When AI makes your teams more productive, cutting headcount is the wrong instinct. Niel Nickolaisen, IT advisor and Field CTO at Valcom Technologies, draws on a lesson from the shop floor to explain why redeploying that freed-up capacity toward growth beats cost-cutting every time.
Any organization that uses AI's productivity gains to cut staff is making a serious strategic mistake.
Let me say it even more directly. When AI reduces the time and effort it takes to get something done, and an organization responds by shrinking the team that delivered those gains, it is being short-sighted—and trading away far more than it saves.
The productivity gains are real. A 2025 Federal Reserve Bank of St. Louis study found that workers using generative AI saved about 5.4% of their weekly hours—and that's before organizations reorganize to take full advantage. Marketing teams report cutting campaign development time by half.
The question isn't whether AI creates capacity. If I use AI to synthesize data research and that saves me two hours of work every week, I’ve got two hours per week of additional capacity. The question is what you do with the capacity once you have it. I learned the answer years ago—on a factory floor.
When new systems free up capacity, you can bank the savings by cutting the team—or you can keep the team and point that capacity at work you never had time to do before: deeper customer relationships, new products, a more proactive posture. The first option locks in a one-time cost reduction. The second creates profitable growth. And profitable growth almost always wins.
A Lesson from the Shop Floor
My route into technology leadership ran through operations: manufacturing, distribution, supply chain, process improvement. I thought all of that was behind me until I was recruited to be the CIO of a company midway through its acquisition binge in 2014. It had been a manufacturing services business; then it bought three large manufacturers.
Shortly after the third deal closed, the CEO pointed out that we were now, unmistakably, a manufacturing company—and that I was the closest thing we had to a lean expert. I protested that my manufacturing days were long behind me and I'd never been especially fluent in lean. He was unmoved. On top of running IT, I'd be leading the lean implementation across all 120 of our plants.
So, I took a crash course in lean.
Lean is a methodology for identifying and reducing waste in a process. A classic example is rework: anytime you don't get something right the first time, you have to do it again. Tools like value stream mapping, visual controls, and standardized work help you find that waste and remove it.
And here's the big idea—actually, the massive idea: When you reduce waste, you create capacity. Which raises the question every leader eventually faces: what do you do with it? You have two choices. Eliminate the capacity by cutting the team or use it to create new value.
Consider one of our plants, which built window shutters. Changing the machinery from one shutter design to another took about 25 minutes on average. We analyzed the changeover for waste, made the changes, and cut the changeover to roughly 5 minutes. With about 10 changeovers in a typical day—we'd also moved to smaller batch sizes to reduce inventory—we saved around 200 production minutes daily. That's about 3 hours and 20 minutes of newly found capacity, every day.
We had two choices: Keep output flat and shrink the team or keep the team and make more shutters. Demand was there, so we could use the found capacity to produce more—with the same team, the same plant, the same equipment, and the same management overhead. The only added cost was raw materials and a bit more machine time. The financial analysis wasn't close. Option 2 generated far more value, because those extra shutters were profitable growth. And profitable growth beats cost reduction almost every time.
The Same Choice, Now with AI
Which brings us back to AI. As your teams get more productive, you face the same fork in the road: Shrink the team, get the same results at lower cost or keep the team and use the found capacity to generate profitable growth.
Take that marketing team that now builds a campaign in half the time. You could cut headcount. Or you could do the things you never had capacity for before—personalizing campaigns not just to the persona, but eventually to the individual. A “someday ambition” is suddenly within reach.
Or consider security. With machine learning and generative AI, my analysts no longer wade through oceans of alerts; the AI surfaces the events that actually matter. They have time back. Should I lay some of them off? No. I should ask what they could do if they weren't buried in triage—like shifting from a reactive to a proactive security posture. Which helps the business more: fewer people producing the same result, or the same people—already in my cost structure—producing far better ones?
Building the Capacity to Capture the Upside
None of this happens automatically. Left alone, freed-up time quietly dissipates—absorbed by busywork or eliminated in the next budget cycle. Capturing it takes deliberate effort. A few moves help:
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Make the capacity visible. Measure the time AI actually frees up, the way we measured saved minutes on the shop floor. Capacity you can't see is capacity you'll lose.
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Decide where it goes before you deploy. We had demand for more shutters before we cut changeover time. Pair each AI investment with a specific opportunity for the freed capacity—new growth, not vague "efficiency." This requires thinking about, identifying, and prioritizing what you’ll do with newfound capacity.
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Train teams to spot the next opportunity. Freed-up time only pays off if someone sees what to do with it. Teach teams to look for and create the next high-value use—not just automate the task in front of them. It's a skill, and it compounds.
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Protect freed-up capacity from the reflex to claw it back. Treat found capacity as an investment in growth, not a cost to be recovered. The instinct to immediately bank the savings is exactly what you're guarding against.
CIOs finally have the ability to point technology directly at better outcomes. But capturing them requires organizational capacity to spot and pursue the opportunities. Let's not squander a historic opening by cutting away the very capacity we need to seize it.
