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.
• Measure outcomes, not AI activity.
The number of AI agents created or how frequently employees use a tool does not necessarily indicate productivity. Track meaningful measures such as delivery speed, reliability, quality, cost, and the amount of work completed.
• Give teams a voice in the transformation.
Engineers are more likely to embrace AI when they are involved in decisions, understand the reasoning behind them, and can share honest feedback about what is and is not working.
• Use AI to remove friction from real workflows.
Jessica's clinician onboarding example illustrates the potential of combining automation, AI, and process redesign. Bringing fragmented workflows into a more integrated system helped reduce duplication, operational effort, and onboarding time.
• Governance becomes more important as AI scales.
Organizations need visibility into what their AI tools and agents are doing, appropriate security guardrails, and oversight of usage and cost. Not every process is a good candidate for AI.
• Build around business needs, not vendor limitations.
When existing tools do not adequately support a business process, organizations should assess whether a tailored solution makes sense. The decision should be grounded in the problem, feasibility, and long-term value.

💬 Jessica Kalinowski's Key Insights

“The number of agents you have spun up in a day is not a productivity measure.”

“How can we empower them to use the tools?”

“People need a voice.”

“Have a strategy. Have a plan. Have measurable outcomes that you're tracking.”

These insights capture Jessica's central message: AI adoption is a leadership and organizational challenge as much as a technology challenge. The strongest results come from empowering teams, solving meaningful problems, and measuring what actually improves.

About Jessica Kalinowski

Jessica Kalinowski is a technology leader with more than 15 years of experience building and scaling engineering organizations, leading cloud transformations, and delivering measurable business outcomes. Her experience spans e-commerce, financial services, and healthcare, with a focus on DevOps, site reliability engineering, platform transformation, automation, and AI-driven improvements.

At ConnectRN, she has helped scale engineering capabilities, advance CI/CD practices, improve platform reliability, and increase developer productivity. Previously, she led large-scale SRE and cloud initiatives at Chewy and MassMutual, delivering cost savings while strengthening system resilience and developer experience.

Jessica is passionate about empowering teams, simplifying complexity through automation, and aligning technology strategy with business value.

Follow Jessica Kalinowski

• LinkedIn:https://www.linkedin.com/in/jessica-kalinowski-4159636b/

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 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.

Follow Andy Bird

• Website:https://izzyops.ai
• Company website:https://revonix.ai
• Email: bird.andy@gmail.com
• LinkedIn: Search for Andy Bird using the LinkedIn profile name mentioned in the episode.

Follow Matt Zembruski

• Website:https://leadingaiagility.com
• LinkedIn:https://www.linkedin.com/in/mattzembruski/
• Email: matt@leadingaiagility.com
• Phone / Text / WhatsApp: +1 978-618-5778

How can organizations use AI to close the gap between business strategy and the work people actually do every day?

In this episode of the AI Accelerator Podcast, Matt Zembruski speaks with Cliff Berg, managing partner of Agile 2 Academy, founder of the Agile 2 initiative, and architect of the Throughline AI strategic execution platform.

With 30 years of experience studying how organizations operate, Cliff shares why traditional planning and task management can get in the way of agility, and how AI can help leaders connect strategy, goals, and execution.

The conversation explores how AI agents become more effective when they understand an organization's intentions, why leaders should give teams problems to solve rather than simply assigning tasks, and how businesses can introduce AI while keeping humans involved in important decisions.

In This Episode, Cliff Berg Discusses:

• Why organizational agility depends on leadership behavior, not just frameworks
• The origins of Throughline and its AI-powered strategic execution platform
• Connecting strategy, planning, and daily work
• Why solving problems can be more effective than assigning tasks
• Lessons from SpaceX's approach to rapid experimentation and execution
• How psychological safety develops when teams are empowered to try things
• Why AI agents need access to organizational goals, intentions, and context
• The importance of human oversight in AI-powered workflows
• How AI can identify dependencies, delays, risks, and strategic misalignment
• Building roadmaps around outcomes instead of excessive task tracking
• Practical ways to identify low-hanging fruit for AI adoption
• Using AI to review subcontract agreements and save time
• Helping mid-market companies connect AI adoption to long-term business strategy

Chapters

00:00 Introduction to the AI Accelerator Podcast

00:56 Meet Cliff Berg: Agile 2 and Throughline AI

02:28 Why Cliff Built an AI-Powered Execution Platform

03:45 Connecting Strategy, Planning, and Execution

04:51 Why Traditional Task Planning Can Get in the Way

05:46 The Problem-Centric Approach to Work

06:43 What Organizations Can Learn From SpaceX

08:02 Empowering Teams and Building Psychological Safety

09:54 Giving AI Agents the Context They Need

10:30 Understanding Organizational Intentions

11:56 Why Strategic Context Matters for AI Deployments

13:38 How Throughline Agents Understand and Prioritize Work

14:52 Keeping Humans in the Loop

15:45 Why AI Output Quality Matters More Than Speed

17:50 Connecting Strategy to Everyday Execution

18:28 Defining Mission, Vision, and Strategy

19:58 Building Flexible Roadmaps and Goals

21:37 Using AI to Identify Dependencies and Misalignment

22:20 Advice for Business Leaders Starting With AI

23:44 Finding Practical AI Use Cases

24:11 Automating Subcontract Reviews With AI

25:30 Moving From Quick Wins to Strategic AI Adoption

26:18 How to Connect With Cliff Berg

💬 Cliff Berg's Key Insights

"The tools we have make it really hard to do all this."

