I remember the first time I watched a business owner dismiss the internet as a fad.
It was 1998. I was sitting in a conference room with a guy who ran a chain of print shops across the Midwest. Someone had just suggested he build a website. His response? "Why would I pay for something nobody's going to use? People want to walk in, talk to someone, and hold the product in their hands."
Three years later, online printing services started eating his lunch. Five years after that, half his locations were closed. A decade later, the business didn't exist.
He wasn't stupid. He wasn't lazy. He just waited for clarity that never came.
I've watched this movie play out three times in my career. First with the internet. Then with mobile. Then with social media. Every single time, the pattern is identical. A new technology emerges. Early adopters look like they're taking unnecessary risks. The skeptics feel smart for waiting. And then the window closes so fast that the skeptics never catch up.
We're in that window right now with AI. And if you're reading this thinking "I'll figure it out when things settle down," I need you to understand something clearly.
Things don't settle down. They speed up. And the gap between leaders and laggards gets wider every single day.
The Myth of Waiting Until It's Clear
Let's talk about clarity for a minute. Because every business owner I meet who's hesitating on AI says some version of the same thing: "I'm waiting until I understand it better" or "I want to see how it plays out first."
Here's what I've learned after 25 years of watching technology transform industries. Clarity is a myth. It's a story we tell ourselves to justify inaction. By the time something is "clear," the opportunity has already been captured by someone else.
Think about the businesses that understood the internet early. They weren't smarter than everyone else. They didn't have better information. They just started building while everyone else was debating.
Amazon launched when most people still thought buying things online was crazy. Facebook opened to the public when MySpace looked unbeatable. Netflix started streaming when Blockbuster had 9,000 stores. None of these companies waited for clarity. They created it.
The same thing is happening right now with AI. The companies that are experimenting today, even imperfectly, are building muscle memory that their competitors will never develop. They're learning what works. They're training their teams. They're discovering use cases that don't exist in any playbook.
Meanwhile, the companies waiting for clarity are falling further behind with every passing month.
I had a call last week with a marketing director at a manufacturing company. She told me her CEO had banned anyone from using AI tools until they could "establish a comprehensive policy." That was eight months ago. The policy still doesn't exist. And their competitors are using AI to write proposals in half the time, analyze customer data faster, and respond to RFPs while her team is still pulling information manually.
Eight months of waiting. Zero benefit from caution. And a gap that gets harder to close every day.
The Cost of Hesitation Compounds Fast
Here's what people don't understand about technology shifts. The cost of hesitation isn't linear. It's exponential.
When you wait, you don't just lose the time you waited. You lose all the learning that would have happened during that time. You lose the competitive advantage that early experimentation provides. And you lose the confidence that comes from understanding something before it becomes mandatory.
Let me give you a specific example. I know two restoration company owners who took opposite approaches to AI last year. One started using AI to help write estimates, respond to customer inquiries, and train new technicians. The other decided to wait and see how things developed.
Twelve months later, the first owner has a team that's comfortable with AI tools. They've figured out what works and what doesn't. They've identified three specific use cases that save them real time and money. And more importantly, they've built a culture where experimenting with new technology is normal.
The second owner? His team is now scared of AI because they've heard so much about how it might replace jobs. They're resistant to trying new tools. And he's looking at the same learning curve his competitor faced a year ago, except now his competitor is already on to the next innovation.
Same starting point. Same industry. Completely different trajectories. Not because one was smarter than the other. Because one moved while the other watched.
This is the part that keeps me up at night when I talk to business owners. The hesitation that feels safe is actually the riskiest choice you can make. Every day you wait is another day your competitors get to learn without you.
AI Is Still Early, Which Is the Opportunity
I want to be clear about something. I'm not saying AI is a finished product. I'm not saying every tool works perfectly. I'm not saying there aren't legitimate concerns about implementation, accuracy, and best practices.
What I'm saying is that "early" is exactly where you want to be.
There's no rulebook for AI right now. No finished playbooks. No established best practices that everyone has to follow. That means the companies willing to experiment get to write the rules for their industry.
Think about what that actually means. In most areas of business, you're competing against established methods, proven strategies, and years of accumulated wisdom. But with AI, everyone is figuring it out at the same time. The playing field is as level as it's ever going to be.
This window won't last forever. In three years, there will be clear winners and clear losers. There will be companies that figured out how to use AI effectively and companies that are still scrambling to catch up. The early advantage will have compounded into something much bigger.
Right now, a small business owner willing to experiment has the same access to AI tools as a Fortune 500 company. That's unprecedented. It won't stay that way. But for this brief moment, the advantage goes to whoever moves first, not whoever has the most resources.
I talked to a financial advisor last month who started using AI to help draft client communications. Nothing fancy. Just using it to create first drafts of emails, reports, and educational content. He told me it saves him about six hours a week. That's six hours he now spends meeting with clients instead of staring at a blank page.
