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.

00:00 Introduction to Andy Bird and AI in customer experience

“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.

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.

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.

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

"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."

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.

Website: https://www.throughlineai.net 

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

Website: https://leadingaiagility.com 

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

Email: [email protected] 

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 transformation

Chapters

00:00 Introduction

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?

This creates a more systematic approach to organizational adaptation.

5. AI adoption should start with real pain points

The Greenfield Savings Bank example illustrates a practical approach to AI adoption. Rather than treating AI as simply a technology rollout, the CEO selected one or two people from each department to identify significant pain points and then match an AI solution to those problems. The shift was from focusing primarily on licensing and deploying technology to focusing on the people, problems, and outcomes that AI could actually improve.

6. The future isn't only about AI doing more

A major theme of the conversation is that organizations succeeding with AI may be the ones that double down on what makes humans most valuable. As automation changes tasks and workflows, organizations need to think carefully about what humans should continue doing, what new capabilities employees need, and how people and AI can work together.

💬 Melissa Reeve's Key Insights

"The future belongs to those who are willing to rewire."

"There's a real mindset shift that needs to happen."

"How is automation impacting the folks in Finance? What are those roles evolving into?"

"The goal of this Impact Hub... is looking at the impact."

"The companies succeeding with AI are the ones that are doubling down on what makes us the most human."

About Melissa Reeve

Melissa Reeve is the creator of the Hyperadaptive Model and author of Hyperadaptive: Re-wiring the Enterprise to Become AI-Native.

She brings more than 25 years of experience as an executive and Agile thought leader. Her work includes studying the Toyota Production System in Japan, serving as the first VP of Marketing at Scaled Agile, and co-founding the Agile Marketing Alliance.

Melissa developed the Hyperadaptive Model to help organizations combine process excellence, systems thinking, and the human side of AI integration. Her research-backed approach focuses on helping companies move from stalled AI pilots toward becoming truly AI-native and continuously adaptive organizations.

She lives in Boulder, Colorado, with her husband, dogs, and chickens.

Follow Melissa Reeve

Website: http://hyperadaptive.solutions

Follow Matt Zembruski

Website: https://leadingaiagility.com

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.

• How AI can optimize existing business workflows

00:00 Introduction

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."

Andre Cuenin serves as Chief Revenue Officer at SolarWinds, where he oversees revenue generation relating to the company's observability, database, and service management solutions.

He brings more than 20 years of experience driving revenue growth strategies at technology companies including Veracode, Rapid7, Cherwell Software, Stratavia, and CA.

Throughout his career, Andre has focused on building cultures of trust, candor, teamwork, and sustainable customer outcomes. He holds a Bachelor of Computer Science from the University of Applied Sciences and Arts of Western Switzerland and an MBA from Strathclyde University in Glasgow.

Website: https://www.solarwinds.com/

Website: https://leadingaiagility.com

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.

• Why AI productivity gains aren't always translating into enterprise-wide transformation

Chapters

00:00 Introduction

Key Learnings

1. AI productivity is not the same as AI transformation

2. Leaders need to understand the art of the possible

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

Responsible AI therefore needs to be considered alongside strategy, implementation, governance, and the customer experience.

4. The AI model isn't always the bottleneck

The surrounding organizational infrastructure, processes, data, governance, and workflows can be just as important as the model itself.

5. Production deployment matters

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

She has delivered more than 100 AI, data, and enterprise technology initiatives across healthcare, finance, transportation, and government, helping organizations move from AI experimentation toward measurable business impact.

Swathi advises government agencies and enterprises on AI strategy, responsible AI, data modernization, and emerging technologies. She is also the co-author of an Ethical AI framework developed for the U.S. government and created The AI Blueprint, a strategy workshop designed to help leaders move from AI-curious to AI-powered.

A graduate of Georgetown University's McDonough School of Business Executive MBA program, Swathi is passionate about making AI practical, trustworthy, and accessible.

💬 Swathi Young's Key Insights

"The actual end-to-end workflows in an enterprise... that's not changing as quickly."

"What is the art of the possible?"

"How could we use this AI responsibly?"

"Very proud that this project got deployed to production and did not stay in the pilot phase."

Follow Swathi Young

Website: https://theaiblueprint.net

Follow Matt Zembruski

Website: https://leadingaiagility.com

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.

• His early work at the Stanford AI Lab and connection to John McCarthy

The transcript traces Jeff's AI background from Stanford and MIT through robotics and into his current work applying Scrum to AI agents.

2. Scrum Can Be Applied to AI Agents

3. AI Can Amplify Existing Teams

4. AI Work Can Be Measured Within Existing Scrum Practices

5. Leaders Need to Build AI Capability Now

6. AI Requires Continuous Adaptation

“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.”

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.

• Website: https://scrumai.org/

Follow Matt Zembruski

• Website: https://leadingaiagility.com

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: [email protected]

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. 

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.

“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.”

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.

AI adoption isn't happening the same way everywhere. In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Erick Watson, fractional CPO at SproutVest, to explore the differences between AI adoption in China and the United States.

✔ Existing AI models can be the better choice for common business problems.