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What does it take to build a stronger local economy beyond the attractions a city is already famous for? I discussed that question with Shani Coleman, Director of Community and Economic Development for Clark County, Nevada. Shani shares how Clark County is supporting the growth and identity of communities across the Las Vegas area. We talk about Chinatown, where local restaurants, small businesses, public art and streetscape improvements are helping tell a broader story about the neighborhood. She also explains how residents can enjoy what the Strip offers while finding plenty to do in other parts of the community. Our conversation also turns to artificial intelligence and the human empathy Shani believes the world needs. What can Las Vegas teach other communities about building an economy and sense of place beyond their best-known destination?  Listen and share your thoughts.

What helps an innovation earn the support, time and investment it needs to reach customers? I explore that question with David Ring, Vice President, Group Innovation at Synthomer. David's own experience taught him that meeting a technical brief does not guarantee a product will succeed. One project delivered what had been asked for, yet missed what customers actually needed. That lesson now shapes how he thinks about listening to customers, finding problems they may not voice directly, and making the case for new ideas in language decision-makers understand In this episode, of the Innovation Storytellers Show, we talk about the different stories innovation leaders need to tell scientists and senior executives, how outside partners can help companies find ideas and expertise, and why business model innovation belongs in the conversation. David shares his concerns about engineering biology and the possibility that it could create new opportunities for a polymer business. What helps good ideas move from the lab into the business, and what has helped innovation gain support in your organization? Please share your thoughts.

Why do so many brilliant innovators become less persuasive the more expert they become? In this episode of the Innovation Storytellers Show, I speak with Bob Rogers, founder of BRC Imagination Arts, about something I think many innovation leaders forget when they walk into the office: people don't leave their emotions at the door just because the conversation has become technical.   Bob has spent a career creating experiences that move people to feel something and then act. His story began as a magician at Disneyland, where he learned an idea that has stayed with him ever since. The point of a performance is not to show people how clever you are. It is to help them discover something within themselves.   The lesson became the foundation for a bigger idea: when we communicate innovation, it is about them, not us. We talk about why scientists, engineers, and technical leaders often become suspicious of emotion even though emotion frequently sits behind the reason they entered their field in the first place. Bob reminds us that many experts first fell in love with their subject as children, long before they understood the technical language, frameworks, or data that later defined their careers.   So why do we strip all of that fascination out when we present an idea to a CEO? Bob argues that if you want to change behavior, you shouldn't start with features, benefits, or a technical explanation. You need to understand the hopes, fears, problems, and ambitions of the person in front of you. We discuss why experts often know too much for their own good when it comes to communication. A CEO, by necessity, is a generalist. If your innovation story requires specialist knowledge before someone can understand why it matters, you may have already lost them. One of my favorite parts of the conversation is Bob's explanation of why the story should come before the artifact. He describes how a moon rock can become one of the most boring objects in a museum if you simply put it in a gray display case and attach a technical label. But if you first take visitors through the risk, danger, ambition, and human story of traveling to the Moon, that same gray rock becomes something extraordinary. Bob has spent decades working at the intersection of imagination, experience design, storytelling, science, and business. This conversation reminds us that innovation doesn't move forward because the facts are impressive. It moves forward when people understand why those facts matter to them. When you present your next breakthrough, do you start with the technology or with the person you need to believe in it?

Why do so many promising innovations succeed as pilots but struggle to become part of everyday operations? In this episode of the Innovation Storytellers Show, I speak with Ian Small, Innovation Lead at Expedition Engineering and convener of the Water Innovator Alliance, about the difficult work that begins after someone has already had the great idea. Ian works at the intersection of innovation, engineering, infrastructure, and the UK water sector, where new technologies have to satisfy regulators, operators, customers, commercial partners, and teams responsible for assets that may remain in service for more than a century. We talk about innovation happening behind infrastructure most of us rarely think about, from drones that can help identify leaking water mains to new technologies for removing PFAS and microplastics, improved sensing, water reuse, and lower-carbon approaches to flood defenses. But our conversation quickly moves beyond the technology itself. Ian explains why getting an innovation adopted can be harder than developing it in the first place. A startup may have a brilliant solution but struggle to identify the right problem, the right water company, or even the right person inside that organization. Meanwhile, large organizations can struggle to communicate their needs clearly enough for innovators to respond. The result is often an expensive gap between invention and adoption. One of my favorite parts of this conversation is Ian's approach to storytelling around pilots. His team deliberately refuses to tell only the polished success story. When something works, they also explain what failed, what proved difficult, and what still needs improvement. That history gives future teams a much clearer understanding of the risks and helps build confidence as a solution progresses from experiment to something ready for wider adoption. Ian also tells me why his team has virtually stopped using the word "innovation" on one major Environment Agency program. The word had become too closely associated with pilots and trials, while the actual objective was delivering lower-carbon solutions at scale and creating measurable value. We discuss why innovation investment needs a clearer view of value from the beginning, why organizations underestimate the work required after a successful pilot, and how storytelling can help secure the resources, runway, and confidence required for long-term adoption. Finally, when I ask Ian what innovation the world needs now, he turns to AI. He argues that we don't need another model or tool. It is that we may still be waiting for the moment when we truly understand how to use the technology we already have effectively. Perhaps that is the bigger lesson running through our entire conversation. Invention is only one part of innovation. The real value arrives when people understand how to adopt it, communicate it, and make it work at scale. How much of your innovation investment is still sitting somewhere between a successful pilot and real-world adoption?

