AI disruption is not a distant risk on the horizon, it is the ground already shifting under every assumption leaders hold about value, talent and competitive advantage. The real danger is no longer that AI fails, but that executives underestimate how profoundly it will rewrite the rules of opportunity.
Seven thousand people in a darkened Chicago ballroom are surrounded by robots, production cells and AI software. Jeremy Gutsche, CEO of Trend Hunter and award winning author, walks onto the Rockwell Automation Fair 2025 keynote stage with a simple promise: your next breakthrough is closer than you think. The technology on display is impressive, but his real subject is something far less tangible and much harder to manage, the way leaders think when the rules of the game change.
He frames the moment without any hedging. “You are experiencing history’s highest rate of change, disruption, ambition and opportunity,” Gutsche says. “There has never been a better time to be creators in the field of automation. You are literally thinking about the robotics, artificial intelligence and the next generation of business that will change our culture, but now is not the time to stay still. It is the time for action.”
The message cuts directly against the instinct that many executives still cling to, which is to “wait and see” until AI disruption becomes clearer or safer. In Gutsche’s view, that instinct is a symptom of path dependency and success bias, not prudent risk management. The organisations that win the next decade will be those that treat AI as an inflection point, not a toolkit upgrade.
Reimagining value at an AI inflection point
To explain what an inflection point really means, Gutsche reaches back to the caveman discovering fire and the schoolchild discovering the calculator. Fire transformed what humans could do with heat and power. Calculators transformed what humans needed to do with mental arithmetic. AI, he argues, sits in that lineage but cuts closer to the core of work itself.
“The question of what opportunity lies so close within your grasp now carries a different weight,” Gutsche says. “You can be an author, an artist, an actor, an influencer, a musician, a programmer, a scientist, a game designer. You can replicate yourself. You have a little chatbot that now has agentic powers to actually carry out your dreams and wishes with genius IQ and access to every fact, every language, every word ever spoken.”
That shift is not about a few tools on a laptop. It is about changing the relationship between human judgment and machine capability. If every executive can, in principle, deploy agents that scan markets, synthesise data, simulate scenarios and draft strategies, then the question “what is your next level?” stops being a motivational slogan and becomes a strategic demand.
Gutsche is clear that most leaders are still stuck in first order thinking. They see AI disruption in terms of obvious impacts such as productivity gains, headcount reductions and cost savings in repetitive work. The deeper value sits in the second and third order effects, where agentic systems reconfigure what organisations can sense, decide and execute. “When something new happens that is big, our cave brain is good at thinking about the immediate impacts,” Gutsche explains. “The true imagination of what happens next is much harder, because there is a whole set of extra industries, behaviours and opportunities that your current mental model simply does not see.”
For AI, that might include entirely new service lines that are only viable when continuous market sensing becomes trivial, or new forms of partnership that emerge when every company can expose its capabilities through intelligent interfaces. Those possibilities are not immediately obvious from a standard business case. They require leaders to spend time deliberately reimagining what their organisation could be if the constraints they take for granted were removed.
Chaos is not a pause, it is the environment
Much of Gutsche’s work is grounded in what he calls “chaos induced recharting”. Periods of disruption are not brief storms to be ridden out, they are the conditions under which the industrial map is redrawn. Economic recessions, pandemics and technological shocks all share the same structure. Established rules weaken, old objections matter less, and new ideas become thinkable.
“Chaos and change cause organisations to retreat, but not always,” Gutsche says. “Chaos reshuffles the deck, changes the rules, switches who is in the lead and creates opportunity always. The interesting part is that when crisis hits, the objections that used to shut down ideas matter less, and that opens a window for reinvention.”
He points to the long list of companies founded in recessions, from Disney and FedEx through Microsoft and Apple. Those stories are not romantic exceptions, they are evidence of how turbulence redistributes advantage. In AI, the pattern is already visible. The largest technology firms are simultaneously posting record valuations and cutting tens of thousands of roles. That combination of confidence and ruthlessness signals that the underlying economics of knowledge work are changing much faster than most boards anticipate.