His larger competitors with teams of people? They're still debating whether AI fits their compliance requirements. Meanwhile, he's building deeper relationships with clients because he has more time to actually talk to them.
That's the opportunity window. It won't be open forever.
Your Role as a Leader
Here's the part nobody talks about. AI adoption isn't really a technology decision. It's a leadership decision.
Your team is watching you. They're paying attention to whether you're curious or dismissive. They're noticing whether you experiment with new tools or avoid them. They're taking cues from your behavior, not your announcements.
I've seen this play out dozens of times. A CEO announces that the company is "embracing AI and innovation." Big announcement. Lots of excitement. Then nothing changes because the CEO never actually uses any AI tools themselves. The team gets the message loud and clear. This is theater, not reality.
Compare that to another client of mine who started each Monday meeting by sharing something new she'd tried with AI that week. Sometimes it worked great. Sometimes it was a complete failure. But her team saw her experimenting, learning, and being willing to look foolish in the process.
Within three months, her entire organization was trying new things. People were sharing what they discovered. The culture shifted from "we don't do that here" to "let's see if this works."
That's the difference leadership makes. You don't have to be an AI expert. You don't have to understand the technical details. You just have to be willing to experiment publicly and create space for your team to do the same.
The organizations that will thrive with AI aren't the ones with the best technology. They're the ones with leaders who model curiosity, reward experimentation, and treat failure as learning instead of liability.
If you're waiting for your team to figure out AI while you stay safely on the sidelines, you're sending a message. And that message is being received.
What This Actually Looks Like in Practice
Let me get specific about what early AI adoption looks like. Because I think a lot of people imagine they need to hire data scientists and build custom systems. That's not it at all.
Start small. Find one task that takes you too long and try using AI to speed it up. Maybe it's writing first drafts of proposals. Maybe it's summarizing long documents. Maybe it's brainstorming marketing ideas or analyzing customer feedback.
The goal isn't perfection. The goal is learning. You want to understand what AI can and can't do. You want to develop intuition for how to use it effectively. You want to discover the specific applications that matter for your business.
I spent about 30 minutes this week using AI to help analyze a client's Google Business Profile data. What would have taken me hours of manual review took a fraction of the time. Was the analysis perfect? No. Did I have to verify things and make corrections? Yes. But I learned something about what's possible, and next time I'll be faster and better.
That's the practice. Small experiments. Consistent learning. Building capability over time.
The businesses that succeed with AI won't be the ones who made the biggest bets. They'll be the ones who made the most bets. Because every experiment teaches you something, and that accumulated knowledge becomes an advantage nobody can take away.
The Real Risk Isn't Moving Too Fast
I know some of you are reading this and thinking about all the things that could go wrong. AI makes mistakes. It can produce inaccurate information. There are legitimate concerns about privacy, security, and reliability.
Those concerns are valid. I'm not dismissing them.
But here's what I want you to consider. The risk of experimenting carefully and learning from mistakes is manageable. The risk of waiting until AI is "safe" and "proven" is that you'll be competing against companies who spent years building capabilities while you built nothing.
Every technology has risks. The internet had security risks. Mobile had privacy concerns. Social media had reputation dangers. Businesses that figured out how to manage those risks while still moving forward are the ones that survived.
The companies that waited for everything to be perfectly safe? Most of them don't exist anymore.
I'm not suggesting you rush blindly into AI without any caution. I'm suggesting that waiting for perfect clarity is a form of blindness too. The goal is thoughtful experimentation, not reckless adoption or paralyzed inaction.
Your Decision Point
We're at a moment that comes once or twice in a generation. A genuine platform shift that will reshape how business gets done.
You get to decide how you respond.
You can observe this moment from a distance. You can read articles about AI, attend webinars about its potential, and talk about how interesting it all is. You can wait for someone else to figure it out and then copy what works.
Or you can participate. You can start experimenting today, even in small ways. You can build capability while your competitors debate. You can position yourself as someone who leads through change instead of reacting to it.
This isn't about being the first company to use AI. It's about refusing to be the last.
I've watched three technology shifts reshape entire industries. The pattern is always the same. Early movers get to learn on their own terms. Late movers get forced to change on everyone else's terms.
AI will not wait for you to feel ready. The market will not pause while you develop a comprehensive strategy. Your competitors will not slow down so you can catch up.
The time to start is now. Not because everything is figured out. Precisely because nothing is figured out. That's where the opportunity lives.
Twenty years from now, people will look back at this moment the same way we look back at the early internet. They'll ask who moved and who waited. They'll marvel at how obvious it all seems in retrospect.
What will your answer be?
SML
Ready to stop observing and start participating? Let's talk about what AI adoption actually looks like for your business. Schedule a conversation and let's figure out where to start.
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