Meridian Point

Agile Meridian

The Meridian Point Podcast explores the intersection of disruption and innovation in today's rapidly evolving business landscape. While drawing on agile and lean principles, we focus on how leaders and organizations can harness disruption to drive positive change and create breakthrough innovations. Each episode features in-depth conversations with thought leaders, entrepreneurs, and change agents who share their real-world experiences and insights on transforming organizations, developing innovative solutions, and navigating change. From AI and emerging technologies to organizational transformation and leadership development, we explore how individuals and companies can not only adapt to disruption but use it as a catalyst for innovation. Whether you're a business leader looking to drive change, an entrepreneur seeking to disrupt your industry, or someone passionate about innovation, The Meridian Point Podcast offers practical strategies and inspiring stories to help you turn disruption into opportunity.

  1. 3d ago

    Bloated Teams, Broken Delivery: The Case for De-Scaling

    The Meridian Point Podcast Episode 175: Bloated Teams, Broken Delivery: The Case for De-Scaling with Nawaz Butt EPISODE SUMMARY Most organizations hired to scale Agile are solving the wrong problem. They add teams, stack committees, multiply hand-offs and approval gates, then wonder why delivery is slower than it was three years ago. Nawaz Butt has spent years inside some of Canada's largest organizations, including Canada Life, and his diagnosis runs counter to almost everything the transformation industry sells: the goal isn't more teams. It's fewer, right-sized, autonomous teams aligned to a purpose they actually own. In this conversation, Nawaz and Kumar Dattatreyan cover what de-scaling actually means in practice, why Conway's Law predicts most of the complexity practitioners are hired to untangle, how psychological safety (not talent) is the real differentiator between high and low performing teams, and why the word "team" gets thrown around to describe groups of people who have never truly pulled for each other. They also get into the future of coaching, the limits of AI in human-centered practice, and what ikigai has to do with leaving a mark on the world. KEY TOPICS COVERED Conway's Law as a Diagnostic Tool Organizations design systems that mirror their communication structures. As companies grow, they add hierarchy, committees and approval gates. The result is a compounding dependency problem that no amount of scaling Agile will fix. De-scaling starts with simplifying decision-making, not multiplying teams. What "Team" Actually Means People throw the word team around to describe groups sharing a Jira board. A real team is small, knows each member's skills and abilities, backs each other up without being asked, and pulls toward the same goal. Most large organizations have groups, not teams. That distinction matters for delivery. The Formula One Model for Alignment Ferrari and Red Bull each have 1,200 to 1,500 people on their Formula One teams. The question isn't how many people, it's whether leadership has done enough work to define the goal so clearly that everyone, from aerodynamics to pit crew, knows their role in winning. Common purpose is the only thing that makes scale work. De-scaling in Practice: Delegation Poker Moving teams from a "telling" stance to an "owning" stance is not a single move. Delegation poker maps the spectrum from directive to fully autonomous. The aim is always to move right, toward autonomy, but most teams start somewhere in the middle. The key is making the conversation about trust explicit. Bring the Work to the Team Too often, people are moved to the work rather than the work coming to the team. Long-lived teams with fixed capacity and clear commitments need ownership over what they build. When that ownership is missing, virtual teams get stood up by borrowing capacity from existing teams, initiatives clash, branches can't merge, and nobody is tracking the true cost of the fragmentation. Psychological Safety as the Prerequisite Google's Project Aristotle identified psychological safety as the single biggest predictor of high-performing teams, above talent, above technical skill, above everything else. Nawaz opens every coaching engagement by asking how much safety exists before asking anything about process. When a Team Doesn't Need a Coach Nawaz walked into an engagement, spent two weeks with a self-organized, nimble team that had been without a manager for over a year and was thriving. He went back to leadership and told them the team didn't need him. His coaching hours are limited. The job is to create conditions for self-sufficiency, not to preserve the coaching role. AI in Coaching: What the Machine Misses Kumar packaged his own coaching knowledge into an AI agent named E. Kumar for a client to use between sessions. Nawaz asked organizations what the AI got wrong and got surface-level answers about abbreviations and misspelled names. His deeper point: AI can't feel the tension in a room, see the rolling eyes, or sense when someone is shutting down. Those are the signals that coaching actually runs on. The Future of Coaching Coaching as a title may fade. The function matters more than the label. The real shift Nawaz wants to see is coaching and mentoring baked into leadership competency models so that every leader develops people as a core part of the job, not as a nice-to-have. MEMORABLE QUOTES "The aim has to be not to have more teams, but to have fewer teams in the organization." "I don't see people as Agile or non-Agile. I meet the team where they are." "This team doesn't need me. I went back to leadership and told them that. My job as a coach is not to preserve my role. It's to create conditions where people become self-organized and self-sufficient." "If somebody says to person one in the morning that you are an a-hole, and to person two in the afternoon, and to person three in the evening, you know who the a-hole is. It's your reflection you see." "AI doesn't have any feeling. It won't feel the tension in the room. It won't see the visual cues. At least not yet." "Do what you like to do. Or at least do more of it if you can't do only that." KEY TAKEAWAYS Conway's Law predicts your delivery problems: your systems mirror your communication structures. Fix the structure before adding more teams. De-scaling means simplifying decision-making, not just cutting headcount. Localized, autonomous decision-making bodies are the goal. The word "team" is overused. Most organizations have groups. Real teams are small, mutually accountable and purpose-driven. Bring the work to the team. Moving people to the work destroys team coherence and creates virtual team debt that nobody is measuring. Psychological safety is not a culture outcome. It's a prerequisite for everything else. A good coach works themselves out of a job. Creating conditions for self-sufficiency is the measure of success, not the length of the engagement. AI augments coaching but can't replace the human capacity to read a room, feel tension or hold space for disagreement. RESOURCES MENTIONED Conway's Law: organizational design principle linking communication structures to system design Project Aristotle (Google): research identifying psychological safety as the top predictor of team performance Delegation Poker: management tool for mapping the spectrum from directive to autonomous decision-making Collaborative Intelligence by J. Richard Hackman: six conditions for effective teams Ikigai: Japanese concept for finding purpose at the intersection of what you love, what you're good at, what the world needs and what you can be paid for XSCALE: de-scaling framework referenced by Kumar Dattatreyan throughout the conversation BetterUp: coaching platform deploying AI agents alongside human coaches CONNECT WITH NAWAZ BUTT LinkedIn: https://www.linkedin.com/in/nawazbutt/ Nawaz runs an Agile and leadership meetup group in Toronto. Connect with him on LinkedIn to follow his thinking on organizational design, de-scaling and people-first coaching. CONNECT WITH KUMAR DATTATREYAN LinkedIn: https://www.linkedin.com/in/coachkdat/ Book a strategy call: https://tidycal.com/coachkumar/30-minute-meeting Learn more about Agile Meridian: https://www.agilemeridian.com Learn more about The Disruptor Method™: https://thedisruptormethod.com/quiz ABOUT THE GUEST Nawaz Butt is an Agile coach and transformation practitioner based in Toronto, Canada. He works inside large organizations helping leadership teams navigate complexity without losing their people in the process. He currently serves as Program Steward at Canada Life, one of Canada's largest insurance providers. He runs a long-standing Agile and leadership meetup group with over 5,000 followers and brings a people-first, framework-agnostic approach to every coaching engagement. ABOUT THE HOST Kumar Dattatreyan is an ICF PCC executive coach and co-founder of Agile Meridian. He is co-creator of The Disruptor Method™, a framework that helps leadership teams disrupt themselves to drive organizational transformation. The Meridian Point explores disruption, innovation and the leadership it takes to navigate both. SUBSCRIBE Spotify | Apple Podcasts | YouTube | LinkedIn New episodes broadcast live every other Tuesday at 12:30 PM Eastern. Leave a review if this episode added value. #DescalingAgile #OrganizationalDesign #AgileCoaching #LeadershipDevelopment #TeamDynamics #ConwaysLaw #BusinessAgility #DeliveryExcellence #ExecutiveCoaching #TheMeridianPoint #Disruption #Innovation #FutureOfWork #PsychologicalSafety #AgileTransformation