How do you get employees to believe in an AI strategy when the future you are asking them to believe in has not happened yet? In this solo episode of the Innovation Storytellers Show, I want to address a question I am hearing increasingly from organizations undergoing major AI transformation: can you really manage AI-driven organizational change through storytelling? I am not sure there is another way to do it. When leadership announces an AI strategy, employees do not immediately experience that future. They experience what leaders tell them about it.  When the explanation stops at productivity, efficiency, agents, automation, and new tools, people begin writing the missing parts of the story themselves. Those private narratives tend to contain three uncomfortable words: replacement, obsolescence, and irrelevance. I have been working through this problem with a major financial services organization, and it has reinforced something I believe strongly about storytelling for AI adoption. The silence surrounding technological change can be more damaging than the technology itself. In this episode, I explain why leaders should communicate the why before the what and connect AI initiatives with the organization's wider purpose rather than simply promising productivity gains. I also talk about something many AI transformation programs seem reluctant to confront directly: fear. If employees are worried about job security, changing responsibilities, or whether their skills will still matter, avoiding those subjects will not make the concerns disappear. Leaders need to name them, discuss what is known and what remains uncertain, and provide a credible plan for how people will develop alongside the technology. This is where AI change management storytelling becomes practical. Show employees what their work could look like after the transformation. Explain what becomes easier. Describe the work they may stop doing and the skills they will have the opportunity to develop. Give them ways to provide feedback as workflows change. Then start telling the stories emerging from inside the organization. If Mary finds a better way to complete a task using AI, tell that story. If Ashok discovers a new coding workflow, let colleagues learn from it. If Juan experiments with visual AI and creates something genuinely useful, show people what happened. Those internal stories can make an AI strategy feel tangible because employees can see people like themselves participating in it. Leaders also need to use the technology themselves. Telling employees to change how they work while having little firsthand experience with AI will quickly undermine credibility. Organizations that manage AI transformation successfully may not be those with the most sophisticated technology roadmap. They may simply be the ones whose leaders never allow employees to lose sight of where the story is heading and where they belong within it. If you are responsible for communicating AI to employees, ask yourself one question. Can your people see a better version of their working lives inside the future you are asking them to build?

What happens when AI makes it easier than ever to produce work, but organizations still struggle to turn that activity into meaningful results? In this episode of the Innovation Storytellers Show, I speak with Dr. Mik Kersten, creator of the Flow Framework and founder of Tasktop. Mik is the author of Project to Product, and Output to Outcome, about why the AI era demands a very different way of thinking about innovation. He has spent much of his career studying how ideas move through organizations, from his early work at Xerox PARC to building technology for value stream management. One lesson has stayed remarkably consistent: large organizations rarely struggle because they lack ideas or ambition. They struggle because their structures, incentives, approvals, and operating models slow the feedback loops required for innovation. AI is making that problem much harder to ignore. When knowledge outputs become cheap and plentiful, measuring productivity by documents created, code written, or tasks completed begins to lose meaning. Mik argues that organizations need to move toward outcome-driven operating models that ask a much more useful question: did the work improve something for the customer, employee, or business? We discuss why some large companies can take around 120 days to move an idea to a customer, while smaller teams can test ideas within weeks. We also examine what happens when AI accelerates a poorly designed system and simply produces more work for humans to review. Mik explains his concept of the outcome loop, where strategy leads to outputs created by humans and AI, followed by customer and employee outcomes that inform the next round of decisions. For leaders thinking about how to communicate AI transformation, this changes the conversation from productivity to effectiveness. We also talk about storytelling for C-suite leaders and why internal examples may become some of the most persuasive tools for winning AI stakeholder buy-in. Instead of telling employees that AI transformation is inevitable, leaders can show where new ways of working are already producing results, what failed along the way, and what the organization has learned. Our conversation also raises a bigger question about human-AI collaboration. Mik believes AI should support decision-making, but accountability must remain with people. His view is simple: humans should continue to report to humans, particularly when organizations are deciding which outcomes technology should pursue. As AI gives organizations the ability to create more, faster, the competitive advantage may no longer come from the volume of work being produced. It may come from knowing which outcomes are worth pursuing in the first place. How is your organization measuring the difference between AI activity and genuine impact?

