I sat in an executive meeting last quarter where the answer arrived before the question did.
The CEO opened with two slides. The first showed AI tools the company had deployed across six departments. The second was a headcount projection. "If we’re getting this much productivity," he said, "how many people can we cut by Q3?"
Nobody in the room had measured the productivity. Nobody had asked what the tools were actually changing. The conversation jumped from adoption to reduction in under ten minutes. I’ve seen this meeting three times now, at three different companies, and it plays out the same way every time.
They’re not alone. Fiverr’s CEO told employees to "automate 100 percent" of their work with AI, then cut 250 people a few months later to become an "AI-first company." Klarna replaced 700 customer service employees with an AI chatbot that handled two-thirds of all queries. Quality dropped. Customers revolted. The CEO later admitted they "went too far" and started quietly rehiring humans. Roughly 55,000 jobs were cut in layoffs that companies attributed directly to AI in 2025, more than three times the total in the preceding two years.
The pattern is the same everywhere. The solution shows up before the problem is understood.
A recent study from the National Bureau of Economic Research surveyed nearly 6,000 executives across the US, UK, Germany, and Australia. About 70 percent of their firms are using AI. Nine in ten report no meaningful change in employment or productivity. The executives themselves spend an average of 1.5 hours a week using AI, less than the workers they manage.
That’s the gap I keep running into. Not between companies that have AI and companies that don’t. Between companies that adopted AI and companies where AI actually changed something.
Here’s what most people miss. AI has collapsed the cost of execution. Writing code, generating content, summarizing research, building prototypes... all of it is faster and cheaper than it was two years ago. That part is real. But when execution gets cheap, it stops being the bottleneck. The bottleneck moves up the stack, to the decisions, the context, and the organizational thinking that execution depends on.
Most companies haven’t noticed the shift. They’re still optimizing for speed at the task level. They’re still treating AI as a tool. Helpful. Fast. Impressive in demos. But still sitting on the edges of the business.
It writes. It summarizes. It assists.
It doesn’t decide. It doesn’t orchestrate. It doesn’t change how work flows through the company.
Tools improve tasks. Systems change outcomes.
Early factories made the same mistake with electricity. They replaced steam engines with electric motors and expected the same work to get faster. Nothing happened. Only when they redesigned the factory floor, rethinking how materials moved, how workers collaborated, how decisions got made, did productivity jump.
AI is at that same inflection point. And the companies cutting headcount before redesigning their operations are making the electric motor mistake all over again.
I’ve spent the last two years building Revolv around a single frustration: the questions most AI strategies never bother to ask. Not questions about models or vendors. Questions about the business. Where do decisions slow down? Where does context get lost? Where do relationships quietly determine outcomes, but never show up in any system?
Every executive I’ve worked with knows the answers. They just haven’t built around them.
Deals depend on who knows who. Decisions depend on who has context. Execution depends on whether that context actually moves. Yet most systems capture transactions, not relationships. Outputs, not intent.
Here’s an example. A CFO is about to approve a vendor switch. The spreadsheet says yes: better pricing, stronger SLA, cleaner integration. What the spreadsheet doesn’t say is that the incumbent’s CEO just joined the board of their biggest client. One relationship, invisible to every system in the building, changes the entire decision. That kind of context lives in people, not dashboards. No CRM captures it. No AI tool surfaces it.
This is the real problem. Not that companies lack AI. They lack coherence. They lack the connective tissue between what people know, what decisions need to happen, and what the organization is actually trying to do. When hundreds of AI-generated outputs exist but nobody can tell which ones matter, the bottleneck isn’t execution. It’s clarity. Product clarity. Strategic alignment. Organizational cognition.
This is what I’ve learned building Revolv. If AI is layered on top of broken workflows, fragmented knowledge, and invisible relationship dynamics, it produces exactly what the data shows today. Activity without impact.
But when you connect people, context, and decisions into a system where intelligence can actually operate, the math changes. Decisions get faster. Knowledge builds on itself. Organizations stop reacting and start moving with intent.
The executives in that NBER study expect impact to come. Soon. They’re right. But not because the models will get better.
Because the companies that figure this out will stop asking how to make AI faster and start asking how to make their organizations think better together. They’ll move from tools to systems. From outputs to outcomes. From information to context. And from isolated intelligence to embedded intelligence.
AI makes building cheap. Systems thinking makes building meaningful.
That’s the shift. And it’s already underway.
