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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AI in Customer Experience: What Works, What’s Overhyped, and What Comes Next | Andy Bird | AI Accelerator Podcast
Episode 39
Tuesday, October 27, 2026 • Duration 36:05
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 Revonix, 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
07:11 How AI is changing the contact center industry
07:48 Building from the knowledge base outward
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
17:20 Why AI implementations need continuous learning
19:02 Latency, transparency, and the AI customer experience
20:17 Real-time versus non-real-time AI interactions
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
31:32 Where AI opportunities may be overlooked
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
35:03 How to connect with Andy Bird
💬 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 Revonix, a customer experience and AI company developing AI-powered solutions through its Izzy platform. He brings more than two decades of experience in customer experience technology, product management, and platform development.
His career began at Lucent Technologies, where he worked as a sales engineer and developed an interest in product development through customer engagement and collaboration with Bell Labs. He went on to contribute to early cloud contact center platforms and build technology for business process outsourcing companies.
Most recently, Andy served as Chief Product and Technology Officer at LinkLive, an AI-enabled contact center platform serving organizations in industries including banking and healthcare. He is now working on another platform build, focused on making AI-powered customer interactions easier to configure, more adaptable, and more accessible to businesses.
His work centers on customer satisfaction, practical AI adoption, cost optimization, and using technology to strengthen the capabilities of people.
AI-Powered Strategic Execution: Connecting Strategy to Results | Cliff Berg | AI Accelerator Podcast
Episode 38
Tuesday, October 20, 2026 • Duration 27:17
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
💬 Cliff Berg's Key Insights
About Cliff Berg
Follow Cliff Berg
Follow Matt Zembruski
AI-Native Transformation: The 5 Stages of Becoming Hyperadaptive | Melissa Reeve | AI Accelerator Podcast
Season 2 · Episode 37
Tuesday, October 13, 2026 • Duration 30:58
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
AI, Revenue Growth & the Future of AI-Powered Companies | Andre Cuenin | AI Accelerator Podcast
Season 2 · Episode 36
Tuesday, October 6, 2026 • Duration 21:09
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
AI Strategy, Responsible AI & Moving from Pilot to Production | Swathi Young | AI Accelerator Podcast
Season 2 · Episode 35
Tuesday, September 29, 2026 • Duration 27:39
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
AI, Scrum & the Future of AI-Augmented Teams | Dr. Jeff Sutherland | AI Accelerator Podcast
Season 2 · Episode 34
Tuesday, September 22, 2026 • Duration 24:36
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
Is AI Killing Agile? AI, Product & the Future of Software Development with Katy Sherman
Season 2 · Episode 32
Tuesday, September 15, 2026 • Duration 23:49
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
AI, Clean Data & the Future of Product Development | Johanna Rothman | AI Accelerator Podcast
Season 2 · Episode 2
Wednesday, September 9, 2026 • Duration 26:27
AI is everywhere, but more AI does not automatically mean better results.
In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Johanna Rothman, known as the Pragmatic Manager, to explore what practical AI adoption really looks like inside product development organizations.
Johanna Rothman has worked in software and product development since 1977 and has written 21 books covering management, leadership, project management, and product development. Her approach is straightforward: understand your current reality, focus on what actually works, and avoid adopting technology simply because everyone else is doing it.
The conversation explores why clean organizational data may matter more than massive datasets, how AI can improve the flow of information across teams, and why organizations need to rethink how they make decisions in an increasingly AI-enabled workplace.
Johanna also explains why teams should use AI together, rather than treating AI as an individual productivity tool. Effective collaboration, better information flow, shorter feedback loops, and faster decision-making can all play a role in creating more adaptable organizations.
In This Episode, Johanna Rothman Discusses:
◼️ Why AI hype makes pragmatic thinking more important than ever
◼️ Why clean data can be more valuable than enormous datasets
◼️ How organizations can use AI to uncover insights from their existing data
◼️ The difference between cost accounting and measuring organizational flow
◼️ Why value stream mapping can help companies understand how work actually moves
◼️ How AI can improve the flow of information and features across an organization
◼️ Why product development teams need to use AI collaboratively
◼️ The concept of flow debt and how accelerating one piece of work can affect everything else
◼️ Why organizations often need fewer features delivered faster rather than more features
◼️ How AI can support better decision-making without removing human judgment
◼️ Why important business decisions should be treated as experiments
◼️ How shorter roadmaps and faster iterations can make organizations more adaptable
◼️ Why finance, HR, sales, and marketing also need to become more adaptable
◼️ How leaders can think differently about AI adoption and organizational change
◼️ The importance of protecting sensitive and proprietary company data when using AI
AI-Powered Service Operations: Driving ROI and Adoption in the Service Lifecycle | Ashok Kartham
Season 2 · Episode 1
Monday, August 31, 2026 • Duration 25:13
AI is transforming service operations, but the real opportunity is not simply adding AI to existing systems. It is using AI to improve service outcomes, empower the workforce, and create measurable business value.
