“A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.” This is one of the most overused quotes in business, attributed to Wayne Gretzky, who is considered one of the greatest hockey players in NHL history. Leaders invoke it whenever they talk about innovation, disruption, or staying ahead of the competition. The lesson is usually framed as prediction: the best leaders can see the future before everyone else
I can imagine that he wasn’t watching the puck at all.
He was watching the players, their momentum, their positioning, the space opening on the ice before anyone else noticed it. The puck simply followed the pattern.
In 2005, I spent most of my free time in my basement teaching myself the technology behind Web 2.0: HTML, RSS feeds, wikis, tags. Looking back, most of that was wasted effort. Within a few years, any grandmother could use social media without any tech knowledge, without ever touching a line of code or knowing what a feed even was. The technology evolved to be user-friendly on a mass scale because that’s what drives adoption.
I see the same pattern playing out with AI right now. The prompting tricks and technical workarounds everyone is scrambling to master today are this moment’s version of HTML. The platforms are already evolving past the need for most of it. That’s why I don’t spend my time chasing the day-to-day changes and challenges of AI. I watch the pattern instead.
For example, not long ago, I started sketching out a custom agent to scour the web for event and media opportunities. Then ChatGPT rolled out scheduled tasks, and what I was putting minimal effort into became a native feature. The same shift is showing up everywhere. Claude used to only hand back text in a chat window; now it can create and edit actual files, including spreadsheets, documents, presentations, and PDFs. Gemini used to mean copying content back and forth between your inbox and a chat window; now it can pull context straight from Gmail and Drive while it drafts inside Docs. Perplexity used to live in its own tab, separate from wherever you were actually browsing; now Comet is built to work as an assistant inside the browser itself. Platforms are continuously updating so that anyone’s grandma can use them without any tech knowledge. As they say, history rhymes.
The way organizations are approaching AI also sounds eerily familiar.
Which models should we use? Which departments should adopt AI first? How many licenses should we buy? What workflows can we automate? How quickly can we deploy?
These were the exact same questions we asked twenty years ago with social technologies.
They’re important questions. They just aren’t strategic ones.
While many organizations are focused on integrating today’s AI capabilities into yesterday’s operating model, the companies building AI are already solving a different set of problems. They’re investing in power infrastructure, data centers, specialized chips, model orchestration, agent communication, and energy efficiency. They aren’t just thinking about what AI can do today. They’re preparing for what it will become over the next five years.
One group is watching where the puck is.
The other is affecting where it’s going.
Patterns emerge when you can see the whole system
Recently, I interviewed technology executive Salil Kulkarni for an upcoming Highlight Georgia article about how his company helps organizations manage incredibly complex technology environments. What impressed me wasn’t the technology itself, but the philosophy behind it.
He described their work as creating visibility across an entire ecosystem in real time. Instead of looking at isolated parts, they build a picture of how thousands of interconnected components influence one another so organizations can make better decisions before small problems become major failures. He called it “trusted runtime truth.” Not a snapshot. Not a postmortem. A living picture of what’s actually happening, updated as it happens.
He expanded this thinking as part of his leadership philosophy.
Leadership has become an exercise in systems thinking.
The organizations that thrive won’t be reactionary, and they won’t treat change as one project at a time. They’ll be the ones with a culture where people feel safe to speak up, question what they see, and act on it. They’ll notice when something in one part of the business starts affecting another, because people are paying attention beyond their own piece of it. And once they notice, they’ll act on it: raise it, trace where it’s likely headed, get ahead of it, and turn that early notice into an edge, whether that means winning more market share or finding a new way to create value for everyone with a stake in the outcome.
The future doesn’t arrive one trend at a time
The sharpest pattern recognition rarely comes from people who’ve spent their whole career staring at one subject. It comes from people who let more than one passion into their work: aging demographics, AI governance, neuroscience, energy infrastructure, labor economics, marketing strategy, education, organizational psychology, hockey, history, philosophy, gardening.
At first glance, those topics seem unrelated.
They’re not.
Being multi-passionate isn’t a distraction from focus. It’s what lets you study the connections between subjects instead of just the subjects themselves.
The future rarely arrives as a single breakthrough. It emerges when multiple forces begin interacting in ways that weren’t obvious before. Futurist Amy Webb makes the same point: trends don’t predict the future. Convergences do.
AI is colliding with demographic shifts. Those shifts are colliding with labor shortages. Labor shortages are reshaping education. Education is changing expectations around work. Geopolitics is influencing infrastructure. Infrastructure is influencing AI development.