What if the biggest weakness in AI-powered innovation is that we are getting better at predicting human behavior while becoming worse at understanding what that behavior actually means? In this solo episode of the Innovation Storytellers Show, I want to talk about something I believe is becoming increasingly important for innovation leaders: AI can identify patterns, surface signals, and predict what customers may do next, but prediction is not the same as understanding. My background in anthropology has always shaped how I think about innovation. Anthropologist Clifford Geertz wrote about "thick description," the idea that observing human behavior without understanding its context can lead us to misunderstand what we are seeing completely. A wink and a twitch may look almost identical, but they mean very different things. AI faces a similar problem. A model might tell you that thousands of customers abandon their shopping carts at the same point. What it cannot reliably tell you is whether they are worried about money, overwhelmed by choice, consulting their family, reconsidering the purchase, or distracted by something happening in their lives. The behavior is visible but the meaning often is not. This creates an important challenge for AI storytelling for business leaders and anyone responsible for storytelling for AI innovation. As organizations gain access to more customer data, they also need people who can listen, interpret, question assumptions, and understand the cultural context behind what the data appears to show. In this episode, I share four ways innovation teams can interpret what AI cannot. I explain why AI should act as a scout rather than the storyteller, why customer stories should challenge data rather than confirm it, and why the strongest human-AI collaboration narrative combines machine intelligence with human judgment. Your competitors can increasingly access the same models, similar data, and comparable technology. The advantage may come from what your people understand that the machines cannot fully explain. If AI can tell us what customers are doing, are we spending enough time asking why?

What happens when AI gives your organization more ideas than your people actually want to own? In this solo episode of the Innovation Storytellers Show, I want to challenge something I am seeing more frequently across innovation teams: the assumption that because AI can generate hundreds of ideas in seconds, it should automatically become our ideation partner. I think we need to question that. Most organizations are not suffering from a shortage of ideas. Employees, customers, partners, suppliers, and markets are already full of observations about products that could be improved, processes that make no sense, new business models worth testing, and customer frustrations waiting to be addressed. The problem is often that organizations are not asking the right people the right questions. AI can produce options at enormous speed, but it cannot manufacture belief. It cannot create the feeling that comes from a group of people building an idea together, choosing to champion it, and taking pride in seeing it become real. That difference matters because innovation eventually depends on execution. If teams feel disconnected from the ideas they are being asked to deliver, more ideation simply creates a larger backlog. In this episode, I look at innovation storytelling in the age of AI, why human ownership still matters, and why storytelling for AI adoption must explain why a direction matters to the people expected to make it happen. I also discuss how leaders can think differently about human-AI collaboration, communicating AI to employees, and winning AI stakeholder buy-in without reducing innovation to another exercise in generating AI output. AI absolutely belongs in innovation. The bigger question is where it adds the most value and where human judgment, imagination, conviction, and conversation still need to lead. Are we using AI to help people build better ideas, or are we slowly removing people from the process that creates belief in those ideas?

What happens to visual storytelling when everyone suddenly has access to the same powerful AI tools? In this solo episode of Innovation Storytellers, I'm looking at what it takes to create visual stories that still feel distinctive, credible, and unmistakably human in an AI-generated world. AI has made it incredibly easy to create polished graphics, videos, presentations, and infographics in seconds. But when every organization can do the same thing, visual sameness quickly becomes another form of noise. Producing more content is no longer enough. The real challenge is deciding what deserves to be communicated and finding a point of view that belongs to your organization rather than an algorithm. I share practical ways innovation teams can use visual metaphor, AI-generated drafts, storyboarding tools, customer reactions, and real-world video without surrendering the judgment that makes a story matter. I also explain why the first AI-generated output should almost never be the final deliverable. The opportunity is to use AI for speed while keeping humans firmly responsible for meaning, emotion, and choice. Because when everyone can create more content, discernment becomes the real differentiator. How are you making sure your visual storytelling still feels human when AI can generate almost anything?