For senior executives, the implication is stark. The AI disruption now under way is not a phase that will pass so that the organisation can return to business as usual. It is the new operating environment. Leaders who treat it as a temporary disturbance will underinvest in the capabilities, structures and cultures needed to navigate it. Leaders who lean into the chaos, experiment systematically and use it to challenge their own assumptions will come out of the period with completely different options.
How path dependency blinds smart organisations
If the opportunity is so large, why do sophisticated organisations keep missing it? Gutsche’s answer is uncomfortable because it places the problem inside the heads of successful leaders rather than at the edges of the technology stack.
He uses the long journey from Roman chariot ruts to NASA’s solid rocket boosters to illustrate how arbitrary constraints become sacred. The width of two horses created a standard for road ruts. The rut width influenced wagon wheels. Wagon tracks informed early rail gauges. Rail gauges then framed the dimensions of trains and, eventually, the size of the tunnels and bridges through which rocket components had to travel. Once a standard exists, it becomes easier to conform than to re-examine.
In the modern enterprise, those “horse dimensions” show up as budgeting cycles, governance processes, architectural standards and product templates that may once have been rational but now simply persist. Gutsche ties this to the neuroscience of myelin, the insulating sheath that forms around frequently used neural pathways. “Whatever you do the most of becomes so paved that you can do it up to a hundred times faster,” he explains. “That is wonderful for performance, but it also means you do not want to do things another way. It makes you better, faster and more consistent, but it also makes you stubborn, repetitive, complacent, resistant and dismissive.”
That description will be familiar to anyone who has tried to move a successful but conservative business into AI at scale. Teams are loyal to the existing model. They are proud of the craft they have refined over decades. They are understandably wary of tools that appear to threaten that identity. Without deliberate intervention, that pride morphs into protectionism, and the organisation’s own expertise becomes a barrier to change.
Gutsche dramatizes this with his paperclip exercise. Adults in a room of thousands quickly converge on a short, predictable list of uses for a paperclip. Kindergarten children generate hundreds. The difference is not intelligence, but the depth of their mental grooves. Adults default to their existing myelin tracks and require structured exercises to escape them. Children have not yet laid those tracks down and can explore more freely. Leaders need to recognise this dynamic in themselves when they dismiss AI use cases as unrealistic or “not for our industry”.
Four kinds of breakthrough every AI leader will face
Gutsche’s most practical contribution to the AI conversation is his taxonomy of four breakthrough types, each corresponding to a different way in which leaders fail to act on an opportunity.
The first is the defining choice, the opportunity that sits only slightly outside the comfort zone and could be pursued with existing capabilities. Trend Hunter’s early platform resembled Pinterest years before Pinterest existed, but Gutsche focused on traffic growth rather than user portfolios. The product decision felt rational at the time and was fully aligned with the company’s strengths. With hindsight, it is a classic defining choice that constrained what the business could become.
The second is the dismissible trend. Blockbuster inventing streaming and Kodak inventing the digital camera are now clichés, but they are useful reminders of how incumbent confidence can be fatal. “You can see the mega trend, you know it is your industry, but because you have so much control you assume you will always stay ahead,” Gutsche says. “That is exactly how the market leader races out on some idea that they in fact invented, and then gets left behind because the other path felt awkward and subtle at the beginning.”
The third type is the missed opportunity that only becomes visible when a customer or junior employee explains it. Gutsche describes discovering an old email from a Nestlé manager who had effectively handed him Trend Hunter’s future business model: custom research using the platform’s data and tools. “What she said was, here is what I do at my job, it takes me six months, and with your machine I could do it better and faster,” Gutsche recalls. “It was instantly obvious once she walked me through it, but for two and a half years that opportunity sat in my inbox because I never met her, never had the coffee, never listened.”
The final category is the hidden gem, the breakthrough that only emerges under pressure when a team is forced to re-examine everything. During the pandemic, Trend Hunter saw its live events revenue collapse and had to decide whether to shrink or pivot. Gutsche chose to redeploy the entire company into AI experimentation. “We forced everyone to do half a day a week of AI,” he says. “At first people pushed back and said they had quotas and invoices, but there were no invoices anyway. So we tried 200 tools, logged 13,000 hours, shared the results, and that changed our trajectory. Revenue per person went up, attrition went away, and AI became the engine for our next chapter.”