    Bloated Teams, Broken Delivery: The Case for De-Scaling

  2. Jun 30

    AI Is Not a Magic Bullet: What Enterprise AI Gets Wrong

    The Meridian Point Podcast EP174: AI Is Not a Magic Bullet: What Enterprise AI Gets Wrong Guest: Ashwini Kumar, Product Manager & AI/ML Practitioner EPISODE SUMMARY Everyone thinks they understand AI now. Ashwini Kumar has been doing the actual work for six years, long before ChatGPT turned every executive into a self-proclaimed AI strategist. From manufacturing and energy to healthcare consulting and major telecom, he's been in the room when enterprises placed their biggest AI bets and watched those bets play out in real time. In this conversation, Ashwini unpacks the patterns he keeps seeing: executives who treat AI as a pedestal project for their resume, organizations that automate broken human workflows instead of rethinking them, and the persistent gap between what companies hope AI will do and what it actually delivers. He also walks through his team's hands-on experience building an agentic RAG system for proposal generation that collapsed a two-week drafting cycle into two days with a four-person team. The conversation covers why machine learning was solving real problems long before LLMs arrived, where change management breaks AI adoption, and what a C-suite leader should actually ask before building an AI strategy. KEY TOPICS COVERED Machine Learning Before the Hype Ashwini describes ChatGPT as "the front end of AI," something anyone can talk to. But machine learning was already powering Netflix recommendations, optimizing roof tile manufacturing recipes, and running CFD wing design simulations. The work was happening. It just wasn't visible to the public. The Executive Pedestal Problem AI becomes a pet project for some executives. They push implementation so hard that they stop asking whether it's solving actual problems. The investment gets so large that the project "cannot fail," and when it does underperform, the numbers get fudged to protect the narrative. Automating Broken Workflows Companies make the mistake of layering AI on top of existing human processes without examining whether those processes make sense. A human might jump between six systems to gather information. An automation doesn't need those same steps. Rethinking the workflow before applying AI is where most organizations skip the most critical step. Agentic RAG vs. Standard LLM Prompting Ashwini's team built a proposal-generation tool using agentic RAG that produces a first draft in one to two days. In a standard LLM like Claude, you'd still need to prompt section by section. With properly designed agents handling the work, the system generates a complete proposal in one run with minimal follow-up prompting. For consulting companies where proposals typically take two weeks (or a month for government work), the time savings translate directly to more proposals submitted and more potential revenue. The 20/60/20 Adoption Rule When you implement AI in an organization, roughly 20 percent of people are enthusiastic and jump in immediately. The middle 60 percent are indifferent, using it occasionally. The bottom 20 percent want nothing to do with it. Without engagement from that broader population, implementation goes flat and ROI disappears. Start with Vision, Not Strategy When a C-suite executive asks for an AI strategy, Ashwini's first move is product thinking: what is your AI vision? What do you see it doing? Some executives are lofty. Some are spot on. Some don't have an answer at all. You can't build a strategy until you know where they actually stand. The Hope-Reality Gap Ashwini's closing observation: there's still a gap between the reality of AI and the hope of AI. The reality will keep moving forward. But there will always be something on the horizon that it doesn't do yet. MEMORABLE QUOTES "ChatGPT is sort of the front end of AI. Something that anybody can talk to." "It becomes sort of a pet thing for some executives. Like, oh, it becomes sort of a thing that they can put on their pedestal. They push it sometimes so hard that they don't think about what problems it's solving." "You shouldn't apply an AI process as a blanket way to solve what you're doing as a human workflow." "A human might be jumping around in different systems to get information. An automation doesn't need to jump around. Well, it does jump around, but much faster." "When we put the agents in and we did the agents correctly, it made a huge difference. We're able to generate the proposal in one run." "There's a top twenty percent of people that are just all about it. And then you have the middle which is indifferent. And the bottom that doesn't want anything to do with it." "The reality of AI and the hope of AI, there's still a gap there." "Everybody believes AI is a magic bullet, but it's not." KEY TAKEAWAYS Machine learning was solving real enterprise problems for years before generative AI arrived. ChatGPT made AI visible, not new. Executive ego is a failure mode. When AI becomes a resume item instead of a problem-solving tool, the organization stops asking the right questions. Don't automate the human. Examine the workflow first. Remove unnecessary steps before applying AI to what remains. Agentic RAG outperforms standard prompting for complex knowledge work like proposal generation. Change management determines AI ROI. Without broad engagement, implementation dies regardless of how good the technology is. Ask for the vision before building the strategy. If leadership can't articulate what they want AI to do, no strategy will save them. The gap between AI hope and AI reality is persistent. The frontier moves, but so do expectations. RESOURCES & LINKS Connect with Ashwini Kumar: LinkedIn: linkedin.com/in/shawnakumar Technologies & Concepts Referenced: Agentic RAG (Retrieval-Augmented Generation) Semantic Kernel (Microsoft) Claude Code CFD (Computational Fluid Dynamics) simulation optimization Machine learning for chemical manufacturing process control ABOUT THE GUEST Ashwini Kumar is a product manager and AI/ML practitioner with six years of experience implementing machine learning and AI solutions across manufacturing, energy, healthcare consulting, and telecommunications. He specializes in identifying where AI can genuinely improve enterprise workflows versus where it's being applied as a superficial fix. His current work focuses on agentic RAG systems for knowledge-intensive business processes like proposal generation. ABOUT THE HOST Kumar Dattatreyan is co-founder of Agile Meridian and co-creator of The Disruptor Method. The Meridian Point explores disruption and transformation through conversations with leaders, practitioners, and entrepreneurs who are shaping how organizations think and operate. Connect: LinkedIn | agilemeridian.com | thedisruptormethod.com Book a conversation SUBSCRIBE The Meridian Point broadcasts live on LinkedIn, YouTube, and Facebook every other Tuesday at 12:30 PM Eastern. Subscribe so you don't miss the next conversation. #EnterpriseAI #AIStrategy #MachineLearning #AgenticRAG #ChangeManagement

