Tasks that once took six hours now take less than one. A two-week process can sometimes be finished in an afternoon.
But workers aren’t getting their time back.
Instead, executives say companies are using those productivity gains to demand more output from the same employees—turning what used to be an eight-hour workload into something far larger.
You used to spend six hours on that. Now it takes 40 minutes. But nobody is sending you home early. The anxiety gripping corporate America about artificial intelligence (AI) isn’t what you think. It’s not about the machines taking over. It’s about what happens to employees after AI turns their eight-hour workday into two—and the boss still expects them at their desk until closing time.
That tension is hardwired into the way companies are quietly rolling out AI tools. Now enter Google‘s Yasmeen Ahmad, the senior customer-facing executive for data cloud strategy as managing director of Google Cloud. She is the person that Fortune 500 companies call when they want to figure out how to put AI to work on their data infrastructure. In other words, she hears how the AI revolution is actually landing behind the scenes, rather than just in a press release.
In a conversation with Fortune, Ahmad said a striking level of efficiency is already happening at scale—but executives are keeping it quiet. Take the energy company AES, which transformed a 14-day auditing and data entry process into a task that now takes just one hour, she said. Or take Dun & Bradstreet, the data and analytics giant, which shrank number-crunching from hours to minutes.
Many corporate leaders are hesitant to trumpet these wins. “Organizations are a little bit, nervous, is maybe the word,” Ahmad told Fortune. In private conversations with Google, she said, executives admit they are thinking hard about the implications of what all these efficiencies are suggesting.
The nervousness reflects a paradox about a giant leap forward in time savings that turns out to be very real. The question of what replaces that time is not.
Keynes called this 100 Years Ago — and he was scared, too
Economists and philosophers have been here before. John Maynard Keynes famously predicted in the 1930s that by 2030, a 15-hour work week would be possible—and then asked, with obvious unease, what people would do with all that free time.
Baroness Dambisa Moyo, an economist who is a member of the Starbucks board and in the UK’s House of Lords, raised that same concern in a recent conversation with Fortune. “He actually said, ‘will they be contemplating God?'” she noted, adding that she shares Keynes’s worry about a rootlessness enabled by AI advances. “There are countless countries around the world right now where they have a lot of young men who are doing nothing,” she said, expressing her concern. “They’re not contemplating God in the manner in which we would want them to.”
“I am perhaps more worried than Vinod Khosla about what a public policy might do and what society looks like,” Moyo said, referring to the legendary venture capitalist who recently shared his predictions with Fortune Editor-in-Chief Alyson Shontell.
The Financial Times‘ Tim Harford, the so-called “Undercover Economist,” laid out the same tension from a worker’s perspective in a recent column, citing a piece of UC Berkeley ethnographic research which found AI-enabled tech workers reporting “momentum and a sense of expanded capability”—but also feeling “busier, more stretched, or less able to fully disconnect.”
This research aligned with a study published in the Harvard Business Review that found early adopters of AI were finding work more intense, which some observers note is almost vampiric in its effect. The HBR, in fact, is finding more complementary research over time, such as the Boston Consulting Group study which found that workers who constantly supervise multiple AI tools report higher levels of mental fatigue, information overload, and decision fatigue—researchers called it “AI brain fry.” Employees who spent more time monitoring AI outputs rather than letting the systems run independently experienced 12% more mental fatigue and significantly more information overload, suggesting that the tools meant to save time can also create new layers of cognitive work.
Harford traced this paradox to the history of supposedly liberating technologies: email was faster than a letter, but spawned a “profusion of low-quality, low-value messages bleeding into the evenings and weekends.” PowerPoint meant that “highly paid and skilled professionals started wasting time making their own slides badly.”
In other words, the question isn’t whether AI gives you back six hours. It’s whether anyone lets you keep them.