"What we need is a tool that actually creates this line of sight from strategy through planning and execution."

"The behavioral norm there is to give people problems to solve, not tasks to do."

"What we want is good output. We want aligned output."

"The AI cannot say that's done."

About Cliff Berg

Cliff Berg is the managing partner of Agile 2 Academy, founder of the Agile 2 movement, and architect of the Throughline AI agent strategic execution platform.

He has spent approximately 30 years studying how organizations operate and helping businesses improve agility, planning, and execution. Cliff previously co-founded Digital Focus, a technology company that grew from two people to 200 employees in five years before being acquired in 2006.

In 2019, he launched the Agile 2 initiative and became the lead author of Agile 2: The Next Iteration of Agile, examining why conventional Agile approaches can fail to produce genuine organizational agility.

Cliff has led more than 10 Agile and DevOps transformations, authored six books, and worked with executives and delivery teams to address organizational and technical challenges. His work combines software engineering, systems thinking, leadership, and AI to help organizations connect strategy with execution.

Follow Cliff Berg

Website:https://www.throughlineai.net

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

Follow Matt Zembruski

Website:https://leadingaiagility.com

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

Email: matt@leadingaiagility.com

Phone / Text / WhatsApp: +1 978-618-5778

What does it really take to become an AI-native organization?

In this episode of the AI Accelerator Podcast, Matt Zembruski sits down with Melissa Reeve, creator of the Hyperadaptive Model and author of Hyperadaptive: Re-wiring the Enterprise to Become AI-Native, to explore how organizations can move beyond AI pilots and build the systems, governance, and human capabilities needed to continuously adapt.

Drawing on 25 years of experience as an executive and Agile thought leader, including serving as the first VP of Marketing at Scaled Agile and co-founding the Agile Marketing Alliance, Melissa shares a practical, human-centered approach to enterprise AI transformation.

The conversation explores the five stages of becoming AI-native, dynamic governance, AI Impact Hubs, the activation layer that can help organizations turn AI experiments into results, and why the companies succeeding with AI may be the ones that double down on what makes humans uniquely valuable.

In This Episode, Melissa Reeve Discusses:

• The Hyperadaptive Model for AI 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. 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/LinkedIn:https://www.linkedin.com/in/melissamreeve/

Follow Matt Zembruski

Website:https://leadingaiagility.com/LinkedIn:https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778

How can 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 cultures of trust, candor, teamwork, and sustainable customer outcomes. He holds a Bachelor of Computer Science from the University of Applied Sciences and Arts of Western Switzerland and an MBA from Strathclyde University in Glasgow.

Follow Andre Cuenin

Website:https://www.solarwinds.com/LinkedIn:https://www.linkedin.com/in/andrecuenin/

Follow Matt Zembruski

Website:https://leadingaiagility.com/LinkedIn:https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778

What does it 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, 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/LinkedIn:https://www.linkedin.com/in/swathiyoung/

Follow Matt Zembruski

Website:https://leadingaiagility.com/LinkedIn:https://www.linkedin.com/in/mattzembruski/Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778

What 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, including early work at the Stanford AI Lab under John McCarthy and work connected to the MIT AI Lab and Rodney Brooks.

Today, Jeff focuses on writing, consulting, and applying Scrum principles to AI through ScrumAI.org. His current work explores how humans and autonomous AI agents can operate together through structured protocols, AI-augmented Scrum teams, and new approaches to organizational productivity.

Follow Dr. Jeff Sutherland

• Website:https://scrumai.org/
• LinkedIn:https://www.linkedin.com/in/jeffsutherland/

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 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.

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

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.

Follow Johanna Rothman
Website:Rothman Consulting GroupLinkedIn:Johanna Rothman LinkedInYouTube:Johanna Rothman YouTubeLinks:Johanna Rothman Linktree

Follow Matt Zembruski
Website:Leading AI AgilityLinkedIn:Matt Zembruski LinkedInEmail: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778

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 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

AI is changing how businesses operate, and according to Priya Lakshminarayanan, the biggest opportunity isn't simply using AI to analyze information. It’s using AI to take action.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Priya Lakshminarayanan, Chief Product Officer at Recurly, to explore AI-powered product development, agentic workflows, subscription growth, and the future of business automation.