    AI Is Not a Magic Bullet: What Enterprise AI Gets Wrong

  3. Jun 16

    The AI Decisions Are Wrong (And Your Data Isn't the Problem)

    The AI Decisions Are Wrong (And Your Data Isn't the Problem) | James Taylor Guest: James Taylor, Founder and Executive Partner of Blue Polaris (formerly Decision Management Solutions) Host: Kumar Dattatreyan Episode Date: June 16, 2026 Watch on YouTube Why You Need to Listen to This Episode If you have sat through one more AI strategy meeting that ended with "we just need better data," this episode is for you. James Taylor has been in the room for those meetings for twenty years. He helped create the decision management category at FICO. He co-submitted the DMN industry standard. He has watched companies pour money into predictive analytics, then machine learning, then generative AI, and watched most of those investments produce demos that impress executives and change almost nothing about how the business actually runs. He has a pretty clear idea of why. It is not the technology. It is not the data. It is the question nobody is asking before the work starts: which decisions are we actually trying to improve? In this conversation, James walks through the discipline he has spent his career building. Why decisions belong outside the process, not buried inside it. Why a language model alone cannot run a regulated business decision. Why "explainable AI" does not actually explain anything a regulator will accept. And the pattern he sees working in the wild, where AI handles the messy parts on either end and prescriptive logic handles the part that has to be auditable. By the end of it, you will probably start noticing missing decisions in your own organization that you have been walking past for years. What You Will Learn in This Episode The Difference Between a Process and a Decision (And Why It Matters More Than You Think) Most teams build their systems as processes with decisions hidden inside as steps. James makes the case that this is the single biggest source of friction in enterprise software. Processes change slowly. They have lots of owners. They require retraining when they change. Decisions change all the time, often have one owner, and need to flex with the market. When you smear them together, every small change becomes a big project. When you separate them, the process gets dramatically simpler and the business gets dramatically more agile. The California DMV Example That Settles the Whole Argument James drops one of those examples that you will use in your own conversations for years. The California DMV has had the same process for licensing vehicles since the 1950s. They send you a bill, you send them a check, they send you a sticker. Nothing about that has changed. What changes constantly is the math underneath, because politicians tinker with the fees every legislative session. Same process for seventy years. Different decision every year. This is what decision management is actually about. The Four Conditions a Decision Has to Meet Before You Should Even Try to Automate It Not everything is a candidate for automation. James walks through the criteria he uses to figure out which decisions are worth the work: the timing has to be predictable, the decision has to be made often enough to justify the effort, the inputs have to be reliably available, and the outputs have to be bounded. If any of those are missing, the decision either stays with a human or needs to be redesigned before you can automate it. This is the filter most AI initiatives skip, and it is why so many of them end up in the slide-deck graveyard. Why Explainable AI Does Not Actually Explain Anything This is the part of the conversation that should make every executive sit up. James was direct: explainable AI does not know how the model decided. It generates a plausible-sounding story about how the model might have decided. Regulators have figured this out. The CFPB has already said the rules do not change just because you used AI. Canada has legislation that requires you to be able to tell any affected customer exactly how a decision about them was made. A chatbot answer is not going to satisfy that. You need actual structure underneath. The Architecture That Actually Works in Regulated Industries Here is where the episode flips the typical AI narrative on its head. James describes the pattern he sees succeeding at his clients. AI ingests the messy input — emails, attached documents, unstructured forms. A prescriptive decision engine runs the actual logic against that data. AI generates the explanation back to the customer or call center rep in plain language. To the user it feels like a chatbot. Under the covers, every step is logged, auditable, and defensible. You get the speed of AI and the accountability of a traditional system. This is not theoretical. This is what is shipping right now. Why the Bottleneck Has Quietly Moved From Data to Decisions There is a moment in the conversation where James says something most AI buyers have not caught up to yet. The old constraint was that you had to digitize and clean your data before you could do anything useful with it. Large language models have largely solved that. They are good at reading documents. The new constraint is upstream of the data. If you do not know what you need to extract from those documents in the first place, the fact that AI can extract anything does not help you. The discipline that tells you what to look for is decision modeling. It used to feel optional. It is now the thing. Why the Hardest Part of This Entire Field Is the Word "Decision" The closing exchange is the one that will stay with you. James explains that the single biggest obstacle to building this discipline inside organizations is that everyone already thinks they know what a decision is. Executives hear the word and picture strategic choices they make in boardrooms. James means the thousands of small operational decisions their systems make every day, often badly, often invisibly. The website that fails to recognize a returning user. The claim that goes to manual review when it did not need to. The loan that gets declined for a reason nobody can articulate. These are the decisions where the value is, and they are the ones nobody is paying attention to. Best Quotes from This Episode "My process doesn't change. They still got the same documents. They still go to the same people. They still look at them. They still check the same things. All of that's the same. All that changes is the decision making." "It used to be for sure you had to digitize the data first before you could do anything else. And now large language models are really good at reading documents. So now what matters is do you know what data you need out of the documents?" "Even if you look at explainable AI, mostly what it does is it comes up with a plausible explanation of how it might have decided. It doesn't really know how it decided." "The people we see basically flailing around are the ones who are like, I'm going to throw the baby out with the bath water and start again with large language models. And we're like, why would you do that? You already know a lot." "It's the D word, decision. On the one hand, everyone knows what decisions mean. The problem is that everyone knows what decisions mean. They think I mean the decisions they make. And generally I don't." Connect with James Taylor LinkedIn: https://www.linkedin.com/in/jamestaylor/ Blog (JT on EDM): https://jtonedm.com Blue Polaris: https://bluepolaris.com Books by James: Digital Decisioning: Using Decision Management to Deliver Business Impact from AI (MK Press, 2019) Real-World Decision Modeling with DMN, with Jan Purchase (MK Press, 2016) Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions, with Neil Raden Take the Disruptor Method Quiz Are you disrupting or about to be disrupted? Find out in under five minutes: https://www.thedisruptormethod.com/quiz Work with Kumar Book a 30-minute conversation: https://tidycal.com/coachkumar/30-minute-meeting

