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Tasks that once took six hours now take less than one. A two-week process can sometimes be finished in an afternoon.<\/p>\n
But workers aren\u2019t getting their time back.<\/p>\n
Instead, executives say companies are using those productivity gains to demand more output from the same employees\u2014turning what used to be an eight-hour workload into something far larger.<\/p>\n
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\u2019t what you think. It\u2019s not about the machines taking over. It\u2019s about what happens to employees after AI turns their eight-hour workday into two\u2014and the boss still expects them at their desk until closing time.<\/p>\n
That tension is hardwired into the way companies are quietly rolling out AI tools.\u00a0Now enter Google\u2018s 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.<\/p>\n
In a conversation with\u00a0Fortune, Ahmad said a striking level of\u00a0efficiency is already happening at scale\u2014but 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.<\/p>\n
Many corporate leaders are hesitant to trumpet these wins. \u201cOrganizations are a little bit, nervous, is maybe the word,\u201d 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.<\/p>\n
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.<\/p>\n
courtesy of Google<\/p>\n
Keynes called this 100 Years Ago \u2014 and he was scared, too<\/p>\n
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\u2014and then asked, with obvious unease, what people would do with all that free time. <\/p>\n
Baroness Dambisa Moyo, an economist who is a member of the Starbucks board and in the UK\u2019s House of Lords, raised that same concern in a recent conversation with Fortune. \u201cHe actually said, \u2018will they be contemplating God?’\u201d she noted, adding that she shares Keynes\u2019s worry about a rootlessness enabled by AI advances. \u201cThere are countless countries around the world right now where they have a lot of young men who are doing nothing,\u201d she said, expressing her concern. \u201cThey\u2019re not contemplating God in the manner in which we would want them to.\u201d<\/p>\n
\u201cI am perhaps more worried than Vinod Khosla about what a public policy might do and what society looks like,\u201d Moyo said, referring to the legendary venture capitalist who recently shared his predictions with Fortune Editor-in-Chief Alyson Shontell.<\/p>\n
courtesy of Dambisa Moyo<\/p>\n
The Financial Times\u2018 Tim Harford, the so-called \u201cUndercover Economist,\u201d laid out the same tension from a worker\u2019s perspective in a recent column, citing a piece of UC Berkeley ethnographic research which found AI-enabled tech workers reporting \u201cmomentum and a sense of expanded capability\u201d\u2014but also feeling \u201cbusier, more stretched, or less able to fully disconnect.\u201d <\/p>\n
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\u2014researchers called it \u201cAI brain fry.\u201d 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.<\/p>\n
Harford traced this paradox to the history of supposedly liberating technologies: email was faster than a letter, but spawned a \u201cprofusion of low-quality, low-value messages bleeding into the evenings and weekends.\u201d PowerPoint meant that \u201chighly paid and skilled professionals started wasting time making their own slides badly.\u201d\u200b<\/p>\n
In other words, the question isn\u2019t whether AI gives you back six hours. It\u2019s whether anyone lets you keep them.<\/p>\n
Your 8-hour day is now 2. Here comes 20 hours of new work<\/p>\n
Mike Manos, chief technology officer at Dun & Bradstreet, said his team is getting more done, faster. \u201cI got the eight hours to two hours,\u201d he told Fortune, \u201cbut now I can get 20 hours of work, because the work came down \u2026 it goes back to productivity.\u201d<\/p>\n
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. \u201cIt\u2019s not so much about people are going to lose jobs, or you\u2019re going to sort of shrink that workforce,\u201d he said. \u201cIt\u2019s about becoming more efficient and, in our case, getting to market faster.\u201d\u200b More capabilities, services, and features will have to be delivered within the same historical timeframe.<\/p>\n
courtesy of Dun & Bradstreet<\/p>\n
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\u2019s code was now written by AI, resulting in \u201cwell over a 10% velocity gain\u201d when multiplied across tens of thousands of engineers. Google CEO Sundar Pichai disclosed this figure in a podcast with Lex Fridman in mid-2025.<\/p>\n
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\u2019s Orlando Lakehouse facility that time spent preparing for his executive meetings\u2014once a \u201chuge chunk\u201d of his day\u2014dropped 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.\u200b<\/p>\n
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\u2019t see a Keynesian workweek resulting, framing the issue as a story of growth. \u201cThat means I can put more volume through my business,\u201d he said, agreeing that most CEOs are working on the same thing right now. \u201cMy business should be growing and will grow. I see the number of my employees going up, not down, because of that.\u201d\u200b Walsh acknowledged that \u201cthe mix\u201d of workers will change, but he stressed, \u201cthis is a huge opportunity.\u201d<\/p>\n
