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AI Accelerator Podcast: Daily Summaries Delivered
by Matt Zembruski & The Podfather Network
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.
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- 5 days ago31 min
AI Adoption Without Fear: Psychological Safety, Cloud Lessons, and Real Results in Healthcare | Jessica Kalinowski | AI Accelerator Podcast
What does successful AI adoption actually look like inside an organization? For technology leader Jessica Kalinowski, the answer starts with people, not tools. With more than 15 years of experience leading engineering, DevOps, site reliability, and cloud transformations across e-commerce, financial services, and healthcare, Jessica shares how her teams have used automation and AI to improve reliability, accelerate delivery, and reduce operational friction. In this episode of the AI Accelerator Podcast, Jessica explains why organizations should approach AI adoption much like cloud transformation: with a clear strategy, appropriate guardrails, measurable outcomes, and a willingness to learn. She discusses how psychological safety helps teams embrace new technology, why mandating AI usage can backfire, and how involving engineers in decisions creates stronger adoption. Jessica also shares a practical healthcare example: rebuilding clinician onboarding in-house, consolidating fragmented systems, and using AI to help reduce onboarding time from approximately 30 days to as little as 24 hours in some cases. If you're a business leader looking to move beyond AI experimentation and achieve meaningful results, this conversation offers practical lessons on building trust, empowering teams, and making technology serve real business needs. In This Episode, Jessica Kalinowski Discusses How her career evolved from quality assurance and test automation to cloud, DevOps, site reliability engineering, and AI. Why automation should focus on removing work that does not create value. How her organization increased deployment frequency from once or twice a month to more than 120 times a month. Why psychological safety and transparency are essential for successful AI adoption. How to introduce AI tools through pilots, feedback, and practical use cases instead of mandates. Why AI usage statistics, such as the number of agents created, are not meaningful measures of business productivity. How AI-assisted document reviews can reduce repetitive operational work. How the team rebuilt clinician onboarding and reduced the process from approximately 30 days to as little as 24 hours in some cases. What cloud transformation can teach organizations about AI governance, security, and adoption. Why teams need measurable outcomes, appropriate guardrails, and a clear strategy before scaling AI. Chapters 00:00 – Introduction to Jessica Kalinowski 01:38 – From Quality Assurance to AI and Engineering Leadership 02:42 – Building a Culture of Automation and Reliability 04:47 – How the Team Increased Delivery Speed and Reliability 05:12 – Creating Shared Ownership of Security and Reliability 06:30 – Applying AI Beyond Engineering 07:16 – Psychological Safety and Incremental AI Adoption 08:44 – Transparency, Team Feedback, and AI Guardrails 09:58 – What AI Adoption Can Learn From Cloud Transformation 10:36 – Introducing AI Safely in a Healthcare Environment 11:20 – Starting With Practical Tools and Human Oversight 12:16 – Why Mandating AI Usage Can Backfire 13:30 – Rethinking AI Productivity Metrics 15:02 – Measuring Business Outcomes Instead of AI Activity 16:30 – Leadership, Trust, and Retaining Engineering Talent 17:55 – Building a Better Clinician Onboarding Experience 19:00 – Reducing Operational Complexity and Vendor Sprawl 20:00 – Using AI to Improve Onboarding Workflows 21:53 – Building and Launching an In-House Platform 23:26 – Moving Beyond One-Size-Fits-All Enterprise Tools 24:45 – Building, Iterating, and Owning Your Technology 27:46 – Testing and Rolling Out Changes in Stages 29:36 – Practical Advice for Business Leaders 30:25 – Creating an AI Strategy With Measurable Outcomes 31:24 – Final Thoughts on AI Adoption and Engineering Leadership Key Learnings Successful AI adoption starts with people. Technology alone does not create transformation. Teams need transparency, opportunities to provide feedback, and confidence that AI is being introduced to improve their work rather than simply monitor or replace them. Learn from cloud transformation. Organizations that moved to the cloud without planning for architecture, performance, cost, and operational change encountered challenges. AI adoption also requires preparation, governance, and a realistic understanding of how new tools affect existing workflows. Introduce AI incrementally. Start with practical use cases, pilot programs, and tools that solve recognizable problems. Let employees evaluate the results and help shape adoption before expanding across the organization.
