Two-thirds of organisations running agentic AI projects haven't moved beyond pilots. That number tends to surprise people until you hear Senthil Muthiah, Senior Partner at McKinsey & Company, explain why. The stall, he argues, isn't a technology problem. It's a strategy problem. What's more, most companies are making the same two mistakes.
The first is treating all work the same, Muthiah explains.
"Service workflows fall in a spectrum, from highly structured, rules-based work to work that requires human judgment. Many companies treat work like they're all the same and use a one-size-fits-all approach, layering agentic AI across the board. While agentic AI moves quickly in the structured areas, it tends to slow down in human decision-making where change management is key."
The second mistake is spreading investment too thin. Rather than identifying where AI will create the most value and concentrating effort there, companies deploy and hope results follow. "Each enterprise has a set of economic leverage points that create disproportionate value when AI is applied," Muthiah says. "Many enterprises take a more organic, inclusive approach — applying AI everywhere — and do not have a clear linkage to value."
Both failures are compounded by impatience. There is a tendency, he says, to load AI deployments with requirements far more stringent than those applied to humans, then assess them against that inflated standard.
"The goal shouldn't be AI for everything. It's AI for the right things, so people are free to focus on high-value work in a coordinated manner."
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Getting the handoff right
Once an organisation knows where to focus, the next challenge is designing the boundary between what AI handles and what humans handle.
"The best handoffs happen when each side is doing the part of the job they're best at," he says.
"AI can take on structured, rules-based tasks, while people step in where nuance, judgment, and real-time decisions are needed. When workflows are designed with that in mind, the transition between AI and humans starts to feel much more natural."
In order-to-cash processes, for example, multiple agents can work across invoicing, collections, and dispute resolution before passing exceptions to human operators. The work moves end-to-end rather than sitting with one team waiting to be triaged. But Muthiah is quick to separate the technical question from the human one.
"The real challenge we're seeing is not the seamlessness of the handoff itself, but the usage and change management required. Offering humans seamless AI has not yet proven that they will necessarily use it."
This gap between deployment and adoption is something organisations consistently underestimate. A well-designed agent workflow means little if the people it's built for don't trust it, understand it, or have any reason to change how they currently work.
What changes for employees
Much of the conversation about agentic AI focuses on what gets automated. Muthiah shifts the frame to what gets freed up. McKinsey's research finds that 70% of human skills remain essential even in heavily AI-augmented environments, and that when the balance is right, the effect on day-to-day work is genuinely positive.