    The AI Decisions Are Wrong (And Your Data Isn't the Problem)

  4. Jun 2

    She Ran a $120M portfolio. Now She's Telling CEOs the Truth About AI.

    The Meridian Point Podcast She Ran $120M at AT&T. Now She's Telling CEOs the Truth About AI. Guest: Suzel Wyvill-Jones, Founder, Mindshift Dynamics Air Date: June 02, 2026 ABOUT THIS EPISODE Here's the part nobody warns you about. The AI demo always works. The vendor walks in, the synthetic data behaves, the dashboard lights up and every executive in the room is sold. Then the rollout starts. The real data shows up. And the whole thing falls apart. Suzel Wyvill-Jones has watched that movie too many times. She spent over twenty years leading $120 million-plus portfolios at AT&T and Cricket before walking away from the corporate ladder to build Mindshift Dynamics. Now she works with executives who are tired of being sold AI and want to know what actually works. In this conversation she does not pull a single punch. She explains why most companies are treating AI like shopping without a list. She walks through the fraud-detection example that exposes why every vendor demo lies to you. She gets into bias, the kind that quietly shapes who gets a loan and who gets a missed cancer diagnosis. She tells you exactly which AI tool a CEO should buy first (it is not the one you are thinking of). And she calls the AGI-in-five-years narrative what it is: overhyped, because the data on this planet is not even close to ready. If you have ever sat in an AI strategy meeting and wondered whether anyone actually knows what they are talking about, this episode is going to feel like a friend finally telling you the truth. Stick around for the end. Suzel shares the moment she realized why this work has become urgent for her and it is not what you would expect from a former corporate executive. WHAT YOU WILL LEARN The full conversation runs through everything below. Some of these moments are tactical. Some are philosophical. A few are personal. All of them are worth your time. Why most companies cannot answer the simplest AI question. Ask an executive what business problem their AI initiative is solving. Suzel explains why most cannot answer and why that single failure predicts everything that goes wrong next. The fraud-system story every executive needs to hear. A perfect demo, a confident rollout, a complete collapse on contact with real data. Suzel walks through the pattern she has seen play out across multiple Fortune 500s. Where to actually start with AI. Hint: not where your vendor wants you to start. Suzel explains why repetitive workflows, weekly dashboards and project management tasks are the right entry point and why enterprise-wide deployments on day one are almost always a mistake. The data-readiness conversation no one wants to have. "There is no AI without data." Suzel breaks down why most enterprise data is not ready for AI, why nobody wants to admit it and what to do about it before you spend another dollar on tools. Bias, the Pope and the part of AI nobody is talking about loudly enough. Suzel references the Pope's recent publication on AI bias to make a point that is bigger than religion or politics. When biased data trains a credit model or a cancer diagnostic, it makes biased decisions at scale. This part of the conversation will change how you think about responsible AI. Why probabilistic decisions and credit decisions do not mix. AI does not make the same decision twice given the same input. For some use cases that is fine. For others (loans, mortgages, anything an auditor might one day ask about) it is a real problem. Suzel and Kumar work through where the line is. The "Claude Mythos" exchange. A short, candid moment where Kumar brings up an unreleased AI model that even its creators say is too dangerous to publish. Suzel's response on regulation, who should write the rules and why the people building this stuff need to be at the table is one of the sharpest moments in the episode. What AI amplifying human leadership actually looks like. Suzel describes producing a PowerPoint in five minutes that used to take two hours. The point is not the time saved. The point is what she does with that time. Stay with this section. The AI Collective and why grassroots is outpacing corporate. A 160,000-person movement called "the human side of AI" is growing faster than most enterprise AI programs. Suzel explains what they do, why it matters and how her work teaching AI literacy to elders came directly out of this community. The quantum-computing metaphor that closes the episode. Classical computing is zero or one. Quantum computing holds both as possibility. Suzel uses this to make a point about how leaders, teams and societies could move past either-or thinking. It lands harder than it sounds. The lightning round. Brazilian engineering culture and what Silicon Valley gets wrong. The introvert-engineer-to-podcaster transformation. The one AI tool a CEO should pick for the next twelve months. The skill a 25-year-old should be developing right now. And one prediction about AGI that will either reassure you or worry you, depending on how you feel about hype cycles. QUOTES WORTH PULLING "Companies are looking at the shiny object when they say 'I have AI,' but they are not looking at what kind of problems they actually need to solve." "There is no AI without data." "Demos work because you have synthetic data. Everything is perfect. Then you go look at the real environment and one missing component breaks the whole thing." "Right now I can produce a PowerPoint in about five minutes. That allows me to be a better professional and a better person." "AGI in five years is overhyped. The data on this planet is not conducive to this." "Instead of dividing society, maybe we should look at ourselves together as a possibility, instead of the separation." "I have children, and when I go, I want to leave here a better place. Lately it is becoming very urgent." WHO THIS EPISODE IS FOR If you are an executive, transformation leader, founder or senior practitioner who is sick of AI keynotes and wants a real conversation about execution: this is for you. If you have ever sat in a vendor pitch and thought "this is too good to be true" but did not know how to push back, this is for you. If you care about responsible AI but do not want to wait for Washington to figure it out, this is for you. And if you are a parent or a leader who thinks about what kind of world we are building for the people who come after us, the last few minutes of this conversation will stay with you. CONNECT WITH SUZEL If anything in this episode lands with you, reach out to her directly. She is one of the most accessible AI strategists I have come across. LinkedIn: linkedin.com/in/suzelwyvilljones Website: mindshiftdynamics.net Email: suzelwj@mindshiftdynamics.net Phone: (404) 277-7177 Location: Brookhaven, Georgia (Atlanta metro) Book: AI-Powered Business Transformation: From Strategy to Scalable Execution in Weeks Join the AI Collective: A global grassroots movement of more than 160,000 people focused on the human side of AI. Search "AI Collective" on LinkedIn or Meetup to find your local chapter. If you are in Atlanta, Suzel runs it. ABOUT THE GUEST Suzel Wyvill-Jones is the founder of Mindshift Dynamics, an AI strategy and transformation advisory firm based in Atlanta. She is a published author, transformation strategist and AI innovation advisor with over twenty years of enterprise experience leading $120 million-plus portfolios at AT&T, Cricket and other companies across telecommunications, financial services and supply chain. ABOUT THE HOST Kumar Dattatreyan is co-founder of Agile Meridian and co-creator of The Disruptor Method. He is an ICF PCC executive coach who works with Fortune 500 leaders and C-suite executives on organizational transformation, business agility and leadership alignment. The Meridian Point is where Kumar sits down with the transformation leaders, founders and executive coaches who are actually doing the work. No fluff. No hype. Just honest conversations about disruption and innovation. Connect with Kumar: LinkedIn: linkedin.com/in/kumardattatreyan Website: agilemeridian.com Book a 30-minute conversation: tidycal.com/coachkumar/30-minute-meeting SUBSCRIBE AND NEVER MISS AN EPISODE If this conversation gave you something to think about, do me a favor. Subscribe wherever you are listening. Hit the bell on YouTube. Follow the show on LinkedIn. Leave a rating on Apple Podcasts or Spotify. It takes thirty seconds and it helps more leaders find conversations like this one. Listen and follow: Spotify | Apple Podcasts | YouTube | And if you know a leader who is currently stuck in AI hype-cycle hell, send them this episode. Suzel might be the conversation they have been waiting for. #AIStrategy #ResponsibleAI #DigitalTransformation #AILeadership #EnterpriseAI #AIAdoption #AIGovernance #BusinessTransformation #FutureOfWork #TheMeridianPoint

