You're holding it wrong
AI spending is on track to hit $2.6 trillion this year. That’s a 47% jump from last year. And yet 72% of CXOs report their organizations have barely broken even or lost money on their AI investments.
Pretty much three out of four. Yikes, man.
I’ll say this… the technology works. The models are better than they’ve ever been. The software wrappers, the CLIs, the harnesses, the connections to productivity apps—the tooling is mature enough that you can spin up a pilot (not just a POC) in a weekend. So why does AI in the enterprise keep failing to make impact?
I’ve spent the last several years leading engagements inside PE-backed financial services companies. Contact centres, lending operations, underwriting workflows, portfolio management. The kind of environments where the work is repetitive, the data exists, and the business case writes itself. If AI is going to work anywhere, it should work here.
And the technology does work. What fails is everything around it.
The organizations I’ve seen get this right understand the human system before selecting the tools.
In 2010, Apple launched the iconic iPhone 4. A gorgeous sandwich of glass and aluminum. Instant classic. Except, it had a fatal antenna flaw that dropped signal when people held the phone a certain way. Users flooded forums, Facebook, the news picked it up. Remember Antennagate? Steve Jobs’s initial response in an email to a frustrated customer was something like: just avoid holding it in that way.
He was right, technically. Certain grips did interfere with the antenna bands, but that was not the point. The phone was designed for how Apple wanted it, not for how people actually used it.
"Scare quotes"
Ready for a huuuge stretch? Something nearly identical is playing out in enterprise AI right now.
Leadership buys the tools, mandates the rollout, declares the transformation. Then BAM adoption flatlines. The diagnosis from the top mirrors Jobs almost perfectly… the workforce is resistant, unskilled, stuck in their ways. They’re holding it wrong. And for real, maybe they are. Workers are underusing the tools, reverting to old workflows, treating AI outputs with more skepticism than leadership thinks is warranted—or like some GenZ folk, actively sabotaging AI at their companies.
All of this points to how leadership thinks work happens, not for how it actually does. The incentive structures that shape daily decisions, the position workers are put in to be accountable for an unaccountable machine, the workflow friction that makes the old way easier than the new one is treated as someone else’s problem. A failure of the user, a failure of the technology—but whooo boy, definitely not how the org is designed, right?
Thomson Reuters found that 82% of C-suite leaders say AI is embedded in their workflows. Only 12% of employees say they’re actually using it. That’s a wild chasm.
Jobs blamed the user’s grip instead of (initially) blaming the antenna. Enterprise leadership is shaping up to blame the workforce instead of redesigning the system to account for the tool. Even if the workers are “holding it wrong”, how they hold it is always a known variable. It’s just not being designed for.
I’ll quote this stat because everyone else has: MIT’s NANDA research found that 95% of enterprise AI pilots delivered zero measurable P&L impact. Conversely, Gartner’s data shows companies using AI to amplify or augment workers consistently outperform those using it to replace them, with no correlation between workforce reduction and higher ROI.
These findings point to the same underlying reality that AI value creation is a systems problem. The technology is one node in a much larger network of incentives, governance, workflows, and very importantly, culture. Optimize the technology node in isolation and you get a working demo. Optimize the system and you get business impact.
Most organizations are still optimizing the node. They subscribe to an expensive foundation model. Run hackathons. Stand up a Centre of Excellence. Check the boxes that look like progress. And then wonder why they can’t show the board a single initiative that moved a number. We put antenna bands on the phone, there should be signal.
The organizations I’ve seen get this right understand the human system before selecting the tools. They design for adoption from day one, treating behavioural change as a parallel workstream alongside the build. They measure with the same discipline they’d apply to any capital investment, and they’re willing to hear that the answer sometimes involves zero AI at all. Shocking.
Honestly, there are so many inflated expectation moments…
AI is in the Trough of Disillusionment in 2026, which means predictability of ROI has to happen before AI can truly scale in the enterprise. In my experience, the predictability problem is an organizational capability issue, not a technology maturity one. Incentives, operational nuances, identifying real problems at the edge, and if I have to say it again, I will: culture. You can’t how do you do fellow kids your way to AI adoption.
The companies that will pull ahead in the next two years won’t be the ones that spend the most on tokens. (Remember how much of a yikes that number is). They’ll be the ones that figured out how to connect AI deployments to the workflows, behaviours, and business outcomes that actually create impact. I’ll be honest—that work is harder, less sexy, and far more consequential than the technology itself.
Nobody is redesigning the antenna. That’s a problem.
Footnote: For those about to “well, actually”… yes, Apple held a press conference where Jobs demonstrated that other smartphones also lost signal when gripped in certain ways. Yes, Apple offered free bumper cases to affected users. And yes, the precise wording of Jobs’s email was closer to “just avoid holding it in that way” than a verbatim “you’re holding it wrong,” which became the internet’s paraphrase. None of this changes the point. The antenna was in the band. The band was where hands go. The design shipped anyway.