I’ve Scaled Tech Companies Past $100 Million for 25 Years. Here Are 3 Things Leaders Miss Before Implementing AI

For the past 25 years, I’ve been part of scaling technology companies past the $100M mark. I’ve seen many waves of innovation over the past few decades, in cloud, mobile, SaaS and so on. Each one came with the same promise to move faster, operate smarter and gain an edge. AI is no different, but the expectations are just much higher, where many business leaders expect to see better and faster results right away.

From what I’ve seen recently, AI feels familiar in a different way. Companies aren’t struggling because they picked the wrong AI tools, they’re struggling because they’re trying to layer AI on top of systems that weren’t built to support it.

AI isn’t just another tool you plug in; it should be seen more as a stress test on how your business actually runs. And in most cases, that test is revealing gaps leaders didn’t know they had.

If you’re thinking about implementing AI or scaling what you’ve already started, there are three things I recommend that should be in place first that will save you a lot of time and trouble in the long run.

1. If your network isn’t stable, AI will amplify the problem

One of the biggest misconceptions I see is that AI will smooth out inefficiencies. It won’t. That’s because AI depends on real-time data and consistent networks that perform the same exact way every time. And if your applications lag, your connectivity fluctuates or your teams are already working around performance issues. AI does not fix that; it speeds it up.

Instead of getting better decisions, you get faster, bad ones. I’ve seen businesses where everything looks fine on paper. Systems are technically “up,” dashboards are green and nothing is fully broken. But employees are dealing with slow apps, dropped calls or workflows that don’t quite complete the way they should. That’s not stability. That’s what I’d call barely holding it together, and AI will expose that immediately.

Before you invest further in automation or intelligence, you need to ask one question: Do our systems perform consistently under normal conditions? Not just when we’re troubleshooting, but every day, because once AI is in the mix, inconsistency only multiplies.

2. If your team is reactive, AI won’t deliver what you expect

The second issue is how teams operate day-to-day. Most organizations are still reactive, so something breaks, performance drops, users complain and then the team steps in to fix it. That’s been the default model for years.

AI assumes a completely different environment. Rather, it works best when systems are monitored continuously, issues are identified before they become visible and adjustments happen in real time without someone needing to drop what they’re doing to step in. But that’s not how most businesses run today.

What I hear from a lot of IT leaders is that their teams are stuck in a constant cycle of troubleshooting. So they don’t have the visibility to see problems early, and they don’t have the time to rethink how systems should operate because they’re too busy keeping things running. Then AI gets introduced, and the expectation is that it will somehow create efficiency on top of that.

But if your team is already stretched reacting to issues, AI just adds another layer of complexity to manage. It doesn’t remove the burden; it shifts it.

The companies that see real results from AI are the ones that have already moved toward proactive operations. They’ve built environments where performance is predictable, not something that needs to be chased down. That’s the difference.

3. Complexity is undermining most AI strategies

The third issue does not get talked about enough: complexity.

Over time, most businesses accumulate technology. New tools get added, systems don’t always integrate cleanly, and before long, you have a stack that technically works — but only because people know how to navigate it.

Then AI enters the conversation, and the instinct is to add more. More platforms, more capabilities, more layers, more, more, more. The key here is that complexity does not create leverage, but it creates a whole lot more friction.

Every additional system is another point where something can fail, another place where data doesn’t sync correctly, another dependency that needs to be managed. AI relies on coordination across all of it, which means the more fragmented your environment is, the harder it is to get consistent outcomes.

I’ve seen companies invest heavily in advanced tools, only to realize their teams are spending more time managing the tools than benefiting from them. At that point, the promise of efficiency disappears.

If your systems don’t work together today, AI won’t fix that. It will make the gaps more visible.

Simplifying how your technology operates, how data flows, how decisions are made and how systems interact is one of the most important steps before adding anything new.

The stakes are higher than most leaders realize

There’s also a financial side to this that often gets overlooked. The average cost of downtime is estimated at $5,600 per minute. That’s not just about full outages, it’s about the small disruptions, the slowdowns, the moments where systems don’t perform the way they should.

AI increases your dependence on everything working as expected. When it doesn’t, the impact isn’t isolated; it ripples across workflows, decisions, and customer experiences. That’s where things start to add up quickly.

AI isn’t the first step

AI has the potential to reshape how businesses operate. I don’t think there’s much debate about that. But it’s not the starting point.

If anything, it’s forcing leaders to take a closer look at how their businesses actually function beneath the surface. Is the network reliable? Are operations proactive? Is the environment simple enough to scale?

Those aren’t new questions. They’ve always mattered. AI is just making them harder to ignore. And the companies that answer them first are the ones that will actually see the results everyone else is expecting.