    She Ran a $120M portfolio. Now She's Telling CEOs the Truth About AI.

  5. May 19

    Joy as a Strategy: Inside Menlo Innovations With Rich Sheridan

    The Meridian Point Podcast Joy Is a Strategy: Inside Menlo Innovations With Rich Sheridan ABOUT THIS EPISODE Here's a number that should stop you cold. Nearly 3,000 people a year fly in from four continents to visit a software company in Ann Arbor, Michigan. They're not coming to see the technology. They're coming to see something almost no workplace has figured out: what it looks like when joy is built into the way a company actually operates. Rich Sheridan is the co-founder, CEO, and Chief Storyteller of Menlo Innovations. He's also the author of two bestselling books, Joy, Inc. and Chief Joy Officer. And in this conversation, he does something rare: he tells the full, unvarnished story of how Menlo came to be, what makes it work, and why most leaders who try to copy it get it completely wrong. This one is going to make you rethink your team, your culture, and probably your next all-hands meeting. WHAT WE TALKED ABOUT Rich was almost a canoe camp director. No, really. By his mid-30s, Rich had the VP title, the stock options, the upward trajectory — and he was miserable. His teams were missing deadlines, shipping broken software, and calling end users "stupid." He was seriously considering walking away from tech entirely. What pulled him back wasn't a promotion or a new job. It was a realization that hit him while reading Tom Peters and Peter Senge: this isn't a technology problem. It's a human organization problem. That shift in thinking became the seed for everything Menlo would become. The reason Menlo is named after Thomas Edison. Henry Ford literally picked up Edison's Menlo Park laboratory and moved it from New Jersey to Dearborn, Michigan. Rich grew up visiting it as a kid. What struck him wasn't the inventions. It was the room — big, open, everyone working shoulder to shoulder on different experiments at the same time, where a failure on the lightbulb could spark a breakthrough on the telephone transceiver. That energy, that camaraderie, that creative friction between smart people — Rich built Menlo around it. Why open offices don't work — and why Menlo's does. Most open office layouts create noise and anxiety. Menlo's creates clarity and connection. Rich explains why 25 years of evidence shows the introvert/extrovert split doesn't predict who thrives at Menlo. The difference isn't the layout. It's whether there's a real culture underneath it. Which leads directly to the thing most leaders get wrong. They have people whose job title is "High-Tech Anthropologist." Their entire job is to observe end users in their native environment, the way a scientist would, without influencing what they see. The insight Rich keeps coming back to: the genius is in the end users. Most companies build products for imaginary users. Menlo studies real ones. And the anthropologists sit right alongside the development teams so what they learn never sits in a report nobody reads. Menlo reorganizes itself every week. Every five business days, the pairs rotate across the entire team. Rich told a story about a multibillion-dollar insurance company that admitted four programmers leaving would put them out of business. Their entire institutional knowledge was locked inside a handful of people. Menlo doesn't have that problem. Everyone has been systematically cross-trained. Anyone can pick up any project. People actually take vacations without their laptops. What leaders get wrong when they try to build this. This is the part every leader listening needs to hear. Rich has watched hundreds of inspired visitors go home and tear down their office walls, move everyone into one room, and wonder why half the team quit two months later. His answer is direct: you didn't build a culture. You built a floor plan. Menlo's workspace is a reflection of their cultural mindset — not the cause of it. Run small experiments. Iterate. Bring your people along. Don't change everything at once because humans don't work that way. LINES WORTH WRITING DOWN "I was stuck in a room full of manure and I knew there had to be a pony in there somewhere." "The challenge I was facing was not a technological challenge. This was about how do we organize the humans more effectively." "We didn't build an open and collaborative workspace. We built an open and collaborative culture. Our workspace is a reflection of our cultural mindset." "The genius is in the end users themselves. We just respect that genius enough to study it." "Four programmers leaving would put them out of business. An insurance company is in the risk mitigation business. They were willing to survive with that. That's ridiculous." "People will either leave — or worse, they'll stay and quit in place. One of the seventy percent that's disengaged, just collecting a check. Which is painful for them as well." WHAT YOU'LL TAKE AWAY FROM THIS ONE The dysfunction in your organization probably isn't a technology problem or a process problem. It's a human organization problem. Joy isn't a perk you add on top of the work. It's a structural decision about how people are organized, trusted, and led. And if you're thinking about transforming your culture, Rich's closing advice is worth the price of admission alone: don't change everything at once. Your people have to come with you, or nothing changes. CONNECT WITH RICH Rich said it himself on air: find him on LinkedIn, mention The Meridian Point in your connection request, and he'll accept and answer your questions. LinkedIn: linkedin.com/in/menloprez Free Virtual Tours of Menlo: Two to three times a month, Menlo runs free public virtual tours. No travel required. Go to menlosolutions.com and click the Tours tab. Visit in Person: Ann Arbor, Michigan. If you want to see the culture in action, they welcome visitors. Rich's Books: Joy, Inc.: How We Built a Workplace People Love Chief Joy Officer: How Great Leaders Elevate Human Energy and Eliminate Fear CONNECT WITH KUMAR Ready to explore what disruption could look like for your organization? Book a 30-minute conversation: https://tidycal.com/coachkumar/30-minute-meeting Take the Disruptor Method quiz: https://www.thedisruptormethod.com/quiz ABOUT THE HOST Kumar Dattatreyan is co-founder of Agile Meridian and co-creator of The Disruptor Method. The Meridian Point explores disruption and innovation through conversations with transformation leaders, executives, and entrepreneurs navigating change.  NEVER MISS AN EPISODE Subscribe on Spotify, Apple Podcasts, or YouTube and join us live every other Tuesday at 12:30 PM Eastern on LinkedIn, YouTube, and Facebook. #JoyInc #Leadership #OrganizationalCulture #Disruption #Innovation #AgileLeadership #WorkplaceTransformation #TheDisruptorMethod #MenloInnovations #HumanCenteredDesign #FearlessLeadership #TheMeridianPoint #ExecutiveLeadership #BusinessTransformation #FutureOfWork