What if the stories you tell about innovation are actually working against you? In this episode of the Innovation Storytellers Show, I do something a little different. I open a new series by inviting other storytellers I deeply admire, people who bring their own lenses, frameworks, and lived experience to the craft of story. I want you to think about storytelling as an expansive, evolving practice, not a single narrative you perfect once and reuse forever, but a skill you keep refining as your audiences, challenges, and ambitions change. To begin that journey, I sat down with Park Howell, a 40-year veteran of brand storytelling and host of the Business of Story. Park shares how he found storytelling through advertising, why stories have a repeatable structure rooted in human biology, and what he calls the science and bewitchery behind stories that truly move people.  We unpack his deceptively simple "and, but, therefore" framework, why leaders lose rooms with bullet points, and how story becomes the bridge that helps people move from status quo thinking to real behavior change. We also explore why storytelling so often fails in organizations, especially when leaders make the story about themselves rather than their audience.  Park explains how innovation stories should focus on outcomes, not offerings, and why emotional connection must come before logic if you want ideas to stick. From the hero's journey and Joseph Campbell's influence to the reality of selling ideas in five-minute executive meetings, this conversation is packed with practical insights for anyone trying to communicate change under pressure. We close by looking at how AI fits into modern storytelling, including Park's work on the Story Cycle Genie, and why emotional intelligence combined with artificial intelligence may shape the next era of leadership communication. If innovation is ultimately about getting people to move, decide, and act, how might your stories need to change to meet them where they are, and what could happen if you finally told the story they were waiting to hear?

How do you bring discipline to innovation without stripping away the creativity that makes it powerful in the first place? In this episode of the Innovation Storytellers Show, I sit down with Stephen Parkins, Innovation Strategist and Founder of Culturedge, to unpack what it really takes to turn innovation into a strategic asset rather than a side project fueled by hope and enthusiasm.  Stephen brings an outside-in perspective shaped by an unconventional career spanning financial markets, startup entrepreneurship, and senior innovation roles within complex global organizations. That distance from the usual corporate playbook allows him to challenge some deeply held assumptions about how innovation should work and why so many well-intentioned efforts struggle to deliver measurable returns. We talk openly about the tension between creativity and structure, and why innovation does not fail because teams lack ideas, but because organizations lack clarity, consistent decision-making, and shared language. Stephen offers a thoughtful perspective on innovation management systems, including the much-debated ISO standards, and explains why guardrails are often misunderstood as constraints.  Drawing on real-world experience from large enterprises, he argues that structure, when well designed, creates the conditions for better experimentation, smarter risk-taking, and stronger alignment between innovators and the core business. The conversation also dives into strategy, funding, and culture, particularly the invisible friction between those running today's business and those inventing tomorrow's. Stephen shares how portfolio thinking, exposure to risk, and optionality can shift innovation from theater to real value creation.  We also explore his work as co-founder of Strategy Quest, a simulation-based approach that helps leaders practice decision-making under uncertainty, surface blind spots, and learn through consequence rather than theory. It is a compelling look at how scenario thinking and simulated environments can prepare the next generation of innovation leaders to see around corners. If innovation is meant to help organizations grow stronger in uncertain times, what needs to change in how leaders think about risk, culture, and decision-making, and are we brave enough to build systems that actually support that ambition?

In this episode of the Innovation Storytellers Show, I sit down with Amy Freeze, a meteorologist, innovator, and public safety advocate who has spent decades helping people understand risk when it truly matters. Everywhere I go lately, conversations circle back to AI, but this one brings it out of the abstract and straight into our homes, our screens, and moments where trust can make all the difference. Amy shares her remarkable journey from broadcast journalism to becoming one of the most recognized voices in weather. We talk about her work forecasting major events like Superstorm Sandy and the Joplin tornado, and how those experiences shaped her sense of responsibility to the public. As the first female chief meteorologist in Chicago and a six time Emmy Award winner, her career has been built on credibility and calm communication. What fascinated me most was why she chose to create a digital avatar, and how she sees AI as a way to deliver urgent, accurate information at scale without losing the human connection people rely on in moments of uncertainty. We also dig into the fears and ethical questions surrounding digital twins, AI driven storytelling, and protecting name, image, and likeness. Amy offers a grounded perspective on why avoiding new technology can sometimes create more risk than adopting it thoughtfully. Together, we explore how empathy, trust, and clear storytelling help audiences move past fear toward understanding, especially when the stakes involve safety, language barriers, and real-time decision-making. This conversation reminded me that innovation does not have to feel cold or distant. It can be practical, human, and deeply rooted in care. We talk about how trusted voices evolve with technology, how stories help people accept change, and why the future of AI may depend far less on hype and far more on responsibility, context, and trust.