Your 8-hour day is now 2. Here comes 20 hours of new work
Mike Manos, chief technology officer at Dun & Bradstreet, said his team is getting more done, faster. “I got the eight hours to two hours,” he told Fortune, “but now I can get 20 hours of work, because the work came down … it goes back to productivity.”
Instead of sending workers home early, Manos said his teams are simply getting more done. A product development cycle tracking to take 24 to 36 months was completed in six months once his team incorporated AI capabilities. Rather than reduce staff, he redeployed those developers to additional projects. “It’s not so much about people are going to lose jobs, or you’re going to sort of shrink that workforce,” he said. “It’s about becoming more efficient and, in our case, getting to market faster.” More capabilities, services, and features will have to be delivered within the same historical timeframe.
That mirrors the picture at Google itself. Ryan Salva, a senior product lead who helped launch GitHub Copilot before joining Google as a Senior Director of Product, Developer & Experiences in mid-2024, told Fortune that 50% of Google’s code was now written by AI, resulting in “well over a 10% velocity gain” when multiplied across tens of thousands of engineers. Google CEO Sundar Pichai disclosed this figure in a podcast with Lex Fridman in mid-2025.
KPMG National Managing Partner of Advisory for Strategy and Markets Patrick Ryan reported a similar shift in his own routine, telling Fortune in conversation at the consulting firm’s Orlando Lakehouse facility that time spent preparing for his executive meetings—once a “huge chunk” of his day—dropped by around 75% after deploying Gemini at KPMG. Within two weeks of launch, he estimated that over 90% of KPMG professionals were using the tool.
Tim Walsh, Chair and CEO of KPMG U.S., agreed in an interview that companies are doing the hard work of shrinking the proverbial task from six hours down to two hours, and that he doesn’t see a Keynesian workweek resulting, framing the issue as a story of growth. “That means I can put more volume through my business,” he said, agreeing that most CEOs are working on the same thing right now. “My business should be growing and will grow. I see the number of my employees going up, not down, because of that.” Walsh acknowledged that “the mix” of workers will change, but he stressed, “this is a huge opportunity.”
A reality check from the C-suite
Not everyone is seeing such clean wins. Wharton professor Peter Cappelli, who has been studying AI adoption across enterprises, previously told Fortune that the reality is “a lot of hard work, very expensive, and not an instant job killer.” Take digital services company Ricoh, a firm that Cappelli studied closely. AI helped it become three times as effective while reducing the number of roles to only three, but at an elevated cost of $200,000 per month. Ricoh confirmed these numbers to Fortune, with VP Ashok Shenoy noting the project broke even within a year.
The reason companies still need employees, Cappelli said, is that “lots of problems have to be chased down, and they’re harder to chase down if they come off of AI… so that’s the payoff, but it’s not cheap, and it took a hell of a long time to do.” Headlines announcing layoffs attributed to AI, he added, deserve skepticism: “If you read what they actually say, they say, ‘We expect that AI will cover this work.’ Hadn’t done it. They’re just hoping. And they’re saying it because that’s what they think investors want to hear.” (Cappelli was talking to Fortune before the Silicon Valley fintech Block, led by CEO Jack Dorsey, announced a whopping 40% layoff, citing AI efficiencies, which is arguably another example of what he mentioned, or a leap forward in the adoption story.)
Walsh of KPMG agreed with Cappelli’s takeaway, saying that “embedding AI into a business takes time.” Organizations have to “rework all of the process flows,” which includes cleaning up their internal data, aligning all their data flows in the same direction, and doing so across the entire business, across the back office, front office, or middle office, whichever the company is focusing on. Large companies—and those with the capital to invest—have been doing this for the past two years already, he said, characterizing it as just a start. “There’s so much work to be done around this.”
Everyone is ‘traveling west’
The catalyst for the productivity shift—where it is actually happening—is the evolution of what Google calls the “agentic data cloud,” in which AI models no longer just answer questions but also act as planners and executors. Google’s Gemini 3, for instance, has moved beyond simple Q&A to what Ahmad calls a “thinking role.”