    Joy as a Strategy: Inside Menlo Innovations With Rich Sheridan

  6. May 6

    AI Won't Save You If You Can't Code: Tom Stiehm's Warning

    Episode Show Notes: AI Won't Save You If You Can't Code Guest: Tom Stiehm, DevSecOps Expert & Software Engineering VeteranHost: Kumar DattatreyanDuration: ~ 34 minutes Here's something nobody in the AI space wants to say out loud. If you learned to code using AI, you have no idea what to do when it fails. And it will fail. That's not a doom prediction. That's Tom Stiehm — 30 years in software, former CTO of Coveros, now bringing those hard lessons to the public sector at Steampunk. Tom is one of the most grounded voices I've had on this show. No hype, no fear. Just a practitioner who has seen every wave of technology promise hit organizations, watched how they handled it, and has a very clear read on what's coming next. This conversation got real fast. And it stayed there. We started with a story about a hurricane. A team at Fannie Mae was mid-sprint when federal legislation dropped: anyone in Houston affected by the hurricane would get mortgage relief. In the old world, that's a back-office nightmare. Manual workarounds, endless errors, people falling through the cracks. This team did something different. They canceled the sprint. Went back to planning. And in three months — with a full month of testing to spare — they shipped the software that made it happen cleanly. That's what real business agility looks like. Not the ceremonies. Not the certifications. The actual ability to turn on a dime when the business needs you to. Tom had coached that team. He'll tell you it wasn't magic. It was a great Scrum Master, serious investment in test automation, and management that actually trusted the team to drive. All three had to be there. None of it happened overnight. Then we talked about security. The thing everyone ignores until it's too late. Tom called application security the poster child for third-class citizens in software development — behind even QA. Security was the thing you did in the last week before a release, when there was no time to fix anything. So you'd negotiate which vulnerabilities were acceptable to ship with. And then just hope. DevSecOps flips that. Security moves left — into the daily build, not the last-minute gate. Tom has spent years making that shift happen at financial institutions and government agencies. The organizations that resist it aren't just creating compliance risk. They're creating business risk. Here's where it gets uncomfortable for anyone betting big on AI right now. Tom's framing is not boom or doom. It's something more useful. AI is a real productivity tool. Used well, it genuinely changes what a developer can accomplish. But here's his analogy for what it's actually like to work with an AI coding assistant: A very enthusiastic, sometimes drunk intern. They'll do a lot of things for you. Some of them brilliantly. And you have to verify everything, because when they get it wrong, they get it confidently wrong in ways that are hard to spot. The problem isn't the tool. The problem is what happens when organizations skip the fundamentals and go straight to the shortcut. Tom calls it the vibe coding trap. He compares it to Visual Basic — Microsoft gave people a powerful tool, most people used it for things it wasn't designed to do, and when something broke they had no idea how to go one layer deeper to fix it. Those codebases became a mess. VB got a reputation. Sound familiar? The Agile parallel is the part of this conversation I keep thinking about. Tom made an observation that I think is one of the most important things said on this show this year. The way Agile adoptions failed is a near-perfect preview of how AI adoptions are going to fail. The pattern is always the same. You want the benefit of a change. Doing it properly seems like a lot of work. So you do some of it and hope for the same result. With Agile, that meant bolting Scrum ceremonies onto existing structures without touching culture or incentives. With AI, it means handing everyone a license for a code assistant, skipping the training, and watching them spend six months in trial and error developing patterns that don't work. Tom's prescription: smaller experiments. Active training. A safe place to practice — what he calls the dojo model. Get experience before you get burned. We also talked about what the airline industry figured out that software hasn't. Most commercial pilots will never face an autopilot failure in a 40-year career. But the industry puts every pilot through simulator scenarios for exactly those situations anyway. Because when it does happen, you need to know what to do without thinking about it. Software teams need the same thing. Not for emergencies they'll face every day — for the AI failure modes they'll only see once in a while, but that will be catastrophic if no one knows how to handle them. Tom thinks we'll get there. We're just not there yet. One more thing worth your attention: BDD and AI. Behavior-driven development is a test-first approach where you write tests before you write software — grounding development in how real users actually move through a system. Tom co-authored research on using large language models to accelerate that process. It's structured, disciplined, and produces real value. It's the opposite of vibe coding. And it's a useful model for what AI-assisted software development looks like when there's an actual framework underneath it. Tom closed with where he's headed. After years in the commercial world, he's joined Steampunk — focused on bringing better software practices to government. Same work. Higher stakes. If you lead teams, make technology decisions, or are trying to figure out where AI actually fits in your organization — this episode is for you. Hit subscribe. You won't want to miss what's coming next. CONNECT WITH TOM STIEHM LinkedIn: https://www.linkedin.com/in/stiehm/ Steampunk, Inc.: https://steampunk.com Tom co-authored a paper on using LLMs to automate BDD — reach out to him directly on LinkedIn for the link. CONNECT WITH KUMAR  LinkedIn: https://www.linkedin.com/in/kumardattatreyan/  Website: https://www.agilemeridian.com Book a 30-minute call: https://tidycal.com/coachkumar/30-minute-meeting New episodes every other Tuesday at 12:30 PM Eastern — live on LinkedIn, YouTube, and Facebook. RELATED EPISODES WORTH YOUR TIME Episode 166 — International by Design If the Fannie Mae agility story resonated, this one goes deeper on what systemic agility actually requires across teams and borders. Episode 162 — When Doing Scrum, Don't Do Scrum The trap of following the framework instead of solving the problem. Everything Tom said about how Agile goes sideways lives in this episode too. Episode 152 — From Agile to AI Avoiding the same transformation mistakes when the methodology changes but the culture doesn't. The perfect companion to this one. The Meridian Point is hosted by Kumar Dattatreyan, co-founder of Agile Meridian and co-creator of the Disruptor Method. New episodes every Tuesday at 12:30 PM Eastern.