Priya shares lessons from her career across Microsoft, PayPal, Meta, Brex, and Recurly, along with how her team is using AI to help subscription businesses optimize revenue and automate growth.
She also explains why businesses need to balance AI automation with customer trust, transparency, and strong controls.

In this episode, we discuss:
◼️ Priya’s career journey from engineering to product leadership
◼️ How AI is transforming subscription management and recurring billing
◼️ Recurly Compass and AI-powered agentic workflows
◼️ How businesses can use AI for revenue optimization
◼️ Using AI to build and prototype products faster
◼️ How natural language is making technology more accessible
◼️ AI-powered personalization and content optimization
◼️ Why businesses should focus on data they already have
◼️ Balancing AI automation with customer trust and control
◼️ The future of AI in product development and business growth

Chapters
00:00 Welcome to the AI Accelerator Podcast
00:20 Introducing Priya Lakshminarayanan & Recurly
01:23 Priya’s journey from Microsoft to Recurly
03:40 AI experimentation at Brex
05:00 AI and subscription growth at Recurly
08:00 Building AI-powered agentic workflows
11:00 AI, product development, and customer outcomes
15:00 AI-powered personalization and content creation
17:00 Building with AI and natural language
19:00 The future of AI and business innovation
21:30 Finding opportunities in your business data
22:00 AI, automation, trust, and control

Key Learnings
✔ AI should solve real business problems, not be adopted simply for the sake of using AI.
✔ Agentic workflows can move AI from analysis to action.
✔ Businesses should look closely at the data they already have.
✔ AI can dramatically accelerate product development and experimentation.
✔ Natural language is making advanced technology accessible to more people.
✔ Customer trust, auditability, and control are essential for responsible AI adoption.
✔ AI can help subscription businesses improve revenue and customer experiences.

💬 Priya’s Key Insights
“Set subscription growth on autopilot.”
“Not doing AI for AI’s sake.”
“Trust in all of this is a big thing.”
“Look deep into what data you have access to.”

Follow Priya Lakshminarayanan
Website: https://www.recurly.com
LinkedIn: https://www.linkedin.com/in/prial

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

Artificial intelligence is changing the way organizations work, but according to Scott Andersen, technology alone isn't enough. The organizations that thrive will be the ones that build strong teams, create meaningful learning environments, and empower every individual to contribute.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Scott Andersen, architect, author, speaker, longtime Microsoft technology leader, and former elementary school teacher. Drawing on more than 25 years of experience spanning education, engineering, enterprise architecture, consulting, and leadership, Scott shares why the best AI strategies begin with people—not technology.

From leading global engineering teams and advising CIOs around the world to helping organizations embrace AI responsibly, Scott explains how his years in the classroom continue to shape the way he approaches innovation, collaboration, and leadership.

At the heart of Scott's philosophy is one simple belief:

"Everyone has something to say, and everyone has something to contribute."

In this episode, Scott reveals:

◼️ Why great AI adoption starts with great leadership
◼️ How teaching shaped his approach to technology and innovation
◼️ Why customer engagement is the foundation of successful architecture
◼️ The four leadership lessons that have guided his entire career
◼️ Why strong teams consistently outperform individual brilliance

◼️ How architects help organizations solve business problems—not just technical ones

◼️ The importance of designing AI around people instead of processes

◼️ Why curiosity and lifelong learning are essential in the AI era

◼️ Lessons from working with CIOs and enterprise leaders around the world

◼️ How organizations can successfully lead digital transformation

◼️ Why everyone on a team deserves a voice

◼️ The future of AI, leadership, and enterprise architecture

◼️ How leaders can create cultures where innovation naturally thrives

◼️ Why business success begins with listening before building

◼️ Scott's advice for navigating the next generation of AI transformation

Chapters

00:00 Welcome to the AI Accelerator Podcast

00:30 Introducing Scott Andersen

02:00 From Elementary School Teacher to Technology Leader

05:30 The Four Lessons That Shaped Scott's Career

09:00 Why Customer Engagement Matters

12:00 Building High-Performing Teams

15:30 Leading Global Engineering Organizations

19:00 AI, Enterprise Architecture & Digital Transformation

23:00 Designing Human-Centered AI Solutions

26:00 Leadership Lessons for the AI Era

29:00 Helping Organizations Navigate Change

32:00 The Future of AI and Enterprise Innovation

35:00 Final Thoughts

Key Learnings

✔ Great technology starts with understanding people

✔ Strong teams consistently outperform individual talent

✔ AI should solve business problems—not create new complexity

✔ Leadership is about creating environments where people can succeed

✔ Customer engagement should drive technology decisions

✔ Every team member has valuable perspectives to contribute

✔ Continuous learning is essential in a rapidly changing AI landscape

✔ Enterprise architecture is ultimately about enabling better business outcomes

✔ Human-centered leadership remains critical as AI evolves

✔ Organizations that embrace collaboration adapt faster to change

💬 Scott's Most Powerful Quotes

"Learning is tied to the interest people have in what you are saying."