What allows someone to see a technology years before the rest of us realize we need it? In this episode of the Innovation Storytellers Show, I speak with Kevin Surace, the serial entrepreneur and inventor whose pioneering work on human-like virtual assistants helped create the path toward Siri, Alexa, and the AI assistants we use today. Kevin takes me back to the late 1990s, when leaving your desktop computer meant leaving your email, contacts, calendar, and digital information behind. His team saw that frustration and asked a remarkably simple question: could everyone have access to the kind of assistant usually reserved for executives? The result was Mary, a virtual assistant that could recognize callers, answer phones, manage calendars, and converse using natural language. Building and launching it required around $100 million at a time when cloud infrastructure did not exist. But this conversation is about much more than the history of AI. Kevin explains why curiosity helps innovators find genuine customer pain, why a product should occupy surprisingly little space in a great investment pitch, and why corporations should stop expecting every innovation project to succeed. His argument is that experimentation works when organizations place smaller bets, learn from the market, and invest further when product timing and product-market fit become clearer.   We also discuss The Joy Success Cycle and a question becoming increasingly relevant as AI changes knowledge work: if technology removes parts of the process we once enjoyed, can we learn to find greater satisfaction in the outcomes we create? For leaders trying to understand storytelling for AI innovation, win AI stakeholder buy-in, or how to communicate AI transformation without making technology the entire story, Kevin offers an unusually practical perspective. Where do you think curiosity ends and the business case for innovation begins? Listen to the episode and share your thoughts with me.

What does it take to create a store that people visit even when they are not planning to buy anything? In this episode of the Innovation Storytellers Show, I speak with Ron Johnson, the creator of the Apple Store, former CEO of J.C. Penney, and author of Shop Different: How Retail Revealed Apple's Genius. Ron shares the experiences that shaped his approach to leadership, beginning with an unlikely high school soccer transformation and continuing through his work at Target, Apple, and some of the most influential retail innovations of the past several decades. Ron explains how Target made great design accessible through its groundbreaking partnership with architect Michael Graves, and why committing to 140 products created a movement that a cautious three-product test never could have achieved. He also takes us inside the creation of the Apple Store, where the objective was not simply to sell computers, but to remove people's fear of technology through welcoming spaces, hands-on experiences, and human support. We hear the story behind the Genius Bar, including why Steve Jobs initially preferred the name Geek Bar, and how Ron persuaded him that genius offered employees and customers something aspirational. Ron also reveals why the Apple Store on Fifth Avenue was built underground, how its glass cube changed the Manhattan skyline, and why opening it 24 hours a day generated $80 million in overnight sales during its first year. Our conversation goes beyond retail. It is about finding a story that gives people something to believe in, communicating a vision clearly enough that teams can act on it, and having the courage to commit fully when an idea has the potential to change an industry. Can physical spaces still create the human connections that digital convenience often removes, and what could your organization learn from the story behind the Apple Store? Share your thoughts.

What happens when everyone in an organization claims to support innovation, but each person has a different idea of what innovation means? In this episode of the Innovation Storytellers Show, I speak with Iffat AlRowaithy, Corporate Innovation and Innovation Center Unit Head at a globally recognized bank in Saudi Arabia. Iffat has spent more than a decade building innovation functions, developing employee capabilities, and connecting new ideas with corporate strategy and measurable business value. We begin with a problem many innovation leaders will recognize about how people define innovation. One department may view innovation as a new product, another may focus on customer experience, while others see a system update as progress. Iffat explains why leaders must establish a shared language before asking people to innovate, including clear expectations around purpose, ownership, timelines, and outcomes. Iffat also shares what she learned while building innovation centers across the public and private sectors. A creative space may look impressive, but it cannot succeed if the organization has not developed the culture, sponsorship, and capabilities required to use it. Her current center was designed around the five senses, creating an experience that helps employees think differently, contribute ideas, and participate in building solutions. We discuss why open calls for ideas can produce large volumes of suggestions without solving the problems business units actually face. Iffat describes how her team reversed the process by asking departments to submit one real challenge and inviting employees across the organization to propose solutions. This approach allowed an IT employee to solve an HR communication problem and helped turn employee insight into an implemented broadcast capability. Our conversation also examines the relationship between AI and human creativity. Iffat shares how employees used AI to move from ideation to a working prototype in around two hours, while emphasizing that human judgment, context, and empathy must remain part of the process. Perhaps the most powerful lesson is that people respond to experiences, participation, and being heard far more readily than they respond to promises. Innovation becomes part of the culture when employees can see their contribution, understand its connection to strategy, and feel included in creating what comes next. Can a shared language, a clearly defined challenge, and greater empathy help your organization turn innovation from an ambition into everyday behavior? Listen to the conversation and share your thoughts.