    AI Won't Save You If You Can't Code: Tom Stiehm's Warning

  7. Apr 21

    Co-Intelligence: Why Learning Together Beats Knowing Together

    Co-Intelligence: Why Learning Together Beats Knowing Together Show Notes | The Meridian Point Podcast Guest: Diana Larsen Here is a question worth sitting with for a moment. Your organization has spent years hiring smart people, building knowledge repositories, documenting processes, and training staff. You have accumulated a lot. So why does it feel like the moment something genuinely new shows up, the whole machine slows down? Diana Larsen has an answer. And it is not a comfortable one. The problem is not that your people don't know enough. The problem is that knowing and learning are two different things. And most organizations have spent decades optimizing for the wrong one. That's what this episode is about. WHO IS DIANA LARSEN? Diana has been in this world longer than most. Over thirty years working at the intersection of teams, learning, and leadership. She came to Agile before there was an Agile Manifesto, through a discipline called socio-technical systems design, which is a fancy way of saying she was already thinking about how people collaborate to get hard things done. She co-authored Agile Retrospectives, one of the most dog-eared books in the field. She co-wrote Liftoff, which is still the go-to guide for getting teams started well. Her most recent book, Lead Without Blame, written with Tricia Broderick, tackles the culture problem that sits underneath almost every team failure. She also co-originated the Agile Fluency® Model, which is worth knowing about if you have ever felt like the Agile frameworks your organization adopted were designed for someone else's problems. She is sharp, candid, and genuinely funny. This was a good conversation. WHAT WE GET INTO The Knowledge Work Trap Peter Drucker gave us the term "knowledge worker." It was a useful frame. Lawyers, accountants, analysts: people whose value comes from what they know and how they apply it. That model worked for a long time. It is not enough anymore. Diana makes a distinction that I have not been able to stop thinking about since our prep call. Knowledge work is about applying what you already know. Learning work is about noticing when the world has shifted and figuring out what that means before your competitors do. Barry O'Reilly wrote a whole book about this called Unlearn, and the core idea is the same: the knowledge that got you here may be the thing slowing you down now. AI is accelerating this problem. The things AI does well, retrieval, synthesis, pattern matching across large datasets, those are the core skills of knowledge work. So if your people are mostly doing knowledge work, you have a real problem on your hands. Not someday. Now. Learning Together Is Harder Than It Sounds Learning as an individual is one thing. You can figure out your own gaps, seek out new information, adjust your approach. Most reasonably self-aware professionals can do that. Learning as a team is a completely different skill set. Diana calls it co-intelligence, and it is one of the central ideas in Lead Without Blame. It is the shared body of understanding that a team builds together through working, failing, reflecting, and adjusting. You cannot build it by aggregating individual expertise. You cannot buy it. You have to grow it deliberately. Most organizations have no idea how to do this. They run retrospectives that produce action items nobody follows through on. They hold all-hands meetings that feel more like announcements than conversations. They promote their best individual performers into leadership and then wonder why the team dynamic shifts. Why Blame Is the Real Enemy You cannot build a learning culture inside a blame culture. Full stop. When people are protecting themselves from consequences, they are not taking risks. When they are not taking risks, they are not learning anything new. When they are not learning anything new, the organization stagnates while the market keeps moving. Diana and Tricia Broderick designed the Lead Without Blame framework around this reality. The shift they are asking for is not soft. It is moving from "I hold you accountable" to "we take responsibility together." That changes who shows up to work and how. Valiant Leaders Diana has a concept she calls valiant leaders, and it is worth understanding clearly. Valiant does not mean fearless or visionary or any of the other adjectives that get attached to executive leadership. It means courageous enough to try things that might not work. Caring enough about the people doing the work to create the conditions for them to succeed. Willing to get out of the way when the team knows more than you do. That last part is where most leaders struggle. And it is exactly where the learning work has to start at the top. The Agile Fluency Model: A Framework That Asks a Question First Most Agile frameworks tell you what to do. The Agile Fluency® Model starts by asking what you actually need. That is a different posture entirely. Diana co-created it with James Shore because the "one right way" conversation about Agile was not matching their experience on the ground. Different organizations need different things from their teams. A team supporting internal tooling has different requirements than a team building a continuously deployed software product. The model helps you get clear on what you actually need before you decide how to get there. Free white paper at agilefluency.org. Worth the read. One Thing She Changed Her Mind On Diana spent years going to the mat for co-located teams. Teams had to be together. That was the position. She changed her mind. In-person time still matters. If you want a high-performing team, you should plan for it intentionally. But it is no longer the prerequisite she once believed it to be. With the right tools and practices, distributed teams can be genuinely high-performing. The fundamentals still apply. They just have to be applied more deliberately. What's Coming Next for Diana She is building a new program called Thrive in Turbulent Times. Small groups, a mix of in-person immersions and virtual sessions, designed for middle managers and senior leaders who are responsible for creating work environments for their teams. Because the quality of that environment determines everything that happens downstream. If you are feeling the weight of that responsibility right now, this is worth knowing about. BOOKS AND RESOURCES MENTIONED Lead Without Blame: Building Resilient Learning Teams — Diana Larsen and Tricia Broderick Agile Retrospectives: Making Good Teams Great (2nd Edition) — Diana Larsen and Esther Derby Liftoff: Start and Sustain Successful Agile Teams — Diana Larsen and Ainsley Nies Unlearn: Let Go of Past Success to Achieve Extraordinary Results — Barry O'Reilly StrengthsFinder 2.0 — Tom Rath Agile Fluency® Model white paper (free): https://www.agilefluency.org CONNECT WITH DIANA Website: https://www.dianalarsen.com Go to the top of her website and hit Subscribe. You can choose to receive her newsletter, workshop announcements, or both. Details about Thrive in Turbulent Times will land there first. LinkedIn: https://www.linkedin.com/in/dianalarsenagileswd/ CONNECT WITH KUMAR If this conversation sparked something for you, let's talk about what it means for your organization. Book a call: https://tidycal.com/coachkumar/30-minute-meeting Take the Disruptor Method assessment: https://www.thedisruptormethod.com/quiz LinkedIn: https://www.linkedin.com/in/kumardattatreyan/ Website: https://www.agilemeridian.com The Meridian Point goes live every Tuesday at 12:30 PM Eastern on LinkedIn, YouTube, and Facebook. If you are not subscribed yet, now is a good time. The Meridian Point Podcast | Agile Meridian

