Innovation Storytellers

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Innovation Storytellers Equip your people with the skills they need to effectively communicate their breakthrough ideas.

10/07/2026

Why can a six-person company sometimes test an idea within weeks while an enterprise with enormous budgets, talent, and technology takes months?

Dr. Mik Kersten has spent years looking at how ideas move through organizations. One finding from that work stayed with me.

In data he examined from large organizations, the average time required to move an idea through the organization and reach a customer was around 120 days. The problem was not necessarily creativity. It was the feedback loop.

Innovation depends on testing assumptions. You put something in front of a customer, learn whether it works, make changes, and try again.

Every approval, handoff, organizational boundary, and internal dependency increases the distance between the original idea and the customer response.

AI now makes this even more interesting because technology can dramatically increase the speed at which individual teams work without fixing the organization around them. You can give people AI tools that operate in seconds and still make them wait weeks for decisions.

For leaders thinking about how to lead AI culture change, this is an organizational question as much as a technology question. Faster tools inside a slow operating model will only get you so far.

Where does innovation slow down inside your organization?

https://zurl.co/SKgGN

10/06/2026

AI is making it extraordinarily easy to produce more work. That does not automatically mean organizations are becoming more effective.

In my conversation with Dr. Mik Kersten, we discussed one of the biggest management questions emerging from AI: what happens when the outputs we have spent decades measuring suddenly become incredibly cheap to create?

Documents, presentations, research, analysis, software, and countless other forms of knowledge work can now be produced at a scale that would have been impossible only a few years ago.

More output can also mean more work for somebody else. More documents to read. More code to review. More proposals to assess. More AI-generated material competing for attention.

Mik argues that organizations therefore need to rethink what they measure. The useful question is no longer simply, "How much did we produce?" It becomes, "What changed because we produced it?"

Did the customer experience improve? Did an employee problem disappear? Did the organization learn something faster? Did the work contribute to a business outcome?

This is where storytelling for AI adoption becomes particularly important. If leaders continue communicating AI transformation primarily through productivity statistics, they risk measuring activity rather than impact. AI can help us create more.

The management challenge is making sure more actually means better. How is your organization measuring AI success today?

https://zurl.co/SXj8Q

09/29/2026

Prediction is not understanding. This is something that matters more as organizations become increasingly dependent on AI-powered customer insights.

AI can tell us what customers click, what they buy, when they leave a shopping cart, what they compare, and what they are likely to do next. That is enormously valuable. But identical behavior can represent completely different human experiences.

A customer hesitating over a purchase could be worried about money. They could be overwhelmed by choice. They might need to talk with their partner. They could be uncertain about whether the product is right for them. Or they may have been distracted.

The data records the hesitation. It doesn't necessarily explain what it means. This is where I think human judgment becomes increasingly valuable. AI storytelling for business leaders cannot simply translate dashboards into presentations. We need to reconnect the data with the lives, identities, motivations, and circumstances behind those numbers.

Organizations that understand this will have something their competitors can't get simply by buying access to the same AI models. They will understand what their customers mean, not simply what they do.

How much of your customer insight tells you what happened, and how much genuinely explains why?

https://zurl.co/Q3OcH

09/25/2026

For years, innovation teams have been told that they need more ideas. AI may have solved that problem so effectively that it has exposed a much bigger one.

Organizations can now generate enormous numbers of concepts, variations, recommendations, and potential initiatives. But if nobody wants to put their name next to those ideas, the bottleneck has simply moved, the challenge becomes ownership.

I remember the energy that comes from a good brainstorming session when a group arrives at something together and somebody immediately says, "I'll champion that."

That moment matters because innovation is eventually a social process. Someone has to persuade a budget holder. Someone has to convince colleagues to change how they work. Someone has to explain the idea to customers. Someone has to keep supporting it when the first implementation problem appears.

That is why corporate storytelling strategy becomes so important when organizations adopt AI. Storytelling for AI adoption is partly about explaining technology, but it is also about restoring the human connection between an idea and the people expected to deliver it.

Perhaps the question innovation teams should be asking is no longer, "How can we generate more ideas?" It might be, "Which ideas are our people willing to own?"

What are you seeing inside your organization?

https://zurl.co/11euq https://zurl.co/m1Yyt

09/23/2026

Are we beginning to confuse using AI everywhere with using AI well? Organizations feel understandable pressure to prove that teams are using AI. Budgets are being allocated, executives are asking questions, employees are being encouraged to experiment, and almost every workflow is being examined for possible automation.

But innovation leaders should be careful not to assume that every stage of the innovation process improves simply because AI is involved. Ideation is one example.

A human brainstorming session may take longer than asking a model to generate 50 concepts. It may involve disagreement, repetition, and ideas that go nowhere. But those conversations also create shared understanding. People hear why colleagues care about a problem. They challenge assumptions. They build on each other's thinking. Eventually, somebody becomes willing to put their name next to an idea and champion it.

