption
Using AI to review subcontract agreements and save time
Helping 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

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 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 ReeveMelissa 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 ReeveWebsite: http://hyperadaptive.solutionsLinkedIn: https://www.linkedin.com/in/melissamreeve/Follow Matt ZembruskiWebsite: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778

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/andrecuenin/Follow Matt ZembruskiWebsite: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778

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-poweredChapters00:00 Introduction01:01 Meet Swathi Young: 20+ Years in Enterprise Technology02:00 From Software Developer to AI Strategist and CTO06:23 The AI Gap: Employee Productivity vs. Enterprise Transformation07:00 Showing Leaders the Art of the Possible07:27 Building Trust Through Responsible AI13:13 Why AI Models Aren't Usually the Biggest Bottleneck16:25 Deploying Generative AI for Citizen Services22:45 An Ethical AI Framework for Federal Agencies23:00 The Minimum Viable Approach to Responsible AI24:00 AI Strategy, ROI and Business Value25:00 Preparing Organizations for an AI-First FutureKey Learnings1. AI productivity is not the same as AI transformationEmployees 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 possibleOne 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 trustOrganizations 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 bottleneckSwathi 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 mattersSwathi 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 YoungSwathi 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 YoungWebsite: https://theaiblueprint.netLinkedIn: https://www.linkedin.com/in/swathiyoung/Follow Matt ZembruskiWebsite: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778

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 structured protocols, AI-augmented Scrum teams, and new approaches to organizational productivity.Follow Dr. Jeff SutherlandWebsite: https://scrumai.org/LinkedIn: https://www.linkedin.com/in/jeffsutherland/Follow Matt ZembruskiWebsite: https://leadingaiagility.comLinkedIn: https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.comPhone / Text / WhatsApp: +1 978-618-5778