Jon Hirst has spent three decades helping organizations do the hardest thing most of them never quite manage: renew themselves. In the age of AI, he has arrived at a framework for navigating the moment that most leaders have not yet considered, and a final piece of advice that cuts through all the noise.

Every organization that Jon Hirst has ever worked with wants the same thing. It wants to be relevant to the people it serves in the future, to renew itself for what is coming rather than remain anchored to what has passed. While the desire is common, the rate of success is actually fairly low.The reason, Hirst has come to believe, is structural: organizations are almost always designed to deliver on value based on past performance, and the gap between past success and what the future requires is wider than most leaders are willing to honestly measure.

"They tend to not have a way to get from one to the other," he says.

Hirst is the founder of Innovation In Mission, a fractional Chief Innovation Officer practice in which he works deeply with four to six organizations at a time, going further than a traditional consultant would without the overhead of full-time placement. He also runs Generous Mind, which he and his wife Mindy have operated for twenty years around the idea everyone has an idea worth sharing.. Together, these two ventures describe a career organized around a single conviction: that ideas matter, that organizations need to cultivate their ability to bring them forward, and that most institutions are getting this wrong in ways they do not fully understand.

Designed for the Past

The organizational renewal problem is older than AI, and more stubborn than most leaders want to admit. Research from Boston Consulting Group and McKinsey consistently shows that roughly 70 percent of digital transformation initiatives fail to meet their objectives. A 2024 analysis by Bain found that 88 percent of business transformations fail to achieve their original ambitions. The pattern is remarkably consistent across industries, geographies, and types of organizations, and Hirst has a precise explanation for why it persists.

The problem is not the absence of good ideas. It is the absence of a built capability for moving those ideas through an organization in a way that turns them into something real. One-off innovation projects can generate excitement and the occasional win, and individuals with genuine creative drive can produce breakthroughs under almost any conditions. But neither of those things constitutes the kind of durable, systematic capacity for renewal that allows an organization to continuously adapt. Until an institution builds that capacity, its ability to reinvent itself remains dependent on luck and individual personalities rather than on anything it can rely on.

"Until you build a capability within an organization that helps you identify who has a new idea and shepherd that idea all the way through the process until it's your next million-dollar program," Hirst says, "your ability to renew yourself for the future is very low, and it's very unlikely to happen."

He grounds this in something he has observed consistently across his career: human nature defaults to the familiar. When we lack a clear path for doing something difficult, we simply do not do it. We reach instead for what we already know, what worked before, what the organization was built around. The absence of a pathway for innovation is not neutral. It is a structural guarantee that the familiar will win, regardless of how urgent the need for change becomes.

A lone innovator with a compelling idea can solve today's problem. A built capability for innovation changes what an organization is capable of becoming.

Everyone Has Ideas

One of the first things Hirst does when he enters an organization is challenge a belief he encounters almost universally: that innovation is the province of a particular kind of person, and that most people simply are not that kind of person.

"People say, oh, I'm not creative. I'm not an innovator," he says. "And the people that say that tend to be some of our core operational folks, who are amazing at running things, but don't necessarily get a lot of joy or spend a lot of time coming up with new ideas."

His response is to push them toward a different understanding of what an idea actually is. An idea, in his framework, is valuable in proportion to its capacity to solve a problem and create value for someone. By that definition, a better way to structure an Excel spreadsheet is as valid an idea as the design of a new product category. The scale differs. The underlying act of human imagination does not. Humans are, he argues, fundamentally designed to have ideas, and that is true regardless of whether the ideas in question are incremental, transformational, or somewhere between.

The distinction matters because organizations that reserve the language of innovation for their most dramatic initiatives will chronically undervalue the incremental improvements that compound over time into competitive advantage. Incremental innovation, capability innovation, and more transformational programmatic innovation all require the same raw material: a person who sees something that could be different and is willing to say so. Building the conditions for that to happen at every level is what Hirst means when he talks about building a capability, not just a pipeline of bold ideas.

Supporting vs. Imitating

When Hirst thinks about where AI fits into the innovation process, he draws a distinction that he believes most organizations are not yet making carefully enough.

There is a difference, he says, between AI that supports the idea generation process and AI that imitates it. The first is an extension of a long tradition of tools that help human beings think more expansively: facilitation frameworks, brainstorming exercises, structured creative processes. The second is something different, and in his view something that tends to produce disappointing results when applied to the parts of the innovation process that depend on a uniquely human creative spark.

