If you’ve been following the AI conversation from the outside, watching the announcements, reading the coverage, hearing about what other companies are doing, it can feel like the window has already closed. Like the businesses that were going to figure this out have figured it out, and what’s left for everyone else is trying to catch up to a distance that keeps widening.

That feeling is understandable. It’s also wrong.

The businesses generating real results from AI right now represent a small fraction of the companies that will eventually build meaningful AI capability. The tools are still new. The best practices are still being figured out. The organizational muscle for working with AI effectively is still being developed, inside even the most forward-thinking companies. The idea that this is a race that’s already been run doesn’t match what’s actually happening.

Why It Feels Later Than It Is

Part of what makes AI feel so far along is the echo chamber effect that Zack Terry, Fast Slow Motion’s Director of AI, describes from inside the industry. When you’re close to this technology every day, reading about it, working with it, talking to people who are building with it, the pace feels relentless. Every week brings new capabilities. Every month brings new applications. From inside that world, it’s easy to feel like the rest of the business community is sprinting to keep up.

From outside that world, the same information creates a different feeling: that everyone else is already ahead, and that starting now means starting behind.

Neither feeling is quite accurate. The reality is that most businesses, including many that have announced AI initiatives or deployed AI tools, are still in the early stages of figuring out how to get real value from this technology. Deploying a tool and building genuine organizational capability are different things, and most companies are closer to the former than the latter.

Henry Hays uses an analogy that puts the current moment in useful perspective. When Henry Ford rolled the first Model T off the line in 1908, it topped out at 36 miles an hour. The most progressive thinkers of that era, the ones who saw the automobile coming and believed in its potential, were still operating with a mental model built around 36 miles an hour. They couldn’t yet conceive of what the technology would become, because the only version they’d seen was the early version.

The businesses using AI today to handle email drafting or basic content generation are doing the equivalent of watching a Model T and thinking they understand where this is going. The ceiling is much higher than most people realize, and the ones who understand that earliest will have the most room to build.

What Early Innings Actually Means

When Zack Terry says we’re still in the early innings, that’s not a motivational framing. It’s a description of where the technology actually is relative to where it’s going.

The AI tools available today are genuinely useful. They’re producing real value in real businesses right now. But they’re also the early version of something that will be significantly more capable, more integrated, and more embedded in how businesses operate within the next several years. The businesses that are building organizational capability now, learning how to use AI effectively, developing the internal knowledge and the institutional processes that make AI work well, are building on a foundation that will keep appreciating in value.

Starting now doesn’t mean starting at the beginning of a race that’s mostly over. It means starting at a point where there’s still enormous untapped potential, where the best practices are still being established, and where the learning that happens today will compound directly into capability that matters significantly over the next three to five years.

What Getting Started Actually Looks Like

The businesses that feel most behind on AI are usually the ones that have been waiting for a comprehensive strategy, a complete plan, or the right moment to launch a full initiative. That wait is what creates the feeling of being behind, not the absence of AI in the business.

Getting started doesn’t require any of those things. Hays is direct about where it actually begins: with the leader. The business owner or CEO who decides to become a practitioner, to use AI consistently in their own daily work until they have a genuine feel for what it can do, is the starting point for everything else.

That sounds simpler than it is. It requires setting aside time, being willing to be bad at something for a while, and resisting the pull to delegate this to someone else to figure out first. But the leader who has personally worked with AI, who has run their own prompts and evaluated their own outputs and pushed back when something didn’t work, is a fundamentally different kind of AI leader than the one who has only read about it or attended a demo.

From that starting point, the path forward is straightforward: identify one specific, boring, repetitive task where AI can deliver a clear return, implement something that handles that task, measure the result, and build from there. Not 19 use cases at once. One. And then the next one, informed by what the first one taught.

The Cost of Waiting Another Year

The honest answer to whether it’s too late is no. But the question worth sitting with is what another year of waiting actually costs.

The businesses that move this year will have a year of organizational learning that the ones who wait won’t have. They’ll have a team that has developed real AI capability, internal processes that have been refined through actual use, and a set of proven use cases that they can build on. That’s not an insurmountable advantage, but it’s a real one, and it compounds.

The businesses that wait another year will be starting from where the early movers started this year, in a market that has moved further, with expectations that have risen, and against competitors who have been building for longer. The starting line doesn’t move, but the field does.

None of this is a reason to panic or to move before the foundational conditions are right. It’s a reason to take the question seriously and make a deliberate decision about when to start, rather than letting the answer be determined by inertia.

The Practical First Step

For a business owner who has been following this from the outside and is ready to stop following and start moving, the first step is personal. Pick one AI tool, create an account if you don’t have one, and spend time with it on real work until you understand what it can do.

Not a demo. Not a report about it. The actual tool, applied to actual work you’re already doing. Preparing for a meeting. Thinking through a decision. Drafting something that you’d otherwise write from scratch. The goal isn’t to find a breakthrough use case on day one. It’s to develop enough personal experience with the technology that you can lead your organization into it with credibility rather than just conviction.

From there, the path is well-established: build a usage policy, find the right internal owner, identify the highest-value starting use cases, and move deliberately. The businesses that have done this well haven’t done it by being the fastest. They’ve done it by being intentional, and intentional is available to any business that decides to start.

Listen to the full podcast episode here.

Related Resources

• Stop Treating AI Like Google: What Business Owners Get Wrong from Day One: Once you’ve decided to start, how you use AI matters as much as that you use it. This post covers the mindset shift that changes how much value you get from every AI interaction from the beginning.
• The Boring, Mundane, and Tedious: Where AI Actually Delivers for Growing Businesses: Finding the right first use case is what turns a decision to start into an initiative that builds momentum. This post identifies where growing businesses see the fastest, clearest returns.
• Why AI Adoption Stalls at the Leadership Level (And What to Do About It): The first step is personal, but leadership modeling is what drives adoption across the rest of the organization. This post covers what that looks like and why it matters.
• The AI Execution Problem: Why Most Growing Businesses Can’t Get Past Experimenting: Starting is the first step. Building momentum from there is a different challenge. This post covers the full picture of what it takes to move from experimentation to real results.