In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Ashok Kartham, CEO of Circuitry.ai, a serial entrepreneur with more than 25 years of experience building technology and enterprise software companies.
Ashok shares how Circuitry.ai is using AI to support field technicians, contact center agents, manufacturers, and dealer networks. He explains why proprietary service data is so important, how organizations can approach AI adoption, and why AI solutions should be connected to tangible business outcomes.
In this episode, Ashok reveals:
◼️ How AI can empower field service technicians and service workforces
◼️ Why proprietary product and service data is critical for enterprise AI
◼️ How AI can improve service outcomes across complex industries
◼️ Why AI adoption requires customization and integration
◼️ How companies can connect AI investments to measurable ROI
◼️ Why Circuitry.ai ties pricing to tangible service decisions
◼️ How manufacturers and dealer networks can approach AI adoption
◼️ Why companies should start with specific business processes
◼️ How AI can transform warranty, repair, and service workflows
◼️ What business leaders should consider before deploying AI
Chapters
00:00 Welcome to the AI Accelerator Podcast
00:13 Introducing Ashok Kartham & Circuitry.ai
00:45 Ashok’s 25+ years in service and enterprise software
07:47 Using AI with proprietary service and product data
14:23 What AI adoption and implementation really look like
17:18 Connecting AI pricing to service outcomes
22:47 Advice for business leaders adopting AI
23:00 Protecting proprietary data in AI implementations
24:00 Building the right AI architecture for your business
25:00 Starting small with AI and improving business processes
26:00 Using AI to reduce friction in service operations
27:00 Final thoughts on AI adoption and ROI
Key Learnings
✔ AI can empower service professionals rather than simply automate their jobs
✔ Proprietary product knowledge can create significant value for specialized AI applications
✔ Service manuals, diagnostic information, parts catalogs, and other specialized data can be critical AI inputs
✔ AI adoption requires thoughtful implementation, customization, and integration
✔ AI investments should connect to measurable business outcomes
✔ Pricing AI around tangible business transactions can make its value easier to understand
✔ Manufacturers and dealer networks need practical strategies for AI adoption
✔ Companies should identify specific processes where AI can reduce cost or friction
✔ Starting with a smaller, manageable use case can help organizations build momentum
✔ Successful AI adoption requires focusing on the business problem, not simply the technology
💬 Ashok’s Most Powerful Quotes
“AI can really have a very beneficial impact on the service outcomes.”
“We wanted to tie our pricing to the service decisions that we are augmenting or automating.”
“AI is really about how do we train AI on very specific, unique and proprietary data.”
“Just isolate that process and then try to deconstruct it with AI in mind.”
“Start small and pick a process that if you break it, it's not going to be the end of the world.”
Follow Ashok Kartham
Website: https://circuitry.ai
LinkedIn: https://www.linkedin.com/in/ashokkartham/
About Ashok Kartham
Ashok Kartham is a successful serial entrepreneur with more than 25 years of experience as CEO of technology and enterprise software companies. He is the CEO of Circuitry.ai, where he focuses on using AI to empower service workforces and improve service outcomes.
Ashok previously founded Mize, which merged with Syncron in 2021 to create a global leader in Connected Service Experience. He also founded 4CS, a leading warranty and service lifecycle management company that was acquired by Parametric Technology Corporation (PTC) in 2011.
With a degree in Computer Science and extensive experience building technology companies, Ashok brings deep expertise in enterprise software, service operations, AI adoption, and business transformation.
Follow Circuitry.ai
Website: https://circuitry.ai
LinkedIn: https://www.linkedin.com/in/ashokkartham/
Follow Matt Zembruski
Website: https://leadingaiagility.com
LinkedIn: https://www.linkedin.com/in/mattzembruski/
Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778
AI Adoption in China vs. the United States | Erick Watson | AI Accelerator Podcast
Season 1 · Episode 30
Monday, August 24, 2026 • Duration 28:05
AI adoption isn't happening the same way everywhere. In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Erick Watson, fractional CPO at SproutVest, to explore the differences between AI adoption in China and the United States.