She claimed that the model can first build a plan, then explore multiple approaches, evaluate them against each other, and hone in on the best answer for the customer.
Google is not alone in going this direction. OpenAI has made a similar agentic push with its Operator product, which can autonomously browse the web and complete multi-step tasks on a user’s behalf. Anthropic’s Computer Use feature, embedded in Claude, allows agents to interact directly with desktop applications. Meanwhile, Microsoft has built Copilot Studio, its own enterprise agentic layer, directly into its Azure cloud, giving it a distribution advantage across the thousands of companies already running on Microsoft infrastructure.
Salva, who spent a decade at Microsoft before joining Google, acknowledged that “we all know that we’re traveling west”—meaning the entire industry shares the same vision of AI autonomy, even if the paths differ. “We’re all trying to get to the same degree of automation. We have slightly different flavors of implementation and workflows for it.”
The jobs that are already gone (you just haven’t heard yet)
The sector where agentic AI is landing hardest—and where the workforce implications are most acute—is customer operations. Eric Buesing, a McKinsey partner who advises financial institutions and insurers on service transformation, told Fortune that the shift he’s observing in just the past six months is qualitative, not just incremental.
“The difference we’re seeing, even from six months ago, is organizations are stepping away from small pilots and experiments with generative AI, where they were finding 5, 10, 15, 20-second savings,” he said, “and seeing where an agentic agent is able to actually automate large portions of work entirely so that they can actually reimagine kind of how work is done.”
The back office of an insurance company, he argued, is a prime example: binding a new policy or processing a small business loan currently requires multiple customer interactions, a front-line rep capturing information, a back-office team making a decision, and then a rep communicating that decision back. “These processes generally require either very long conversations or multiple interactions,” Buesing said, offering the examples of a front-facing representative capturing information while a back-office team works on the decision. “AI can perform those functions faster, run a customer history profile in real time while the customer is still speaking to the front-line rep, and help that human make a decision.”
A McKinsey survey of 440 customer experience and operations executives found that 60% or more of the tasks performed in customer operations today are “potentially addressable with AI.” But Buesing was careful to separate the addressable from the capturable. “What is addressable versus what will be capturable, and with what time period? Humans don’t necessarily adapt to change as quickly as the technology is evolving,” he told Fortune.
The new AI voice agents, which six months ago still sounded “tremendously robotic,” have recently crossed a threshold. Latency is barely perceptible, and the agent “sounds casual, fun, friendly, even a little bit joking around.” Early evidence also suggests that, in certain contexts, such as first-round job interviews or ordering sensitive medication, customers actively prefer talking to AI because they “don’t feel judged.”
Buesing said he had independently read the same Harvard Business Review article on work intensity and largely agreed with its premise. Once building AI agents moves from “nights and weekends fun project work” to the expected baseline output an employer demands, workers will “find themselves on a wagon wheel of having to build more agents to try to keep up with the expectations of production,” he told Fortune.
ADP Chief Economist Nela Richardson and her colleague Jay Caldwell offered another perspective during a joint breakfast with media members in New York City. AI is entering a workforce that is already, as Caldwell put it, “anxious”—and he said that was risky. “The importance for HR professionals right now is not as much about the technology,” he said. “It’s more around how we lead through the technology. How do we bring our workforce alongside the transformation?”
The answer, Richardson suggested, is not to hide productivity gains but to invest visibly in people so they feel equipped for the new regime. “Investing in upskilling is not just a strategy,” she said. “It’s a reassurance. It’s a trust pact between the employer and the worker.” She said companies have a lot of work to do, adjusting to the new mentality of what it means to do work in the AI age. “We need to help reframe productivity for our workers,” she said, because little task completion moments will be swallowed up by AI efficiencies. “To me, it’s shifting from productivity based on volume of work to value [of work], and that’s a big shift within an organization.”