    Co-Intelligence: Why Learning Together Beats Knowing Together

  8. Apr 7

    Why Your Best People Keep Getting in Each Other's Way | Ryan Behrman

    Why Your Best People Keep Getting in Each Other's Way | Ryan Behrman Guest: Ryan Behrman, Owner & CEO of StrongSuits and Principal at Touchthink Host: Kumar Dattatreyan Episode Date: April 7, 2025 Watch on YouTube Why You Need to Listen to This Episode Your best people are getting in each other's way. They are not doing it on purpose. Most of them do not even know it is happening. And the personality assessments you have been using to fix it are part of the problem. Ryan Behrman has spent years watching high-performing individuals become the source of team friction, not because of their weaknesses, but because of their overplayed strengths. The empathetic leader who becomes the pushover. The detail-oriented analyst who becomes the bottleneck. The optimist who misses the risk everyone else saw coming. Every strength has a shadow version, and most teams have no idea theirs are on display. In this conversation, Ryan shares how StrongSuits, a card-based team development system he now owns and runs, replaces the PDF report with something far more powerful: your teammates telling you who you are, in real time, in the room. The feedback is direct, the format is playful, and the results are the kind that stick because people experienced them together rather than reading about themselves alone. If you work with teams, coach leaders, or have ever wondered why smart people keep creating friction for each other, this episode will give you a new way to see it. What You Will Learn in This Episode Why Your Assessment is Missing the Most Important Variable Sitting alone, answering 60 questions, and receiving a report tells you something about yourself. It tells you almost nothing about your team. Ryan explains why the real insight comes from the live, face-to-face moment when a colleague hands you a card and says "I see this in you," and why that experience changes the dynamic in a way that asynchronous assessments cannot replicate. The team is the unit of analysis. The report treats the individual as the unit. That gap is where most team development falls apart. The Overplayed Strength You Are Not Tracking StrongSuits is built on a concept that sounds simple until you apply it: every strength has an overplayed version, and that overplayed version is usually the source of team friction. Ryan walks through how the system surfaces those patterns not through self-reporting, but through real-time feedback from the people who experience your overplays firsthand. The result is a conversation the team could never have started on their own. Opposite Strengths and What They Actually Mean Most teams try to balance personality types by filling gaps. Ryan challenges that framing entirely. If your whole team lands in the same quadrant, the question is not who is missing. The question is what that pattern is telling you about what you are trying to do and where you are likely to get in each other's way. The systemic question is always more useful than the inventory question. The Moment That Changes a Team Ryan describes what happens when someone hears from five teammates, in rapid succession, that they have been seen for a strength they never thought anyone noticed. That moment shifts something in the room that no report ever could. The feedback is no longer abstract. It is personal, specific, and delivered by the people they work with every day. Why 32% Engagement Is a Design Problem, Not a Motivation Problem Leaders reach for tools like StrongSuits because they want to understand why people are not more engaged. Ryan reframes the question. Thirty-two percent engagement is not a problem with people. It is a problem with the systems, structures, reward mechanisms, and communication patterns surrounding them. Kurt Lewin said it simply: behavior is a function of the person and their environment. Fix the environment and the behavior follows. How to Actually Try StrongSuits Ryan walks through exactly how teams can get started, from buying a physical or virtual deck and playing the out-of-the-box games, to the single-player app for solo exploration, to a two-day certified facilitator training for those who want to run it with their own teams. The full multiplayer app is in development. For now, the highest-value experience is teams playing the eight flagship games together in person or on Miro. Best Quotes from This Episode "It's not about the cards you end up with. It's the feedback you get to give people in real time." "The greatest benefit comes when the team plays the games together. Everything else is a pale version of that." "Your best people are probably not getting in each other's way on purpose. They probably don't even know it's happening." "Behavior is a function of the person and their environment. StrongSuits tries to bring in both." "If you can figure out where the flow of information is constrained and unblock it, that frees up the latent energy your organization already has." Connect with Ryan Behrman LinkedIn: https://www.linkedin.com/in/ryanbehrman/ StrongSuits: https://www.strongsuits.com Physical and virtual card decks, the free manual, the single-player app, and certified facilitator training are all available at strongsuits.com. If you want to explore becoming a licensed partner, Ryan and the full network of licensed trainers are listed on the site. Take the Disruptor Method Quiz Are you disrupting or about to be disrupted? Find out in under five minutes: https://www.thedisruptormethod.com/quiz Work with Kumar Book a 30-minute conversation: https://tidycal.com/coachkumar/30-minute-meeting

    Why Your Best People Keep Getting in Each Other's Way | Ryan Behrman
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About

The Meridian Point Podcast explores the intersection of disruption and innovation in today's rapidly evolving business landscape. While drawing on agile and lean principles, we focus on how leaders and organizations can harness disruption to drive positive change and create breakthrough innovations. Each episode features in-depth conversations with thought leaders, entrepreneurs, and change agents who share their real-world experiences and insights on transforming organizations, developing innovative solutions, and navigating change. From AI and emerging technologies to organizational transformation and leadership development, we explore how individuals and companies can not only adapt to disruption but use it as a catalyst for innovation. Whether you're a business leader looking to drive change, an entrepreneur seeking to disrupt your industry, or someone passionate about innovation, The Meridian Point Podcast offers practical strategies and inspiring stories to help you turn disruption into opportunity.

Information

  • Creator
    Agile Meridian
  • Years Active
    2024 - 2026
  • Episodes
    174
  • Rating
    Clean
  • Copyright
    © 2026 Agile Meridian
  • Show Website
    Meridian Point