Erick shares his perspective on how cultural, economic, and business differences influence AI adoption, where the U.S.-China AI conversation gets misunderstood, and how companies should think about building AI systems that create a real competitive advantage.
The conversation also explores open-source AI models, proprietary data, AI strategy, and a practical approach for companies looking to begin their AI transformation.
In this episode, we discuss:
◼️ AI adoption patterns in China vs. the United States
◼️ Why AI adoption looks different across markets
◼️ The most overhyped and underreported stories in U.S.-China AI
◼️ When companies should build proprietary AI solutions
◼️ When businesses should simply use existing AI models
◼️ Open-source AI and the changing global AI landscape
◼️ Data, security, and AI implementation
◼️ How companies can start small with AI
◼️ Reinventing business processes with AI
◼️ Turning frontier AI research into products people actually use
Chapters
00:00 Welcome to the AI Accelerator Podcast
01:17 Erick’s journey into computer science and technology
07:36 AI adoption in China vs. the United States
14:37 Build proprietary AI or use existing models?
22:40 The most overhyped and underreported U.S.-China AI stories
25:53 How businesses should start their AI journey
Key Learnings
✔ AI adoption needs to be understood within its cultural and economic context.
✔ Companies should determine whether AI can create a genuine competitive advantage before building proprietary systems.
✔ Existing AI models can be the better choice for common business problems.
✔ Open-source models are becoming increasingly important in the AI landscape.
✔ Businesses should start AI adoption with a manageable process rather than trying to transform everything at once.
✔ The best AI opportunities often come from eliminating friction in existing business processes.
💬 Erick’s Key Insights
“There's two decisions you have to make right up front.”
“Is that solution going to be unique to my business?”
“Start small.”
“Reinvent it from the ground up.”
About Erick Watson
Erick Watson helps AI labs, Web3 protocols, and venture studios turn frontier research into products people use. Through SproutVest, he works as a fractional CPO, embedding with founding teams for 6–12 months to define product lines, build go-to-market strategies, and ship products.
Follow Erick Watson
Website: https://sproutvest.com
LinkedIn: https://www.linkedin.com/company/sproutvest
X: https://x.com/sproutvest
Bluesky: https://bsky.app/profile/sproutvest.bsky.social
Telegram: https://t.me/ErickWa
GitHub: https://github.com/ErickWa
Follow Matt Zembruski
Website: https://leadingaiagility.com
LinkedIn: https://www.linkedin.com/in/mattzembruski/
Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778
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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.
1. Becoming AI-native requires more than adding AI tools
Melissa's Hyperadaptive Model focuses on the organizational transformation required to become AI-native. The challenge isn't simply choosing an AI model or purchasing licenses. Organizations need to rethink how their processes, people, governance, and operating systems work together as AI becomes part of everyday work.
2. AI transformation requires an activation layer
One of the central ideas discussed in the episode is the need for an activation layer between AI pilots and actual business results. Organizations can experiment with AI, run pilots, and demonstrate impressive technology. But without the systems and organizational mechanisms required to activate those capabilities, pilots can remain disconnected from meaningful outcomes.
3. AI breaks down traditional organizational boundaries
AI doesn't necessarily stay inside traditional departmental boxes. As Melissa explains, people increasingly need to work across what she calls their "adjacent competencies." Instead of thinking only about what HR does or what Finance does, organizations need to ask how AI is changing the work, roles, and capabilities within each function.
4. AI Impact Hubs can help organizations manage this transition
Melissa describes the concept of AI Impact Hubs as a way to understand how AI and automation are affecting different parts of an organization.
These hubs can examine questions such as:
How is automation changing work within Finance?
How are roles evolving?
Can frontline employees be upskilled or reskilled?
What new AI-plus-human roles could emerge?
How can organizations support employees through the transition?
This creates a more systematic approach to organizational adaptation.
5. AI adoption should start with real pain points
The Greenfield Savings Bank example illustrates a practical approach to AI adoption. Rather than treating AI as simply a technology rollout, the CEO selected one or two people from each department to identify significant pain points and then match an AI solution to those problems. The shift was from focusing primarily on licensing and deploying technology to focusing on the people, problems, and outcomes that AI could actually improve.
