Scaling agentic AI for operational breakthroughs | McKinsey
4/21/26, 11:14 Scaling agentic AI for operational breakthroughs | McKinsey
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Scaling agentic AI for operational
breakthroughs
April 1, 2026 | Video
T
Agentic AI is all about doing things—just like operations. McKinsey partner
Michael Chang explains its potential, why waiting to implement could be risky,
and the keys to realizing impact.
his transcript has been lightly edited for clarity.
What differentiates agentic AI, and why is it
critical in the race to rewire operations?
Many companies are already adopting gen AI and agentic AI. The underlying technology is all the same.
What makes agentic AI especially good for operations, and companies that focus on operations, is that it is
all about doing things. What makes agentic AI di!erent is that it will do the thing for you.
Video
If you think about a company's process and operations, a lot of these tasks require judgment, inputs, and
then activities happen—you have to make certain determinations. In the age of gen AI, you still require a
human to make those decisions or changes in manufacturing, procurement, or product development. But in
the world of agentic AI, that part can be automated.
The value at stake for agentic AI is signi"cantly larger. We're seeing manufacturing or product development
processes shrink in lead time by 20 or 30 percent. The critical part to deliver impact is where you want to
deploy these agents and how they work together to realize the expected impact.
How mature is agentic AI in operations today,
and where are the greatest opportunities?
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4/21/26, 11:14 Scaling agentic AI for operational breakthroughs | McKinsey
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How mature is agentic AI in operations today,
and where are the greatest opportunities?
The maturity of agentic AI in operations is, honestly, at a very early stage. I think there are a lot of root
causes to that. The number one reason: companies typically see this as a big change to how they do things
right now, and business operations are very complicated by nature. However, the potential and the value
are absolutely there.
We see a lot of early movers starting to see signi"cant impact, particularly in areas such as procurement,
where we already have traditional AI, such as spend cubes or di!erent breakdowns of cost.
If you take that and you apply negotiation strategy, you can build agents that help to understand di!erent
negotiation tactics and come up with recommendations to help our supply managers negotiate more
e!ectively. This can bring improvements of 5 to 10 percent in terms of bottom-line procurement costs.
In manufacturing, deciding how to translate initial product design into con"guration of a manufacturing line
and how to break down the di!erent steps into a manufacturing assembly process can be done in days or
weeks with agentic AI.
Where do companies get stuck when scaling
agentic AI, and how can they break through?
There's a lot of di!erent research out there, but the consensus is that out of 100 companies that attempt
such transformations, 90 percent don't see real "nancial bene"t.
Although the future potential of agentic AI is very promising, most operations companies are still facing a
lot of hurdles. Unless you have top-down, leadership buy-in, most companies are in a wait-and-see mode.
They want to see what is actually proven, what has been done elsewhere, before they adopt it internally.
That poses two risks: Number one: you are going to be left behind with a higher cost base than your
competitors. Number two: this is not a situation where it's going to "x itself. The core gap in most
organizations is a talent one. Because if you're not willing to take the steps to understand what can be
done with agentic AI, you're not going to build a team behind it that can actually make it happen.
Successful implementation of agentic AI, at the end of the day, still comes down to:
1. knowing where the impact is and where you want to embed agents into your processes
2. reimagining those processes so that humans and agents collaborate together to maximize the
impact
3. having a clear execution engine and governance so that all of this can be deployed e!ectively
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ABOUT THE AUTHOR(S)
Michael Chang is a partner in McKinsey’s Taipei o&ce.
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