Every board meeting I sit in lately seems to feature the exact same slide. A well-meaning department head puts up a chart showing that thanks to their new AI tools, their team is saving "40 hours a week" on drafting emails, summarizing reports, or generating code.
The executives nod, applaud the efficiency, and move on. Nobody asks the only operational question that actually matters: Where did those 40 hours go?
I have audited the operational throughput of these teams. I can tell you exactly where the saved time goes. It goes into a void. It goes into longer coffee breaks, endless slack scrolling, or worse—it goes into generating exponentially more low-value work.
When you measure AI success by "time saved," you are completely misunderstanding the economics of artificial intelligence.
Before AI, creating a 10-page market research brief took a human three days. That friction acted as a natural filter. Because it was hard to create, we only asked for it when we genuinely needed it.
Today, AI can generate that same 10-page brief in twelve seconds. The friction is gone. So what happens? Middle managers start requesting five briefs a day instead of one a month. The marketing team isn't acting on better information; they are just drowning in a higher volume of perfectly formatted noise.
We have successfully automated the production of corporate clutter.
Making a useless process faster is not an operational win. It is operational self-sabotage.
If an AI tool saves your customer support team 20 hours a week drafting replies, but your customer churn rate hasn't dropped and your resolution quality hasn't improved, your ROI is exactly zero. You just built a faster hamster wheel.
We need to completely rebuild how we evaluate enterprise AI. Operations leaders must stop tracking output metrics (words generated, time saved, tickets closed) and start tracking throughput and quality metrics.
Are we making decisions faster?
Has our defect rate dropped?
Are we closing deals with a higher win rate?
Is the cognitive load on our top performers actually decreasing?
If the answer to those questions is no, it doesn't matter if your shiny new LLM integration saves your team 100 hours a month. You are not buying efficiency. You are just subsidizing a faster version of the status quo.
Stop measuring the speed of the engine. Start measuring the destination of the car.
Top comments (0)