- 1 weeks ago36 min
AI in Customer Experience: What Works, What’s Overhyped, and What Comes Next | Andy Bird | 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 experience 01:54 Andy’s career journey into product and technology leadership 04:56 Building AI-powered customer experience platforms 05:10 The three stages of the customer journey 06:39 Why customer experience technology needs a holistic approach 08:22 Introducing Izzy and customizable AI agents 10:00 Identifying the ideal customers for AI-powered CX 11:48 Self-service AI and keeping humans in the loop 12:18 How business process outsourcing is evolving 12:48 Understanding shared AI agents 14:13 Improving the end-customer experience 15:00 Natural conversations and knowledge-grounded AI 19:02 Latency, transparency, and the AI customer experience 22:20 Choosing AI models based on performance, cost, and use case 23:43 Optimizing costs while improving customer satisfaction 24:07 Why legacy systems and architectural decisions create challenges 28:32 The most overhyped and underrated areas of AI 29:16 Why replacing people with AI can backfire 31:00 Why human support still matters in customer service 32:06 Practical advice for business leaders adopting AI 33:03 Start with your three biggest business problems 34:21 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 satisfact
- 2 weeks ago27 min
AI-Powered Strategic Execution: Connecting Strategy to Results | Cliff Berg | AI Accelerator Podcast
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:<
- 3 weeks ago30 min
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 transformation The five stages of becoming an AI-native organization Why AI pilots often fail to translate into enterprise results The importance of an organizational activation layer Dynamic governance in an AI-powered enterprise How AI changes traditional organizational boundaries Building AI Impact Hubs across departments How HR, Finance, and other functions may evolve alongside AI Upskilling and reskilling employees for AI-plus-human roles Lessons from Greenfield Savings Bank's AI transformation Why AI adoption is more than a technology or licensing decision How leaders can identify pain points and match them with AI solutions Why successful AI organizations may need to double down on human capabilities Rewiring traditional organizations to become more adaptive Chapters 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.
- Oct 6, 202621 min
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 workflows Using AI to prepare for customer meetings How AI agents can connect information across CRM, finance, and support systems The importance of data breadth and quality when implementing AI Why organizations need to think beyond individual employee productivity How leaders should approach AI adoption and transformation The difference between AI optimization and AI-driven opportunity Why AI may represent one of the most significant technology shifts of our lifetime How companies can stay competitive as AI adoption accelerates What 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
- Sep 29, 202627 min
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 transformation Moving from AI pilots to production and measurable business impact The "art of the possible" for AI in different industries Why the AI model itself is rarely the biggest organizational bottleneck Building responsible AI without breaking customer or citizen trust Lessons from deploying a generative AI citizen services assistant for the City of Virginia Beach Using AWS Bedrock and generative AI in a production environment The economics of AI, including cost, ROI, and business value AI transformation in government and the public sector An ethical AI framework developed for U.S. federal agencies The minimum viable approach to responsible AI for midsize organizations Preparing the workforce for an AI-first future How 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
- Sep 22, 202624 min
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 McCarthy His experience with Rodney Brooks and the development of autonomous robotics How lessons from robotics influenced the creation of Scrum Why Jeff describes Scrum as a protocol Why AI agents need structured processes and protocols How Scrum can be adapted for AI-augmented teams The role of AI agents in software development Using AI to manage servers and technology infrastructure The emerging model of AI-augmented Scrum teams How AI can amplify the capacity of a team Measuring AI contribution alongside human work The changing roles of Product Owners and Scrum Masters What CEOs and executives need to do to adapt to AI How organizations can begin building AI capabilities The 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
- Sep 15, 202623 min
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.
About AI Accelerator Podcast
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.
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