For Salva at Google, who has spent 25 years watching developer tools evolve, the better analogy for where we are isn’t email or PowerPoint. It’s the five stages of autonomous driving, and we’ve only reached stage three or four. The real promise, he told Fortune, isn’t that AI does your job faster; it’s that it changes which parts of the job are yours to do. He said the best developers he sees today aren’t hammering at keyboards—they’re “locked into the architecture,” delegating execution to “a fleet of agents” running in parallel while they hold the big picture in their heads. “That,” he said, “is where productivity happens. That’s where focus and flow happen.”
Where Salva diverges from some of his competitors is in what the future should feel like. “If we’re optimizing for short attention spans,” he said, “what we’re really sacrificing is that delightful Zen moment that you get when you’re locked in”—the deep focus that he believes is where the most important work actually gets done. He said he spends significant time thinking about how to design tools that preserve that state even as they delegate the mechanical work to external systems.
The real disruption isn’t technical. It’s cultural
What Manos at Dun & Bradstreet found is that the real disruption isn’t technical, it’s cultural. “At the end of the day, the AI revolution will be successful when you’ve actually changed the people and the people culture to adopt this new framework,” he said. He thinks his company is succeeding where others have failed in AI adoption because it approached things differently. It rolled out AI gradually, starting with small wins: automating the repetitive tasks, like quality assurance testing.
“We didn’t jump in and go, ‘Everybody AI tomorrow,'” he said. “You’ve just got to be a little bit fleet of foot to be able to dance and learn what you’re being shown and pay attention to what you’re being shown.” He also said that different teams adopt at different speeds, and making room for that allows the learning curve to unfold.
Buesing said he saw the same pattern in his client work. Organizations are now overwhelmingly “in pilot to scale, scaling, or building plans to introduce agentic AI”—but the human side of the equation is lagging the technology. “That wave is coming,” he told Fortune. “And I think organizations may be a little bit slow on that right now.”
The job titles themselves are already in flux. Buesing said he’s already heard companies experimenting with terms like “advocate” or “journey manager” to replace the old “agent” label—partly because it’s become hopelessly ambiguous in the age of AI agents, and partly because the human role genuinely is becoming something new.
Venki Padmanabhan, who is currently a plant manager at a manufacturing firm in Ohio after a globe-spanning career that included several stints as a chief executive in his native India, told Fortune that he’s spent decades studying human potential in the workplace, and he has a longer historical view. His favorite example is a Siemens plant in Amberg, Germany, that kept the same 1,100 employees over 20 years while technology evolved around them. Those workers went on to generate eight times the business output. (Siemens calls this its “factory of the future.”)
“The companies that understand how to unlock this intelligence, engage their people, deploy the tacit knowledge they already have, then use AI are going to win extraordinarily,” he said.
The companies that simply cut, he warned, “will milk the economic value of the knowledge that the AI had from past practice for maybe 10, 15 years. But there’s no more new knowledge being developed because humans develop knowledge, and then the well will run dry.”
The honest answer, as Manos summed it up, is that those six free hours you just saved by using AI aren’t coming anytime soon. What is coming is a widening aperture—more problems to solve, more projects to chase, a bigger version of the job. “The work is not going to go away,” he said. “Pieces and parts of the work may go away, but that just means we’re going to be able to address more.”
Manos noted that Dun & Bradstreet traces its founding to before the Civil War and has survived through business iterations dating back to Abraham Lincoln’s time. The business model of organizing data, he pointed out, used to look very different. “The way they used to do it was, get on a horse, ride into town, figure out who the blacksmith was and who the grocery store was, and then they wrote it down and put it in a book.” The work is the same now as it was then, but all the horses are gone, all the locations are changed. The context has changed, but it still works.
Whether that’s liberation or a treadmill set to a higher speed is shaping up to be the defining labor question of the decade.