"Teams always win over individual excellence."

"Everyone has something to say, and everyone has something to contribute."

"If you never try, you'll never discover what you're capable of."

"The best solutions begin with understanding your customer."

About Scott Andersen

Scott Andersen is an author, speaker, architect, and longtime technology leader with more than 25 years of experience spanning education, enterprise architecture, consulting, engineering, and digital transformation.

Beginning his career as an elementary school teacher, Scott developed a people-first leadership philosophy that continues to influence his work today. Throughout his career, he has worked with CIOs, enterprise customers, product groups, and engineering organizations around the world, helping organizations navigate emerging technologies and build high-performing teams.

His unique combination of educator, architect, consultant, and thought leader gives him a practical perspective on how organizations can successfully adopt AI while keeping people at the center of innovation.

Follow Scott Andersen

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

Microsoft: https://www.microsoft.com/

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 is transforming every industry, but according to Evan Kubicek, organizations that succeed won't be the ones chasing every new AI tool. They'll be the ones that stay grounded in real business problems, real customer needs, and real human impact.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Evan Kubicek, Fractional Chief Revenue Officer at AiPRL Assist, Senior Faculty of Marketing, Sales, and Entrepreneurship at Eastern Illinois University, and a leader often described as the "Indiana Jones of Social Entrepreneurship."

Drawing from his unique background in startups, higher education, international development, and AI, Evan shares how businesses can move beyond AI experimentation to build practical solutions that improve customer experience, employee productivity, and organizational performance.

From helping retailers eliminate disconnected customer experiences to discussing why higher education must rethink learning in the AI era, Evan explains why the future belongs to leaders who combine technological innovation with human-centered leadership.
At the heart of Evan's philosophy is one simple idea:
"Ground AI in reality."

In this episode, Evan reveals:

◼️ Why businesses should solve real problems before adopting AI
◼️ How emotionally intelligent AI improves customer experience
◼️ Why disconnected business systems create poor customer service
◼️ How AI can unify communication across email, phone, chat, and social media
◼️ Why API integrations matter more than flashy AI demos
◼️ How retailers can create seamless customer journeys
◼️ Why specialized AI agents outperform one giant AI model
◼️ The importance of grounding AI in real business workflows
◼️ How organizations can build meaningful social impact through business
◼️ Why company values must translate into everyday decisions
◼️ How AI is reshaping higher education
◼️ Why educators should raise expectations—not lower them—with AI
◼️ How AI helps students bridge the gap between theory and real-world business
◼️ Why leaders shouldn't chase every AI trend
◼️ How businesses can move from AI hype to purposeful execution

Key Learnings
✔ AI succeeds when it's built around solving real business problems
✔ Great customer experience depends on connected data, not disconnected systems
✔ AI should remove friction for both customers and employees
✔ Specialized AI agents often outperform general-purpose AI for business workflows
✔ API integrations are the foundation of practical enterprise AI
✔ Organizations should define clear values before pursuing social impact
✔ AI can improve education by raising expectations instead of lowering them
✔ Students should learn how to collaborate with AI—not avoid it
✔ Businesses should focus on meaningful implementation rather than chasing every AI trend
✔ Human-centered leadership remains essential as AI capabilities continue to evolve

💬 Evan's Most Powerful Quotes
"Ground AI in reality."
"The experience on that trail matters."
"It's not intended to replace systems. It's intended to connect them."
"People don't want to repeat themselves."
"I've actually raised the bar on my expectations rather than lowering it."
"You can't afford to not be doing something."
"Everything is AI… but you still need to know where to focus."
"The more people share their successes and failures, the faster everyone learns."

Follow Evan Kubicek
LinkedIn: https://www.linkedin.com/in/evankubicek
Website: https://www.aiprlassist.com

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 is changing how organizations operate, how teams work, and how leaders navigate uncertainty. But according to Andrew Metz, leaders don’t need to become AI experts before they start experimenting.
They simply need to “jump in the pool.”

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Andrew Metz, VP of Sales at Zywave, speaker, and leadership expert with more than 17 years of experience building high-performing sales teams and developing hundreds of sales professionals and leaders.

Having spent his career helping lead Zywave through six CEOs, three private equity transitions, and the growth of a SaaS company from approximately 100 to 1,000 employees, Andrew brings a unique perspective on leadership, culture, trust, and navigating constant change.

From personally experimenting with AI tools to encouraging non-technical professionals to build confidence through hands-on experience, Andrew explains why leaders can no longer afford to watch the AI revolution from the sidelines.

He also explores one of the biggest misconceptions surrounding AI: that the technology will simply replace people.

Instead, Andrew believes AI creates an opportunity to combine analytical capabilities with the creativity, empathy, anticipation, and human connection that great leadership requires.