courtesy of KPMG<\/p>\n
A reality check from the C-suite<\/p>\n
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 \u201ca lot of hard work, very expensive, and not an instant job killer.\u201d 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\u00a0Ashok Shenoy\u00a0noting the project broke even within a year.<\/p>\n
The reason companies still need employees, Cappelli said, is that \u201clots of problems have to be chased down, and they\u2019re harder to chase down if they come off of AI\u2026 so that\u2019s the payoff, but it\u2019s not cheap, and it took a hell of a long time to do.\u201d Headlines announcing layoffs attributed to AI, he added, deserve skepticism: \u201cIf you read what they actually say, they say, \u2018We expect that AI will cover this work.\u2019 Hadn\u2019t done it. They\u2019re just hoping. And they\u2019re saying it because that\u2019s what they think investors want to hear.\u201d (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.)<\/p>\n
Walsh of KPMG agreed with Cappelli\u2019s takeaway, saying that \u201cembedding AI into a business takes time.\u201d Organizations have to \u201crework all of the process flows,\u201d 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\u2014and those with the capital to invest\u2014have been doing this for the past two years already, he said, characterizing it as just a start. \u201cThere\u2019s so much work to be done around this.\u201d<\/p>\n
Everyone is \u2018traveling west\u2019<\/p>\n
The catalyst for the productivity shift\u2014where it is actually happening\u2014is the evolution of what Google calls the \u201cagentic data cloud,\u201d in which AI models no longer just answer questions but also act as planners and executors. Google\u2019s Gemini 3, for instance, has moved beyond simple Q&A to what Ahmad calls a \u201cthinking role.\u201d<\/p>\n
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.<\/p>\n
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\u2019s behalf. Anthropic\u2019s 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.<\/p>\n
Salva, who spent a decade at Microsoft before joining Google, acknowledged that \u201cwe all know that we\u2019re traveling west\u201d\u2014meaning the entire industry shares the same vision of AI autonomy, even if the paths differ.\u200b \u201cWe\u2019re all trying to get to the same degree of automation. We have slightly different flavors of implementation and workflows for it.\u201d<\/p>\n
courtesy of Google<\/p>\n
The jobs that are already gone (you just haven\u2019t heard yet)<\/p>\n
The sector where agentic AI is landing hardest\u2014and where the workforce implications are most acute\u2014is customer operations.\u00a0Eric Buesing, a McKinsey partner who advises financial institutions and insurers on service transformation, told Fortune that the shift he\u2019s observing in just the past six months is qualitative, not just incremental. <\/p>\n
\u201cThe difference we\u2019re 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,\u201d he said, \u201cand seeing where an agentic agent is able to actually\u00a0automate\u00a0large portions of work entirely so that they can actually reimagine kind of how work is done.\u201d\u200b<\/p>\n
courtesy of McKinsey<\/p>\n
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. \u201cThese processes generally require either very long conversations or multiple interactions,\u201d Buesing said, offering the examples of a front-facing representative capturing information while a back-office team works on the decision. \u201cAI 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.\u201d\u200b<\/p>\n
A McKinsey survey of 440 customer experience and operations executives found that 60% or more of the tasks performed in customer operations today are \u201cpotentially addressable with AI.\u201d But Buesing was careful to separate the addressable from the capturable. \u201cWhat is addressable versus what will be capturable, and with what time period? Humans don\u2019t necessarily adapt to change as quickly as the technology is evolving,\u201d he told Fortune. <\/p>\n
The new AI voice agents, which six months ago still sounded \u201ctremendously robotic,\u201d have recently crossed a threshold. Latency is barely perceptible, and the agent \u201csounds casual, fun, friendly, even a little bit joking around.\u201d Early evidence also suggests that, in certain contexts, such as first-round job interviews or ordering sensitive medication, customers actively\u00a0prefer\u00a0talking to AI because they \u201cdon\u2019t feel judged.\u201d\u200b<\/p>\n
Buesing said he had independently read the same\u00a0Harvard Business Review\u00a0article on work intensity and largely agreed with its premise. Once building AI agents moves from \u201cnights and weekends fun project work\u201d to the expected baseline output an employer demands, workers will \u201cfind themselves on a wagon wheel of having to build more agents to try to keep up with the expectations of production,\u201d he told Fortune.\u200b<\/p>\n