6. The future isn't only about AI doing more
A major theme of the conversation is that organizations succeeding with AI may be the ones that double down on what makes humans most valuable. As automation changes tasks and workflows, organizations need to think carefully about what humans should continue doing, what new capabilities employees need, and how people and AI can work together.
💬 Melissa Reeve's Key Insights
"The future belongs to those who are willing to rewire."
"There's a real mindset shift that needs to happen."
"How is automation impacting the folks in Finance? What are those roles evolving into?"
"The goal of this Impact Hub... is looking at the impact."
"The companies succeeding with AI are the ones that are doubling down on what makes us the most human."
About Melissa Reeve
Melissa Reeve is the creator of the Hyperadaptive Model and author of Hyperadaptive: Re-wiring the Enterprise to Become AI-Native.
She brings more than 25 years of experience as an executive and Agile thought leader. Her work includes studying the Toyota Production System in Japan, serving as the first VP of Marketing at Scaled Agile, and co-founding the Agile Marketing Alliance.
Melissa developed the Hyperadaptive Model to help organizations combine process excellence, systems thinking, and the human side of AI integration. Her research-backed approach focuses on helping companies move from stalled AI pilots toward becoming truly AI-native and continuously adaptive organizations.
She lives in Boulder, Colorado, with her husband, dogs, and chickens.
Andre describes one practical path for AI adoption as optimization: take something the organization already does and use AI to perform it more effectively and efficiently.
2. AI agents can connect information across the organization
One of the biggest opportunities comes from connecting AI to the information already distributed throughout an organization. Customer information may exist across multiple systems. An AI agent can help bring that information together so employees don't have to manually search through different platforms to understand the full customer story.
3. Data breadth and quality matter
Connecting an AI system to enterprise data is only part of the challenge. Organizations also need to consider whether the AI has access to the breadth of data that actually matters and how they can perform appropriate quality checks on that information. Better AI outcomes require organizations to think carefully about their underlying data.
4. AI creates opportunities beyond optimization
Andre describes a distinction between using AI to do existing work better and using AI to solve problems in completely different ways. Optimization can make existing processes faster and more efficient. The larger opportunity may come from asking: What could we do differently if AI fundamentally changed how this problem was solved?
5. AI could reshape competitive advantage
Andre believes AI represents one of the most significant technology shifts many people will experience during their lifetime. For business leaders, the challenge is not simply understanding AI. It is figuring out how to leverage it while maintaining competitiveness.mAs AI adoption accelerates, companies that successfully integrate AI into both optimization and new opportunities may gain an advantage over organizations that fail to adapt.
💬 Andre Cuenin's Key Insights
"You do what you have done before, but more effectively, more efficiently."
"A great example of doing something I did before, but much more effectively and efficiently."
"How can I leverage it and also make sure I stay competitive?"
"There’s gonna be two types of companies in the future."
About Andre Cuenin
Andre Cuenin serves as Chief Revenue Officer at SolarWinds, where he oversees revenue generation relating to the company's observability, database, and service management solutions.
He brings more than 20 years of experience driving revenue growth strategies at technology companies including Veracode, Rapid7, Cherwell Software, Stratavia, and CA.
Throughout his career, Andre has focused on building cultures of trust, candor, teamwork, and sustainable customer outcomes. He holds a Bachelor of Computer Science from the University of Applied Sciences and Arts of Western Switzerland and an MBA from Strathclyde University in Glasgow.
1. AI productivity is not the same as AI transformation
Employees can already use AI to write emails, summarize documents, and accelerate individual tasks. But Swathi points out that end-to-end enterprise workflows are not changing at the same pace. The opportunity for leaders is to move beyond individual productivity and examine how AI can fundamentally improve the workflows that run the organization.
2. Leaders need to understand the art of the possible
One of the questions Swathi hears most often from business and public-sector leaders is essentially:
What could AI actually do in my organization?
Understanding the possibilities requires looking at specific use cases within an organization's industry, workflows, customers, and operational environment.
3. Responsible AI is about maintaining trust
Organizations aren't only asking what AI can do. They're also asking how they can use it responsibly without damaging the trust they've built with customers and citizens over many years.
Responsible AI therefore needs to be considered alongside strategy, implementation, governance, and the customer experience.
4. The AI model isn't always the bottleneck
Swathi discusses a common challenge in enterprise AI: organizations can have access to increasingly capable models, but still struggle to translate those capabilities into production outcomes.