At the heart of Andrew’s approach is one powerful piece of advice:
“Jump in the pool. No one knows AI.”

In this episode, Andrew reveals:
◼️ Why leaders should lead by example when adopting AI
◼️ Why no one needs to be an AI expert before getting started
◼️ How personally experimenting with AI builds leadership confidence
◼️ Why AI is changing too quickly to wait for the perfect strategy
◼️ How leaders can use AI to handle more analytical and data-driven work
◼️ Why AI can create more space for creativity and strategic thinking
◼️ The importance of combining left-brain and right-brain capabilities
◼️ How non-technical professionals can build confidence using AI
◼️ Why early adopters and internal champions are critical to AI adoption
◼️ How sharing AI best practices can ignite experimentation across an organization
◼️ Why hands-on experimentation is one of the fastest ways to overcome AI intimidation
◼️ How leaders can navigate private equity ownership transitions
◼️ Why protecting organizational culture matters during periods of change
◼️ The role of trust and human connection in high-performing teams
◼️ How leaders can balance empathy and accountability

Chapters
00:17 Introducing Andrew Metz
00:40 Andrew’s Leadership & Sales Journey
05:57 Why Leaders Need to “Jump in the Pool” With AI
06:21 Personally Experimenting With AI
12:05 Why AI Isn’t Simply Going to Take Your Job
12:17 Combining Left-Brain AI Capabilities With Right-Brain Human Skills
18:12 Helping Non-Technical Teams Experiment With AI
18:22 Building Confidence Through Hands-On Experience
18:24 The Power of Early Adopters & AI Champions
21:52 Leading Through Private Equity & Ownership Changes
22:04 Protecting Culture During Organizational Change

Key Learnings
✔ Leaders should personally experiment with AI instead of watching from the sidelines
✔ AI is evolving too quickly for anyone to claim complete expertise
✔ Leading by example is essential for successful AI adoption
✔ AI can handle more analytical work and free people to focus on creativity
✔ Human skills become more valuable as AI capabilities increase
✔ Non-technical professionals build confidence through hands-on experimentation
✔ Small successes with AI can create momentum across an organization
✔ Early adopters and internal champions can accelerate cultural change
✔ Sharing best practices helps organizations increase AI adoption
✔ Trust and human connection remain essential during periods of uncertainty
✔ Leaders must actively protect culture during ownership and leadership transitions
✔ Empathy and accountability can coexist in high-performing organizations

💬 Andrew’s Most Powerful Quotes

“Jump in the pool. No one knows AI.”
“If you knew it three months ago, the mark’s already moved.”
“Use AI as much as you can personally.”
“AI is gonna take our jobs — but in reality, it’s not true.”
“You want to free yourself up to be able to do more right-brain stuff.”
“That’s where people start to build confidence, when they get a little bit of traction.”
“The early adopters and the champions sharing best practices will plant some seeds and ignite some sparks.”

Follow Andrew Metz
LinkedIn: https://www.linkedin.com/in/andrew-metz-sales/
Website: zywave.com

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 is transforming every industry, but according to Alvin Pereira, the organizations that win won't be the ones with the most AI tools. They'll be the ones that combine innovation, empathy, and execution to solve real problems for real people.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Alvin Pereira, Senior Director and Head of Strategy & Operations for Global Workforce Management Products at ADP, former product leader at Google and Disney, and founder of The Pereira Foundation, to discuss how leaders can embrace AI while keeping people at the center of every decision.

Drawing from his experience leading products, building teams, and driving innovation across some of the world's most recognized organizations, Alvin shares practical insights on AI adoption, organizational transformation, product leadership, and creating cultures where innovation thrives.

From Disney's MagicBand platform to enterprise workforce technology, Alvin explains how AI is accelerating product development, reducing barriers to innovation, and enabling organizations to move from idea to execution faster than ever before.

At the heart of Alvin's philosophy is one powerful idea:

"AI should enhance the human."

In this episode, Alvin reveals:

◼️ Why AI should amplify people rather than replace them

◼️ How leaders can reduce fear and resistance around AI adoption

◼️ Why experimentation beats perfection in today's rapidly changing world

◼️ How AI is accelerating product development and innovation cycles

◼️ The importance of empathy in leadership and product design

◼️ Lessons learned from building products at Disney, Google, and ADP

◼️ Why culture matters more than process when driving transformation

◼️ How small teams can achieve extraordinary results

◼️ The role of change management in successful AI adoption

◼️ Why leaders must stay connected to customers and frontline employees

◼️ How AI can help organizations move from idea to prototype faster

◼️ The importance of servant leadership in modern organizations

◼️ Why adaptability is becoming the most valuable business skill

◼️ How leaders can create environments where innovation flourishes

◼️ What the future of AI-powered organizations may look like

Chapters

00:00 Welcome to the AI Accelerator Podcast

00:29 Introducing Alvin Pereira

01:22 Alvin's Career Journey Across Technology, Hospitality & Product Leadership

03:04 Lessons from Google, Disney, Hilton & ADP

03:31 Product Management, Design & Customer-Centric Innovation

06:07 The AI Revolution and Product Development

07:00 Why AI Should Enhance Humans, Not Replace Them

09:04 Managing Organizational Change in the AI Era

12:05 Helping Teams Embrace AI Through Experimentation

14:21 Building a Growth Mindset Culture

15:32 Empowering Teams to Solve Bigger Problems

20:32 Leadership Lessons for Driving Innovation

21:22 Building Disney's MagicBand Experience

24:36 Culture, Trust & Organizational Success

26:26 The Biggest Opportunities Leaders Should Focus On

29:14 Accelerating Innovation with AI

31:42 Leadership, Legacy & The Pereira Foundation

33:24 Final Thoughts

Key Learnings

✔ AI is most powerful when it enhances human capability

✔ Organizations that embrace experimentation adapt faster

✔ Change management is often harder than technology adoption

✔ Great leaders focus on removing obstacles for their teams

✔ Empathy remains a critical competitive advantage

✔ Culture drives innovation more effectively than process alone

✔ AI enables faster prototyping, testing, and iteration

✔ Small, empowered teams can create outsized impact

✔ Customer-focused innovation produces better outcomes

✔ The future belongs to leaders who combine technology with humanity

💬 Alvin's Most Powerful Quotes

"AI should enhance the human."

"You don't build products for yourself. You build them for the people who use them."

"It's not impossible. We just haven't figured out how to do it yet."

"The best framework is the one that helps your team get things done."

"Culture exists for the people inside the organization."

"Leadership is about serving your team."

"The faster you can move from idea to prototype, the faster you can learn."

"Technology changes. Human needs don't."

About Alvin Pereira

Alvin Pereira is a product and strategy executive with leadership experience spanning ADP, Google, Disney, Hilton, and Workforce Software. Throughout his career, he has led product innovation, digital transformation initiatives, and organizational growth efforts focused on creating exceptional customer experiences.

Currently serving as Senior Director and Head of Strategy & Operations for Global Workforce Management Products at ADP, Alvin helps organizations navigate change while building products that improve how people work.

Beyond his corporate leadership roles, Alvin is also the founder of The Pereira Foundation, focused on expanding opportunities and supporting communities through education and service.

Follow Alvin Pereira
LinkedIn:https://www.linkedin.com/in/alvinpereira/

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 #ArtificialIntelligence #Leadership #ProductManagement #Innovation #DigitalTransformation #FutureOfWork #AIAdoption #ProductLeadership #ADP #Disney #Google #AIAcceleratorPodcast #MattZembruski #AlvinPereira

AI is transforming every industry, but according to Shay Harel, the organizations that succeed won't be the ones with the most AI tools. They'll be the ones that can connect strategy, execution, and innovation.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Shay Harel, SVP of Engineering at Advisor360, to explore what it takes to lead enterprise organizations through the AI revolution while continuing to deliver business results today.

Drawing from a career that spans manufacturing, customer support, QA, hardware engineering, software development, cybersecurity, fintech, product leadership, and executive management, Shay shares how leaders can balance long-term transformation with short-term execution.

Having led global teams across the United States, Israel, India, China, Australia, Canada, and the United Kingdom, Shay explains how AI is reshaping leadership, decision-making, collaboration, and innovation inside modern organizations.

At the heart of Shay's leadership philosophy is one powerful idea:
"Strategic thinking plus a Get-Stuff-Done attitude is where the magic happens."

In this episode, Shay reveals:
◼️ Why successful AI adoption requires both strategy and execution
◼️ How leaders can bridge the gap between business goals and technical innovation
◼️ The balance between AI automation and human judgment
◼️ What enterprise organizations are really doing with AI today
◼️ Microsoft Copilot, Anthropic Claude, and the evolving AI landscape
◼️ Why non-technical leaders need to start learning AI immediately
◼️ How AI can uncover insights that traditional reporting misses
◼️ The future of AI-powered decision making inside organizations
◼️ Why culture is often the biggest barrier to AI adoption
◼️ How global teams accelerate innovation and problem solving
◼️ The importance of aligning engineering, product, support, sales, and customer success
◼️ Lessons learned building zero-to-one innovation projects inside large organizations
◼️ How to protect existing revenue while investing in future growth
◼️ Why leaders must experiment with AI before asking teams to adopt it
◼️ The mindset shift every executive needs to make today

Chapters
00:00 Intro
01:00 Guest Introduction
03:00 Background & Career Journey
07:00 Enterprise AI Adoption
11:00 Microsoft vs Anthropic vs OpenAI
16:00 Advice for Non-Technical Leaders
20:00 AI + Human Decision Making
24:00 Organizational Change & Leadership
27:00 Final Advice
29:00 Where to Connect with Shay