For executives looking at AI disruption inside their own organisations, those four patterns are a useful diagnostic. Which AI opportunities feel slightly uncomfortable but obviously feasible? Which macro trends are being acknowledged but quietly dismissed because the organisation believes its brand or regulatory position will protect it? Where are customers or frontline staff already explaining new AI powered services that leadership has not yet taken seriously? And where might genuine hidden gems only surface if teams are given permission and time to explore?
From corporate strategy to personal agency
Part of the reason Gutsche’s keynote resonates with an audience of engineers and executives is that he ties corporate decision making to deeply personal stories. His narrative about his father, a poor immigrant in Calgary who turned leftover groceries into a micro-business, bought a nightclub with a fake ID, and eventually ended up owning a professional football team, is not simply colourful detail. It is a demonstration of how opportunity recognition works at human scale.
For his father, the pattern was simple but relentless. See the overlooked value, make the uncomfortable bet, and then do the hard work that others avoid. The grocery store waste became discounted food for neighbours. A bankrupt restaurant became a break-even billboard before it served a meal. A failing sports franchise became a community cause that he saved seat by seat, conversation by conversation. “He realised it is not simply about hard work,” Gutsche says. “You also need to find that overlooked opportunity, you need to reimagine to find it, and you need to be willing to knock on a thousand doors to make it real.”
In the generative AI era, that pattern translates directly to executive behaviour. The overlooked opportunities are the awkward, early-stage AI use cases that do not yet fit neat financial models. The uncomfortable bets are the cross-functional teams and protected exploration time that feel hard to justify in a quarterly forecast. The hard work is not debugging code, but having hundreds of conversations with customers, employees and partners about what genuinely matters and how AI could serve it.
Gutsche is acutely aware that AI disruption also carries real risks for careers, especially at entry level. He notes that the top ten technology firms have collectively laid off around 150,000 people in a period of record performance. That is not a temporary correction. It is a structural signal that routine knowledge work is being automated. “In 1929 or 2008 if you lost your job, you waited for the market to come back,” he says. “Now we are in a different world, and the sense of purpose, thinking about what you do, becomes even more important. You must deeply think about what your next level is, for yourself, your kids, your team.”
The implication is clear. AI strategy is not only a question of where to invest and which platforms to choose. It is a question of how to give people agency in a landscape where traditional career ladders are being dismantled. Leaders who ignore that dimension will face a workforce that feels threatened, disengaged and resistant. Leaders who engage with it honestly can turn AI disruption into a catalyst for reskilling, internal mobility and new forms of entrepreneurship inside and outside the organisation.
Choosing to break from the path
Gutsche closes his keynote with a challenge that sits somewhere between call to action and personal provocation. “Most people do not break from the path,” he says. “Saying yes to anything new is uncomfortable. It is easier to say no when we are busy, it is easier to say no when we are successful, it is easier to say no and it is easiest to say nothing at all. But you are capable of more than you think. If you want that breakthrough, you are going to have to think about how to break rules, push harder, act sooner and never give up.”
For executives funding AI programmes, that list reads less like motivational advice and more like a governance checklist. Breaking rules means challenging legacy standards and processes that no longer make sense. Pushing harder means insisting that AI moves past pilots into core workflows. Acting sooner means making informed bets before competitors force the issue. Refusing to give up means accepting that some AI experiments will fail, and building a culture that learns from those failures rather than retreating.
Gutsche distils his “hunter” instinct into three traits that apply as much to boardrooms as to entrepreneurs. “One, be curious. Two, be insatiable. Three, be willing to destroy how we have done things in the past,” he says. “Innovation is not always the big thing. So often it is the little thing that you make big.”
That is perhaps the most useful lens for AI disruption. The breakthrough that changes your organisation is unlikely to be a single flagship project or a dramatic announcement. It will probably start as a small, awkward, second order idea that cuts across existing boundaries and feels uncomfortable at first. Leaders who cultivate the curiosity to notice it, the appetite to pursue it and the courage to dismantle the old structures that stand in its way will be the ones who look back on this period not as a threat, but as the moment they redefined what their organisations could be.
