In Charlie Chaplin’s 1936 film Modern Times, a factory worker struggles to keep pace with an ever-accelerating assembly line—until the machine swallows him whole. Nearly 90 years later, Wharton professor Eric Bradlow has the image on his mind. The machines are smarter now. The stakes are higher. And according to a sweeping new joint report from Accenture and the Wharton School, the humans running them are falling behind in a way that should alarm every boardroom in America.

There is a lot of breathless talk of autonomous agents reshaping every corner of corporate America, from handling sales calls to writing code to managing supply chains. But the report from a partnership between Accenture’s global products practice and Wharton’s AI & Analytics Initiative adds evidence of an emerging, inconvenient pattern: the smarter AI gets, the more it demands of the humans behind it.

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“Intelligence may be scalable, but accountability is not,” says the report, titled The Age of Co-Intelligence: How Humans, AI Agents, and Robots Are Redefining Value. It’s a sentence that sounds almost simple until you sit with what it means for every boardroom deploying agents by the hundreds. “This asymmetry is critical,” it continues, arguing that as AI removes limits on how much thinking and analysis can be done, humans still have to decide what matters, set strategy, and more important, own the outcomes.

The central finding is not that AI is coming for human jobs—it’s that it poses a direct challenge to all the leaders who will have to manage a world of autonomous bots crawling through the white-collar economy. “In a co-intelligent enterprise, leadership does not diminish as AI improves,” the report reads. “It becomes more consequential.”

While the report illustrates hypothetical upsides and doesn’t discuss the downsides of agents run amok, consider single errors rippling through entire systems: one agent’s hallucinated inventory figure causing downstream agents to massively over-order stock; or a customer service agent telling a customer that the problem is fine, and solved, when it isn’t and a human isn’t taking the lead. James Crowley, Accenture’s global products industry practices chair and a coauthor of the report, told Fortune: “We like to say humans in the lead, not in the loop.” If humans aren’t consciously taking the lead, errors can multiply at scale.

The numbers underneath that claim are staggering. Analyzing task-level data across 18 industries using O*NET (Occupational Information Network) and Bureau of Labor Statistics data, Accenture researchers found that more than 50% of working hours across the American economy are now in play—subject to reshaping by about 60 digital and physical AI agents considered in the study. This is a truly massive dataset, corresponding to more than 120 million workers across the 18 industries studied. In banking and capital markets, Wharton and Accenture estimated that the share of hours impacted by digital agents alone exceeds 45%.

A mass redeployment of labor

For a $60 billion company—a real client modeled in the report—the researchers estimated approximately $6 billion in potential annual revenue growth from deploying agentic AI at full maturity, alongside $1.7 billion in annual productivity gains. The catch: By 2028, roughly one-third of those productivity gains showed up not as direct cost savings, but as “capacity freed”—hours that need to be deliberately redirected toward higher-value work, or they simply evaporate.

“Productivity becomes growth only through redeployment,” the report warns. “Unless leaders deliberately redeploy that capacity toward higher-value work, productivity gains stall at efficiency and fail to translate into growth.”

Crowley told Fortune that the failure mode isn’t deploying too many agents—it’s failing to think about them as a coherent workforce rather than a collection of one-off experiments. “Everyone’s building an agent here, an agent there, sometimes thousands,” Crowley said. “What we tried to do is step back and look at what the agentic landscape will look like at an enterprise level.”

That enterprise view is where the accountability problem bites hardest. AI agents are already spreading “rapidly across the enterprise value chain, often ahead of formal strategy and governance,” the report notes, with nearly three-quarters of knowledge workers now using AI—frequently through unsanctioned, bring-your-own tools, a phenomenon sometimes called “shadow AI.” By 2028, roughly a third of enterprise applications are expected to embed agentic capabilities. And yet the report makes clear that governance architecture has not kept pace.

From a tech CEO’s perspective, this report rings true. Andrey Khusid, CEO of Miro, the $17.5 billion productivity startup that made headlines for deciding to leave Russia amid the outbreak of the Ukraine war, recently sat down with Fortune for a chat about the state of things. Miro’s main app, a productivi

‘Intelligence may be scalable, but accountability is not’: A new report exposes the hidden cost of the AI agent revolution