Key Learnings
✔ AI adoption is a leadership challenge as much as a technology challenge
✔ Strategic thinking and execution must work together for successful transformation
✔ AI should enhance human decision-making, not eliminate it
✔ Non-technical leaders must develop AI literacy now
✔ Enterprise AI success requires alignment across departments
✔ Culture and organizational readiness often matter more than technology selection
✔ Global teams bring diverse perspectives that accelerate innovation
✔ Leaders should personally experiment with AI before driving adoption
✔ Organizations that delay AI adoption risk losing competitive advantage
✔ The future belongs to companies that combine human expertise with AI capabilities

Shay's Most Powerful Quotes
"Strategic thinking plus a Get-Stuff-Done attitude is where the magic happens."
"I've been lucky enough to experience every layer of technology, from customer support to executive leadership."
"The line between what AI handles and what humans handle is shifting every week."
"If your company hasn't started, you're already behind."
"Great organizations align engineering, product, support, customer success, and sales."
"AI gives us insights that traditional reporting could never uncover."
"Innovation is about building the future while protecting the business that funds it."

Follow Shay Harel
LinkedIn: https://www.linkedin.com/in/shay-harel-6170352/

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 is transforming every industry, but according to Chris Seferlis, most organizations are approaching AI the wrong way.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Chris Seferlis, Strategic Advisor at Microsoft, Boston University Professor, author, keynote speaker, and enterprise AI strategist, to discuss what leaders need to understand about AI adoption, Microsoft Copilot, AI agents, and the future of work.

Drawing from years of experience helping enterprise organizations implement AI solutions, Chris shares why so many AI projects fail, how leaders should think about productivity versus replacement, and why successful AI adoption starts with people, process, and business outcomes—not technology.

From Microsoft Copilot and Azure AI to enterprise data strategies and agentic workflows, Chris provides practical insights leaders can use today to unlock real business value while avoiding the common pitfalls that derail AI initiatives.

At the heart of Chris's message is one critical idea:

"Start with the business problem, not the AI."

In this episode, Chris reveals:

◼️ Why most AI projects fail to meet expectations
◼️ The biggest mistakes leaders make when implementing AI
◼️ Why AI should enhance people, not simply replace them
◼️ How Microsoft Copilot is changing workplace productivity
◼️ The difference between Copilot Chat and Microsoft 365 Copilot
◼️ Why data quality remains critical for AI success
◼️ How organizations can maximize existing Microsoft investments
◼️ The rise of AI agents and agentic workflows
◼️ Why business outcomes should drive AI strategy
◼️ How AI is transforming enterprise operations and decision-making
◼️ The role of governance, security, and responsible AI adoption
◼️ Why leaders need hands-on AI experience themselves
◼️ How organizations can build an AI-ready culture
◼️ Practical examples of AI-powered automation and productivity gains
◼️ What the future of work looks like in an AI-driven world

Key Learnings

✔ Most AI failures are caused by poor strategy, not poor technology
✔ Organizations must define business outcomes before implementing AI
✔ AI is currently creating more productivity gains than workforce replacement
✔ Data quality remains one of the biggest success factors for AI initiatives
✔ Microsoft Copilot is evolving into a powerful enterprise productivity platform
✔ AI agents will automate increasingly complex business workflows
✔ Leaders should personally experiment with AI before driving adoption across teams
✔ Successful AI adoption requires change management and employee engagement
✔ Governance and security must be part of every AI strategy
✔ Organizations that embrace AI thoughtfully will gain a significant competitive advantage

💬 Chris's Most Powerful Quotes

"Most AI projects fail because organizations don't clearly define what success looks like."

"AI should be tied to a business outcome, not deployed for the sake of using AI."

"The companies seeing the biggest wins are using AI to augment people, not replace them."

"Your data strategy is your AI strategy."

"Leaders need to experience AI firsthand before they can successfully lead AI transformation."

"The future belongs to organizations that can combine human expertise with AI capabilities."

Follow Chris Seferlis

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

Website:https://seferlis.com/

YouTube:https://www.youtube.com/@ChrisSeferlis

X:https://x.com/bizdataviz

Email: chris@bizdataviz.com

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 is transforming the workplace faster than most organizations are prepared for but according to Amanda C. Chambers, the future belongs to leaders who empower people to learn, adapt, and grow alongside technology.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Amanda C. Chambers, CEO of The Institute for Organizational Leadership (IOL Global), to explore how AI is reshaping leadership, learning and development, project management, marketing operations, and organizational growth.

With more than 25 years of experience in executive operations, instructional design, leadership development, podcast production, and business strategy, Amanda shares practical insights on how organizations can responsibly integrate AI while keeping people at the center of transformation.

From AI-powered marketing systems and content workflows to leadership coaching and workforce development, Amanda explains why the companies that thrive in the AI era will be the ones that invest in both technology and human capability.

At the heart of Amanda’s message is a powerful leadership principle:

“Let your people learn.”