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, \u201canxious\u201d\u2014and he said that was risky. \u201cThe importance for HR professionals right now is not as much about the technology,\u201d he said. \u201cIt\u2019s more around how we lead through the technology. How do we bring our workforce alongside the transformation?\u201d<\/p>\n
courtesy of ADP<\/p>\n
The answer, Richardson suggested, is not to hide productivity gains but to invest visibly in people so they feel equipped for the new regime. \u201cInvesting in upskilling is not just a strategy,\u201d she said. \u201cIt\u2019s a reassurance. It\u2019s a trust pact between the employer and the worker.\u201d\u200b 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. \u201cWe need to help reframe productivity for our workers,\u201d she said, because little task completion moments will be swallowed up by AI efficiencies. \u201cTo me, it\u2019s shifting from productivity based on volume of work to value [of work], and that\u2019s a big shift within an organization.\u201d<\/p>\n
For Salva at Google, who has spent 25 years watching developer tools evolve, the better analogy for where we are isn\u2019t email or PowerPoint. It\u2019s the five stages of autonomous driving, and we\u2019ve only reached stage three or four. The real promise, he told Fortune, isn\u2019t that AI does your job faster; it\u2019s that it changes\u00a0which parts\u00a0of the job are yours to do. He said the best developers he sees today aren\u2019t hammering at keyboards\u2014they\u2019re \u201clocked into the architecture,\u201d delegating execution to \u201ca fleet of agents\u201d running in parallel while they hold the big picture in their heads. \u201cThat,\u201d he said, \u201cis where productivity happens. That\u2019s where focus and flow happen.\u201d\u200b<\/p>\n
courtesy of ADP<\/p>\n
Where Salva diverges from some of his competitors is in what the future should feel like. \u201cIf we\u2019re optimizing for short attention spans,\u201d he said, \u201cwhat we\u2019re really sacrificing is that delightful Zen moment that you get when you\u2019re locked in\u201d\u2014the 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.\u200b<\/p>\n
The real disruption isn\u2019t technical. It\u2019s cultural<\/p>\n
What Manos at Dun & Bradstreet found is that the real disruption isn\u2019t technical, it\u2019s cultural. \u201cAt the end of the day, the AI revolution will be successful when you\u2019ve actually changed the people and the people culture to adopt this new framework,\u201d 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.<\/p>\n
\u201cWe didn\u2019t jump in and go, \u2018Everybody AI tomorrow,’\u201d he said. \u201cYou\u2019ve just got to be a little bit fleet of foot to be able to dance and learn what you\u2019re being shown and pay attention to what you\u2019re being shown.\u201d He also said that different teams adopt at different speeds, and making room for that allows the learning curve to unfold.<\/p>\n
Buesing said he saw the same pattern in his client work. Organizations are now overwhelmingly \u201cin pilot to scale, scaling, or building plans to introduce agentic AI\u201d\u2014but the human side of the equation is lagging the technology. \u201cThat wave is coming,\u201d he told Fortune. \u201cAnd I think organizations may be a little bit slow on that right now.\u201d <\/p>\n
The job titles themselves are already in flux. Buesing said he\u2019s already heard companies experimenting with terms like \u201cadvocate\u201d or \u201cjourney manager\u201d to replace the old \u201cagent\u201d label\u2014partly because it\u2019s become hopelessly ambiguous in the age of AI agents, and partly because the human role genuinely is becoming something new.<\/p>\n
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\u2019s 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 \u201cfactory of the future.\u201d) <\/p>\n
\u201cThe 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,\u201d he said.<\/p>\n
courtesy of Venki Padmanabhan<\/p>\n
The companies that simply cut, he warned, \u201cwill milk the economic value of the knowledge that the AI had from past practice for maybe 10, 15 years. But there\u2019s no more new knowledge being developed because humans develop knowledge, and then the well will run dry.\u201d\u200b<\/p>\n
The honest answer, as Manos summed it up, is that those six free hours you just saved by using AI aren\u2019t coming anytime soon. What is coming is a widening aperture\u2014more problems to solve, more projects to chase, a bigger version of the job. \u201cThe work is not going to go away,\u201d he said. \u201cPieces and parts of the work may go away, but that just means we\u2019re going to be able to address more.\u201d\u200b<\/p>\n
Manos noted that Dun & Bradstreet traces its founding to before the Civil War and has survived through business iterations dating back to Abraham Lincoln\u2019s time. The business model of organizing data, he pointed out, used to look very different. \u201cThe 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.\u201d 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.<\/p>\n
Whether that\u2019s liberation or a treadmill set to a higher speed is shaping up to be the defining labor question of the decade.<\/p>\n
#productivity #paradox #work<\/p>\n","protected":false},"excerpt":{"rendered":"
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