The surrounding organizational infrastructure, processes, data, governance, and workflows can be just as important as the model itself.
5. Production deployment matters
Swathi highlights the launch of a citizen services virtual assistant for the City of Virginia Beach as a project she is particularly proud of.
The assistant, known as BeachBoard, uses generative AI and AWS Bedrock to help answer questions about city services, including areas such as utility payments and employment opportunities.
For Swathi, an important part of the achievement was getting the system into production rather than leaving it as an AI pilot.
About Swathi Young
Swathi Young is a technology executive, AI strategist, and award-winning CTO with more than 20 years of experience leading digital transformation across the public and private sectors.
She has delivered more than 100 AI, data, and enterprise technology initiatives across healthcare, finance, transportation, and government, helping organizations move from AI experimentation toward measurable business impact.
Swathi advises government agencies and enterprises on AI strategy, responsible AI, data modernization, and emerging technologies. She is also the co-author of an Ethical AI framework developed for the U.S. government and created The AI Blueprint, a strategy workshop designed to help leaders move from AI-curious to AI-powered.
A graduate of Georgetown University's McDonough School of Business Executive MBA program, Swathi is passionate about making AI practical, trustworthy, and accessible.
💬 Swathi Young's Key Insights
"The actual end-to-end workflows in an enterprise... that's not changing as quickly."
"What is the art of the possible?"
"How could we use this AI responsibly?"
"Very proud that this project got deployed to production and did not stay in the pilot phase."
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.
“They really need Scrum more than the humans, because they screw up faster than humans.”
“The goal is to augment the team and make the single team worth 10 teams.”
About Dr. Jeff Sutherland
Dr. Jeff Sutherland is the co-creator of Scrum and Scrum@Scale and a signatory of the Agile Manifesto. His career in technology and AI spans decades, including early work at the Stanford AI Lab under John McCarthy and work connected to the MIT AI Lab and Rodney Brooks.
Today, Jeff focuses on writing, consulting, and applying Scrum principles to AI through ScrumAI.org. His current work explores how humans and autonomous AI agents can operate together through structured protocols, AI-augmented Scrum teams, and new approaches to organizational productivity.
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.
Chapters
00:00 Welcome to the AI Accelerator Podcast
00:30 Introducing Johanna Rothman, the Pragmatic Manager
01:30 Johanna's journey through product development and management
03:00 Cutting through the AI hype
05:00 Clean data, small models, and practical AI
07:50 What does dirty organizational data look like?
08:20 Cost accounting vs. flow and throughput
09:00 Using AI with value stream mapping
10:00 Measuring work, wait time, WIP, throughput, and cycle time
11:00 AI readiness and organizational assessments
12:00 Why organizations need to understand their current reality
14:20 Data quality and using AI for better business insights
15:15 Understanding flow debt
15:40 Why collaborative teams should use AI together
17:00 Improving information flow across organizations
18:30 AI, decision-making, and organizational effectiveness
19:50 Why valuable decisions should be treated as experiments
21:00 Johanna's writing, books, and pragmatic management philosophy
23:50 Building adaptable organizations with shorter roadmaps
25:00 Why organizations need to experiment and pivot
26:00 The future of AI and organizational adaptability
Key Learnings
✔ AI adoption should begin with understanding the organization's real problems
✔ Clean, useful data can be more valuable than simply having more data
✔ AI can help organizations analyze flow, throughput, cycle time, and work-in-progress
✔ Product development is fundamentally collaborative work
✔ Teams can get more value from AI when they use it together
✔ Faster information flow can lead to better and faster decisions
✔ Organizations often need fewer features delivered more effectively
✔ Important decisions should remain flexible and be treated as experiments
✔ Shorter roadmaps allow organizations to respond to changing conditions
✔ AI should strengthen human decision-making rather than eliminate it
✔ Organizations need to build a culture of experimentation and adaptability
💬 Johanna's Key Insights
“The hype cycle is unbelievable.”
“What we need is effective flow of information and flow of features.”
“The faster you can make that flow of information move, the faster people can then make better decisions.”
“The most valuable decisions need to be able to be thought of as experiments.”
About Johanna Rothman
Johanna Rothman, known as the Pragmatic Manager, is an author, consultant, and expert in product development, project management, program management, portfolio management, leadership, and modern management. She has written 21 books along with hundreds of articles and thousands of blog posts. Her work focuses on helping leaders and teams understand their current reality, improve collaboration, and focus on outcomes that matter.