In this episode, Amanda reveals:

◼️ Why organizations must train employees to use AI responsibly
◼️ How AI enhances human creativity instead of replacing people
◼️ The role of leadership in guiding AI adoption across teams
◼️ Why learning and development will become more important in the AI era
◼️ How IOL Global manages AI-powered marketing and YouTube operations
◼️ The practical ways Amanda’s team uses Claude, ChatGPT, and AI workflows
◼️ Why executives must personally learn AI—not just delegate it
◼️ The importance of strategic partnerships in navigating AI transformation
◼️ How AI is accelerating instructional design and content creation
◼️ Why organizations that refuse AI adoption risk falling behind
◼️ The future of AI-powered project management and leadership operations
◼️ How companies can maintain the human element while automating workflows
◼️ Why ethical AI adoption and workforce training matter more than ever
◼️ How curiosity, adaptability, and learning culture drive long-term success
◼️ Amanda’s core philosophy: “AI should help people do better work.”

Key Learnings

✔ AI should enhance people—not replace them
✔ Organizations must actively train teams to use AI responsibly
✔ Learning and development are becoming even more valuable in the AI era
✔ Leaders must personally understand AI to guide transformation effectively
✔ AI creates more time for creativity, strategy, and relationship building
✔ Human oversight remains essential in AI-powered workflows
✔ Strategic partnerships accelerate AI learning and implementation
✔ Ethical AI adoption requires intentional leadership
✔ Marketing, content creation, and project management are rapidly evolving with AI
✔ Curiosity and adaptability are becoming core leadership skills

💬 Amanda’s Most Powerful Quotes

“Let your people learn.”
“AI is helping people do better work.”
“You need to train your teams to use these tools responsibly.”
“Learning is not going away—it’s changing.”
“It’s not about replacing people. It’s about enhancing what we can do.”

Follow Amanda C. Chambers

Website: http://www.iolglobal.com/

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

Facebook: https://www.facebook.com/amanda.chambers.330467/

Follow Matt Zembruski

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

AI is accelerating faster than most organizations can adapt—but technology alone is not enough.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Jenny Korten, Co Partner at Thinking 5.0 and creator of Mind Nudger AI, to explore one of the most important leadership questions of the AI era: How do we use AI to enhance human wisdom instead of replacing it?

Drawing from decades of neuroscience based leadership coaching, intergenerational strategy, and enterprise transformation across Africa, Europe, and Asia, Jenny shares how leaders can bridge the widening gap between exponential AI advancement and incremental human learning.

From mindset rewiring and cognitive bias to HR transformation, AI readiness, and the neuroscience of sustainable learning, Jenny introduces a people first model for AI adoption that empowers organizations to evolve without sacrificing humanity.

At the center of Jenny’s philosophy is a bold idea: The future is not about AI replacing people—it is about AI amplifying collective human intelligence.

In this episode, Jenny reveals:

◼️ Why AI adoption fails when companies invest in software but ignore people
◼️ The “Formula 1 car without a trained driver” analogy for enterprise AI
◼️ How neuroscience explains resistance to AI and organizational change
◼️ Why leaders must move from tech first to people first thinking
◼️ The hidden danger of AI anxiety and survival mode in the workforce
◼️ How Thinking 5.0 helps organizations protect and enhance human wisdom
◼️ The Mind Nudger AI framework for habit formation and learning retention
◼️ The O.R.A. Method: Observe, Reflect, Act
◼️ Why reflection—not reaction—is the leadership skill of the future
◼️ How HR must evolve from administrative support to strategic transformation
◼️ Why intergenerational alignment is essential for future ready organizations
◼️ How AI can reduce burnout while improving strategic capacity
◼️ The importance of training the human mind as much as the machine
◼️ Why collective intelligence will define next generation leadership
◼️ Jenny’s powerful question: “Are we the last human generation with wisdom?”

Key Learnings

✔ AI transformation without human transformation creates failure
✔ Reflection is becoming a competitive leadership advantage
✔ AI should reduce burnout—not increase anxiety
✔ Human wisdom must remain central in AI strategy
✔ Organizations need both technology investment and people investment
✔ HR and L&D are essential drivers of AI readiness
✔ Intergenerational alignment strengthens organizational resilience
✔ Sustainable learning requires incremental habit building
✔ AI can empower people when paired with critical thinking
✔ Collective intelligence is the real future of work

💬 Jenny’s Most Powerful Quotes

“If you invest $1 in technology, invest $1 in the human mind.”
“AI is the Formula 1 car but your people are the driver.”
“The future is not about AI. The future is about evolving collective intelligence.”
“Reflection reconnects us with human wisdom.”
“Are we the last human generation with wisdom?”

Follow Jenny Korten

Thinking 5.0:https://www.thinking50.studio/LinkedIn:https://www.linkedin.com/in/jenny-korten-736b16219/Email: jenny@thinking50.studio

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