David Turk is Vice President of Digital Strategy & Solutions at GEI, leading digital strategy and AI adoption across the AEC industry.
Organizations are racing to prove that AI is making their people more productive. I’m seeing this play out firsthand as organizations track adoption, measure hours saved and celebrate work that can now be completed faster. Those are real wins, and they matter.
But what if productivity is the wrong place to declare victory?
Saving time is not the same as creating business value. It creates an opportunity to do something with that time. The hours AI gives back could become more work, better client outcomes, higher margins, new growth or more time for people to learn and develop. But none of those things happen automatically because an hour was saved. Productivity creates the opportunity for value. It does not decide what that value becomes.
And that may be the harder part of the AI productivity race. As the gains accelerate, leaders face a far more important question than how many hours were saved: What happens to those hours next?
AI saved the hours. Now what?
Consider a task that once took an employee 10 hours to complete. With AI, it now takes six. The productivity gain is real. Four hours have been freed up. On a dashboard, that looks like a win.
But where did those four hours actually go?
Maybe the employee completed more of the same work. Maybe they spent more time with a client, solved a harder problem or developed a new skill. Maybe the time improved margins or helped a team meet a deadline. Or maybe those hours simply disappeared into an already busy day.
That is why productivity is the beginning, not the ending. AI can give time back, but it cannot decide what an organization should do with it. And as these gains multiply across hundreds or thousands of employees, that distinction becomes much bigger than a few hours on a timesheet.
Hours saved are an AI metric. What those hours become is a leadership question.
Everyone owns a piece. Who owns the whole?
This is where converting productivity into value gets messy. I’ve seen the same AI initiative look very different depending on where you sit in an organization. The AI team sees adoption and hours saved. Operations sees delivery and throughput. Finance sees the economics. HR sees workforce capacity and development. Business leaders see clients, growth and pricing. Employees experience what actually changes in the work.
None are necessarily wrong. They are simply looking at different pieces of the same value problem.
The challenge is that those pieces rarely fit neatly together. Different functions have different measures, incentives and even different business languages. The organization is trying to manage an integrated value problem through functions that each see only part of it.
That disconnect has consequences. An organization can become very good at creating and measuring AI productivity while remaining surprisingly bad at turning those gains into business value. Adoption can rise. Hours can be saved. Dashboards can improve. The technology can work as intended, and the value can still get lost between the pieces.
AI didn’t create this organizational fragmentation. It exposed it.
That may be why the value-capture gap is so difficult to close. It isn’t necessarily a lack-of-data problem. It may be a lack-of-connection problem.
Everyone may be doing what they’re supposed to do. But who is connecting the pieces?
Don’t just do more. Decide what matters more.
The easiest response to AI-created capacity is to fill it. If an engineer saves four hours, give them four more hours of work. But the engineer may see an opportunity to improve the technical solution. A project manager may need help on another deliverable. A client leader may want that time spent strengthening a relationship. Finance may see an opportunity to improve margin. Everyone may see value in those four hours, but they can’t all spend those four hours.
That is where strategy enters the conversation. The hardest part isn’t identifying things that create value. It is choosing among them. Lots of things create value. Strategy is deciding which ones matter most.
Business models complicate that decision. AI creates an uncomfortable tension for businesses that sell time: What happens when the technology makes the thing you’re selling take less time? In professional services, completing work faster on a time-and-materials contract can have very different economics than on a fixed-fee engagement. Same productivity gain. Different business value.
So the answer cannot be command-and-control from the top or a free-for-all from the bottom. Leaders cannot decide how every saved hour should be used, and employees cannot decide on their own what the organization should value. The two have to work together if those individual hours are going to translate into meaningful value at scale.
Leadership defines the direction of value. People closest to the work determine how to create it.
Creating value is one thing, but who captures it is another.
Creating value is not the last check mark. Organizations have to be intentional about how that value is captured and by whom.
At some point, the C-suite will huddle up and ask a simple question: We invested millions in AI. Where did the return actually show up? “We saved thousands of hours” may no longer fly.
Value capture cannot be an accidental byproduct. Business leaders and those closest to clients need to understand where value was created, where it moved and who was positioned to capture it. Someone has to connect those dots and determine how much value was actually captured.
As technology becomes more accessible, what remains scarce becomes more valuable. As producing answers gets easier, understanding which answer matters most to the client becomes the greater challenge. Using AI productively will be necessary to survive. Turning it into tangible business value is how organizations thrive.
AI productivity may soon be the price of admission. Value capture is where the real competition begins.
Organizations that mistake productivity for the finish line may discover too late that it was only the starting line. By then, the race may already be over.
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