"There's something that's very unique about a certain part of that creative process of ideating as a human," he says. "AI can imitate, and maybe by sheer brute force of giving you three thousand ideas and synthesizing down to the top three, it can come up with some decent things to consider. But there's something about ideating as a human that it doesn't do."

His practical conclusion is that the question of where AI belongs in any given creative process is not a question with a fixed answer. There is no universal checklist that says AI always belongs here and never belongs there. The task is discernment: understanding, for a particular problem in a particular organization with a particular team, which parts of the process benefit from what AI does well, and which parts require human contribution that the tools cannot replicate. When he has been willing to give AI the right parts of the job, he has found it genuinely supercharges the work. When he has given it the wrong parts, it has produced competent-looking output that lacks the quality that makes ideas actually move through an organization and take root.

The Third Way

When Hirst is asked what he would tell every leader navigating the current AI moment, he offers a framework that he describes as the third way: not ideological, not purely pragmatic, but practical and values-based simultaneously.

The ideological response to AI positions organizations on a fixed point of a spectrum and builds every decision from there. No AI, AI-first, cautious adoption, aggressive deployment: whatever the position, it becomes the lens through which every question is answered before it is fully examined. Hirst's concern with that approach is not that the positions are wrong but that the certainty is unwarranted. The technology is changing too quickly, the implications are too complex, and the organizational situations are too varied for a predetermined stance to reliably serve any given institution well.

The purely pragmatic response is equally problematic, though for a different reason. Pragmatism, in its straightforward definition, means doing whatever works. In the context of AI, Hirst argues, that approach strips away the values-based guardrails that protect an organization from moving quickly in a direction it did not actually intend to go. "AI will allow you to do bad things faster," he says, plainly and without elaboration.

The third way holds both in tension. It takes values seriously as the compass for daily decisions about how and where to use AI, while remaining genuinely practical about the fact that those decisions will need to be made quickly, repeatedly, and in circumstances that will keep changing. The leaders who navigate this moment well, he believes, will be the ones who have done the harder work of clarifying what they actually value before they had to decide how to use the technology. Values are not a constraint on effective AI adoption. They are the mechanism that keeps adoption coherent when everything else is moving fast.

"When you have a clarity about your values," he says, "you can do a quick gut check measurement of: am I using AI in a way that aligns with that or not? You're gonna know. It's not rocket science."

The organizations that get this right are not necessarily the ones moving fastest. They are the ones that have built enough internal clarity to make a hundred daily technology decisions with integrity, without having to convene a meeting about each one.

Know What You're Protecting

His final thought, offered at the end of the conversation, is perhaps the most direct thing he says.

"In our rush to use these new tools, do everything you can to know what your core contribution is. Protect it. And amplify it with the tools, rather than use them to essentially diminish what your core contribution is."

The distinction between amplifying and diminishing is the whole of the argument in compressed form. The same tool, applied differently, produces opposite outcomes: one version of AI adoption makes a person or organization more of what it actually is; another version quietly replaces the thing that made it distinctive in the first place. The question of which outcome you are producing is one that most organizations are not yet asking often enough, or clearly enough, or honestly enough about the specific decisions they are making every day.

Hirst has spent twenty years watching organizations try to become more relevant to the future and fail because they treated innovation as an event rather than a capability, as something that happened to organizations rather than something they built the ability to do. The age of AI has compressed both the urgency and the consequences of that choice. The window for building the capability, rather than reacting to its absence, is narrower than it has ever been. And the organizations that understand what they are actually protecting, before they decide what they are willing to accelerate, are the ones most likely to still recognize themselves on the other side of this moment.

Jon Hirst is the founder of Innovation and Mission and co-founder of Generous Mind, where he has spent thirty years helping individuals and organizations learn to advance ideas with purpose. This conversation is part of the Humans & AI: Founder Series by HumanCulture.

Sources

Mavim. (2025, October 23). Why 70% of digital transformations fail: Insights and solutions. Citing research from Boston Consulting Group, McKinsey, and Bain & Company (2024). https://blog.mavim.com/why-70-of-digital-transformations-fail-insights-and-solutions

Furstenthal, L., Jorge, F., & Roth, E. (2022, August 17). For innovation to thrive, it needs an ecosystem. McKinsey & Company. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/for-innovation-to-thrive-it-needs-an-ecosystem