AI is also accelerating other frontiers most leaders aren’t tracking closely yet: quantum computing, biotechnology, materials science, robotics. Each of those advances feeds back into AI itself, which means the next leap tends to arrive faster than the last one did. That compounding effect is the real story. Change isn’t just continuing. It’s accelerating. Staying focused on the present isn’t a safe, steady choice anymore. It’s the riskiest one.
No single data point explains the future.
Patterns do.
Silos hide patterns
Later in our conversation, Salil made another keen observation. He said there’s no such thing as “the technologist” anymore, or “the marketing person,” or “the salesperson.” Everyone touches all of it now, whether or not it’s in their job description.
The best organizations don’t ask technology to solve technology problems. They bring together people from finance, operations, marketing, IT, customer-facing teams, and leadership because each group sees a different part of the system. Innovation happens when those perspectives intersect.
That may be one of the biggest leadership challenges facing organizations today.
Most companies are still organized around functions.
Marketing sees customers.
Finance sees costs.
HR sees talent.
Operations sees efficiency.
IT sees technology.
Each department becomes better at understanding its own world while losing visibility into the larger system.
Yet competitive advantage increasingly lives between those functions.
The leaders who recognize patterns aren’t necessarily smarter.
They simply have a wider field of vision.
Readiness isn’t a rollout
This is the same pattern-recognition problem showing up at the organizational level, just easier to miss because it looks like a single event instead of a repeating shape.
Recently, I referenced Canva’s decision to give 5,000 employees an entire week to experiment with AI. The tools were ready. The humans weren’t, as Canva’s Chief Customer Officer put it.
Most organizations respond to a stall like that with another mandate, another calendar clearing, or another all-hands training. Each fix treats the moment as a single problem to solve once and move past.
Research from Harvard Business Review suggests that’s the wrong frame entirely. Even though 88 percent of companies now report regular AI use, adoption still stalls in most of them, and the cause isn’t the tools. It’s anxiety about relevance, identity, and job security running underneath the surface. That anxiety doesn’t resolve itself once and disappear. It resurfaces every time the technology takes another leap, the next model, the next capability jump, the next round of headlines about what AI can now do that it couldn’t do last quarter.
Solve this stall and declare victory, and the same stall will be waiting on the other side of the next update. That’s the mistake underneath the mistake: treating a recurring pattern like a one-time event.
The better question isn’t “how do we fix this rollout.” It’s “how do we build an organization where adapting, questioning, and creating are just part of how people work, not a special initiative we roll out when the pressure gets high enough.” Culture like that doesn’t come from a mandate or a cleared calendar. It gets built one decision at a time, long before the next wave of change shows up and demands it.
Readiness isn’t something you achieve once. It’s a pattern you either build into how your organization works, or you keep discovering you don’t have it, one stall at a time.
Curiosity is becoming a competitive advantage
Curiosity gets treated like a luxury in most organizations. It takes too much time. It doesn’t map to a deliverable. It doesn’t show up on a quarterly review. So the incentive structure quietly trains it out of people, one performance cycle at a time, rewarding speed and compliance instead.
That way of thinking came from an education system and an organizational structure both built for an age that no longer exists.
AI is about to hand people their time back. The research, the first draft, the routine analysis, the tasks that used to eat an entire afternoon are shrinking down to minutes. The real test of leadership isn’t whether an organization integrates AI. It’s what happens to the hours it frees up. Reinvest that time into curiosity, exploration, and creativity, and an organization keeps discovering what’s next. Let it get swallowed by more output at a faster pace, and an organization has just built a faster version of what was already falling behind.
That reinvested time is where pattern recognition actually happens. It rarely shows up in the middle of a full calendar.
Stop watching the puck
Wayne Gretzky didn’t become extraordinary because he predicted the future.
He understood the system.
He watched the movement of the game until the next play became almost inevitable.
This is what it now takes to simply stay in the game.
Don’t spend all of your time watching AI.
Watch what AI is changing.
Watch education.
Watch demographics.
Watch infrastructure.
Watch customer behavior.
Watch regulation.
Watch how work is changing.
Watch where trust is growing. Watch where it’s disappearing.
The future rarely announces itself with a few data points.
It usually begins as a pattern that most people haven’t noticed yet.
The leaders who learn to recognize those patterns won’t just respond to the future.
They’ll help create it.
If you’re trying to figure out what pattern your organization is actually in right now, that’s the conversation I have with leaders every week. Let’s Talk.
