The Mid-Market AI Trap (And Lessons from the Early Internet)
AI for Mid-Market Leaders Series – Part 2 of 4
In Part 1, we explored why mid-market companies can’t simply borrow the enterprise approach to AI. Large organizations have the structure, capacity, and governance to analyze AI from every angle before they act. Mid-market companies don’t have that luxury — which is why AI has to start at the leadership table, led by the CEO.
There’s a second point worth exploring, and it may be even more important: For mid-market companies, the real risk isn’t in moving too soon on AI. It’s in waiting too long to start learning.
It may sound counter-intuitive, but we have seen this pattern before.
We’ve Already Lived Through a Version of This
In the late 1990s and early 2000s, the internet arrived with a flood of hype and absurd valuations. A lot of “dot-com disruption” was speculative and overconfident. Many CEOs looked at the frenzy and made a rational decision: “We’ll wait until this makes sense.”
And then something interesting happened. Yes, the hype collapsed. Yes, many early players failed. But the internet itself didn’t go away. It matured quietly and became embedded in how every company operates:
how we find customers
how we recruit talent
how we manage information
how we buy, sell, schedule, and support
how we collaborate
That shift didn’t happen in a dramatic wave. It happened through steady, practical adoption, long after the headlines moved on.
The companies that waited for clarity eventually had to catch up to their customers, competitors, and even their own employees, often at a higher cost and with more disruption.
For those of us around consulting at that time, we remember companies spending whatever it took to catch up to their competitors, executives facing pressure from their shareholders, and rush technology implementations that many times went off the rails and cost losses in the millions.
Many mid-market companies never caught up and were bought up by competitors or private equity firms at bargain basement prices.
AI Is Entering the Same Cycle
The excitement around AI today feels familiar: new innovations every week, bold predictions, and a lot of noise. Therefore it’s tempting to say: “Let’s see where this goes before we commit.”
But AI isn’t just another technology trend. Like the internet, it will likely become part of how work is done. Your work is done.
Eventually early adopters will fade, vendor lists will consolidate, headlines will tone down, and AI will simply move into the background and actually become the foundation of how your organization operates.
This is why waiting for “the end state” is almost always too late.
The Mid-Market Trap
Mid-market companies sometimes assume that the safest position is to “let the big players figure it out first.” However, that’s a dangerous mindset trap. Mid-market companies stay ahead and win by learning early, due to their advantages:
faster decisions
simpler governance
closer leadership engagement
ability to pivot quickly
greater visibility of early wins
But those advantages only matter if you start while the cost of experimentation is low, the stakes are manageable, and your team is curious rather than cynical. Once AI becomes “standard,” you’ll be implementing it on someone else’s timeline.
A Hint of What You Should Do Next
As we covered in Part 1 of this 4 part series, the first step is to bring your leadership team together and align on what AI is for in your business, in real, concrete terms, and not in abstract statements.
The next step builds on that work.
CEO Hint #2
Surface the risks and roadblocks your team sees, and convert them into actionable decisions in your AI Blueprint.
Ask your leaders what they’re concerned about, what could slow you down, and where you may lack capability.
In the next newsletter, I’ll share a simple template for identifying roadblocks and adding them to your AI Blueprint, along with the most common concerns that surface in leadership teams.
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