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.

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 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?