Ownership has value. The goal of human-AI collaboration should not be to remove people from every process where AI can produce a faster answer. It should be to decide where machines improve the work and where human participation improves the chances that anything actually happens afterward.

For innovation leaders, that distinction may become more important than the number of AI tools available to the team.

Where do you think AI belongs in the ideation process?

https://zurl.co/BqnKF

09/22/2026

AI can generate 100 ideas before your coffee gets cold. But generating ideas and creating conviction are two very different things.

The conversation around AI often focuses on speed, volume, and productivity. Those benefits are real. But innovation does not end when an idea appears on a screen. Someone still has to believe in it, advocate for it, persuade others to support it, and carry it through the difficult work of ex*****on. This is where I think many organizations are creating a problem for themselves.

When ideas emerge from a room full of people debating, challenging, improving, and eventually choosing a direction together, the outcome usually carries a degree of ownership. When hundreds of AI-generated ideas arrive almost instantly, that emotional connection can disappear.

This is why AI storytelling for business leaders matters. The job is no longer simply to present an idea. Leaders need to explain why this direction matters, why people should care, and why the disruption involved is worth it.

AI can increase the number of possibilities available to us. It cannot decide which possibility people will believe in.

How are you maintaining human ownership as AI becomes more involved in innovation?

https://zurl.co/QJypR

09/18/2026

One of my biggest concerns about AI has very little to do with what the technology can generate. I’m much more interested in what happens when we gradually stop making decisions for ourselves.

AI can suggest the infographic, choose the image, recommend the structure, propose the headline, summarize the audience, and confidently tell us that every idea is a good one.

That convenience is useful, but it can also make us passive.

For innovation leaders, communication teams, and anyone responsible for storytelling, I think there is an important line to protect. Let AI generate possibilities, but keep the judgment about what matters with the humans who actually understand the organization, the audience, the stakes, and the story.

Speed was once a competitive advantage in content creation. AI is rapidly removing that advantage because everybody can now move quickly. Discernment is becoming far more valuable.

]In this clip from my latest Innovation Storytellers episode, I explain why choosing well may matter more than generating more.

Where do you think human judgment needs to remain firmly in control as AI becomes more capable?

https://zurl.co/yxKzB

09/18/2026

One of my biggest concerns about AI has very little to do with what the technology can generate. I’m much more interested in what happens when we gradually stop making decisions for ourselves.

AI can suggest the infographic, choose the image, recommend the structure, propose the headline, summarize the audience, and confidently tell us that every idea is a good one.

That convenience is useful, but it can also make us passive.

For innovation leaders, communication teams, and anyone responsible for storytelling, I think there is an important line to protect. Let AI generate possibilities, but keep the judgment about what matters with the humans who actually understand the organization, the audience, the stakes, and the story.

Speed was once a competitive advantage in content creation. AI is rapidly removing that advantage because everybody can now move quickly. Discernment is becoming far more valuable.

In this clip from my latest Innovation Storytellers episode, I explain why choosing well may matter more than generating more.

Where do you think human judgment needs to remain firmly in control as AI becomes more capable?

https://zurl.co/bu3qv

09/16/2026

AI has made visual storytelling dramatically easier to produce, but I’m increasingly convinced that it has also made distinctive visual storytelling much harder.

When every organization has access to similar image generators, video tools, presentation platforms, and design assistants, simply producing something polished is no longer enough. Your competitor can probably create something equally polished before lunch.

The question is no longer whether you can produce the visual. The question is whether that visual communicates a point of view that genuinely belongs to your organization.

I have seen teams do everything they were supposedly meant to do. The product is strong, the campaign launches on time, the visuals look professional, and the social content is ready. Then very little happens.

The problem is often not volume or production quality. It is distinctiveness.

In this clip from my latest Innovation Storytellers solo episode, I talk about why the bar for visual storytelling has moved and why human judgment matters more as AI-generated content becomes easier to create.

Are we using AI to make our stories stronger, or simply making more of them?

https://zurl.co/y6A3A

09/08/2026

One of the biggest mistakes I see leaders make when telling stories about innovation is assuming the story should center on the innovation itself.

In this clip from the Innovation Storytellers Show, Park Howell explains why that instinct backfires and why even the most impressive product, initiative, or breakthrough falls flat when the audience cannot see themselves in it.

Park puts it plainly. People do not care about your innovation, your brand, or your widget. They care about what changes in their lives result from it. When storytelling shifts from self-focused to audience-focused, something important happens. Trust starts to form. Understanding, appreciation, and empathy come into play. And suddenly, the story has weight.

What struck me most in this moment is how tightly story and trust are linked. When leaders make the story about the audience, they show that they understand what people want, why it matters, and what is standing in the way. That is when stories stop being slides and start becoming catalysts for action.

Curious to hear your take. Where have you seen stories fail because they focused on the offering instead of the outcome?

https://zurl.co/i5fvT

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