The Great AI Divide — Part I
Why Most Companies Are Getting It Wrong (And a Few Are Getting It Spectacularly Right)
I'm a CTO and founder with nearly two decades of experience driving growth and transformation through technology. At Stronghold Investment Management, I led the development of a systematic real asset trading platform and modernized everything from Salesforce strategy to custom cloud-native infrastructure. My background spans commercial real estate, e-commerce, and private markets — always focused on delivering innovation, velocity, and meaningful business outcomes. I hold a PhD in Theoretical & Computational Biophysics and was recognized as a Google Developer Expert in Cloud. I build high-trust, high-output teams. I’ve rebuilt broken cultures, hired top-tier engineers, and helped early-stage and PE-backed companies scale with confidence. System modernization is my specialty — not just upgrading software, but aligning teams and infrastructure with what the business actually needs. Currently, I lead client engagements through Heavy Chain Engineering and am building Newroots.ai, an AI-driven relocation advisory platform.
I’ve been saying it to anyone who will listen: AI is delivering 10x, even 100x, the value we were seeing just a few years ago. I see it every day in my own work and with the teams I train. So when I see that infamous report from a place like MIT claiming that 95% of organizations are getting zero return on their AI investment, I have to call it like I see it.
That’s not an AI problem. That’s a you problem.
Two major reports dropped recently, and they paint a fascinating, almost contradictory, picture of the state of AI in the enterprise. On one hand, you have the Wharton School’s “Accountable Acceleration” report, which shows that AI usage is mainstream, with 82% of leaders using it weekly and three-quarters already seeing positive returns. This is the world I live in. This is the reality of teams who are shipping better work, faster.
On the other hand, you have the MIT report, “The GenAI Divide,” which paints a bleak picture of stalled pilots and wasted billions. They found that while 80% of companies have piloted tools like ChatGPT, only 5% of custom enterprise AI solutions ever make it to production. A 95% failure rate.
And the core of ‘getting it’ is realizing that AI isn’t a magic wand that lets you bypass every lesson we’ve ever learned about business.
Here’s the thing: both reports are right. They’re just telling different sides of the same story. The GenAI Divide is real, but it’s not a divide between AI that works and AI that doesn’t. It’s a divide between companies that get it and companies that don’t. And the core of ‘getting it’ is realizing that AI isn’t a magic wand that lets you bypass every lesson we’ve ever learned about business. It’s a tool, and like any powerful tool, it requires discipline to wield.
The 95% Failure Rate: Parroting Limitations is Easy
The MIT report is a perfect portrait of what happens when organizations treat AI like a magic black box. They hear the hype, throw some money at a pilot, and are shocked when it doesn't instantly solve all their problems. As one CIO in the report put it, "We've seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects."
That's the first group: the ones who tried it, got disappointed, and blamed the technology or walk away. But there's a second group that's even worse. I see them constantly. They haven't even touched AI coding tools, but they've already written it off. They've memorized every criticism they can find—"Hallucinations! Context windows! It's not secure!"—and use those as shields against actually trying. And, when forced to attempt something folks don't want to adopt, one often gets malicious compliance.
As I wrote in a recent post, parroting the limitations is easy. Using the tools well is harder. The 95% of companies failing are stuck in one of these two camps: either they tried it without discipline and got burned, or they never really tried at all. What's worse, these are organizations that have entire playbooks for change management. They know Kotter. They've run massive, disciplined rollouts for new software, new processes, new everything. But with AI, it's like they've developed collective amnesia. They're just smearing this technology all over their org in such a desultory pattern it looks like a Jackson Pollock painting, with similar perceptions of elegance.
The Wharton report, in contrast, is a study of the 5% — or just later in time and maybe folks learned more along the way? It's a look at the companies that have pushed through the initial frustration and are now in a state of "accountable acceleration." They're measuring ROI, they're increasing their budgets, and they're embedding AI into their core operations. They're the ones seeing the 10x-100x returns.
The Proof is in the Trenches
The most telling part of the MIT report is the section on the “shadow AI economy.” While only 40% of companies have official LLM subscriptions, over 90% of employees are using personal AI tools to get their work done.
This is where the real value is being created. It’s the individual engineer who, after a few frustrating hours, figures out the right prompting strategy and saves himself weeks of work. It’s the marketing team that learns to feed the AI a proper brand voice document and finally gets copy that doesn’t sound like it was written by a robot.
This isn’t happening in the formal, top-down, multi-million dollar “AI initiatives” that are failing 95% of the time. It’s happening in the trenches, with people who are motivated to solve real problems.
The divide isn’t about access to technology. It’s about approach. The failing majority is waiting for a perfect, turnkey solution to be handed to them. The successful minority is rolling up their sleeves, learning the strategies, and putting in the work. They understand that AI is not a magic wand; it’s a leverage point. And the payoff is a level of productivity you simply can’t ignore.
In the next post, I’ll break down the single biggest mistake the 95% are making—and how the other 5% are turning it into their biggest advantage. Hint: it has a lot more to do with engineering than you think.
