AI Employees Reduce Human Performance by 18% as Companies Rush to Integrate Digital Coworkers

AI agents are not your "coworkers"

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool -- one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers recently studied by Emma Wiles, a Boston University business professor, treating Alex as a "coworker" and not a software tool would lead you to do a worse job. Wiles found that people caught 18% fewer errors when the work was said to have come from an agentic "AI employee" rather than a chatbot. It turns out that what's in a name matters. A lot. This is an alarming glimpse of the future Silicon Valley is hurling us toward. Last year Nvidia's CEO, Jensen Huang, talked about workplaces of "digital humans." Since April, Microsoft, OpenAI, Anthropic, and Google have all released new tools oriented toward managing teams of AI agents, many of which are explicitly advertised as digital colleagues with the flexibility and cognitive power of actual humans. And nearly a third of the 1,261 managers who participated in Wiles's study said their companies already frame AI agents as employees (23% even list them on org charts). The technical progress of agentic AI is not all hot air, of course. Agents, which can effectively be thought of as AI tools programmed to work in a loop until they achieve a goal, have become measurably better at more complicated tasks. But it's a huge leap to refer to these tools as coworkers or employees, and doing so will set unrealistic expectations for what AI can do while leaving the human employees supposedly responsible for them worse off. That's partially because, Wiles's research suggests, it inverts our sense of who's in charge. When an AI tool was framed as an employee, participants in the study saw themselves as less responsible for its output. They were also 44% more likely to escalate its questionable work to a manager for further review rather than trusting their own corrections (thus negating the time-saving purpose of using the AI agent in the first place). That matters far beyond office culture: As AI agents are embedded into health care, warfare, education, and government, there's a growing risk they'll become a convenient place to dump blame for failures that are instead the product of bad human decisions, incentives, and oversight (recall how the bomb strike on a girls' school in Iran was popularly blamed on Claude, when all signs point to a cascade of human errors). "AI agents right now are being marketed as things that can replace humans, and I think that's just a losing proposition," says Daron Acemoglu, an economist at MIT who won the Nobel Prize in 2024 and studies AI's impact on the economy. "They should instead be optimized so that they can improve human capabilities, which is not what they have [been] at the moment." What could that look like? Consider a new effort at Stanford, where researchers presented 1,500 workers in 104 jobs with information about what tasks AI could potentially do in their work and then asked what would actually be most helpful and productive. Workers did want automation in certain areas: Law clerks thought AI could help ensure that adequate progress was being made across cases, for example. But often the tasks that tech experts deemed most suitable for AI -- like verifying customer credit ratings for sales reps -- were what the actual workers said they definitely did not want or need an agent to do. Which brings us back to Alex. Calling Alex an employee is easy -- and convenient, especially when something goes wrong -- but it's a branding exercise. It doesn't make the tool more fit for the job, and as Wiles's research shows, it makes the humans around it worse at theirs. And recall that they are the ones with the agency that AI is trying to replicate. They deserve better than Alex.

A.I. 'Employees' Might Disrupt Work in Unexpected Ways

Scholars say the "unknown unknowns" of using artificial intelligence in the workplace may be undermining the technology's advertised benefits. Over the past year or two, companies have started using so-called artificial intelligence agents as bona fide "employees," even including them in their organizational charts. Emma Wiles, a Boston University professor who studies how A.I. affects workers, stumbled onto this phenomenon in October, at a conference where two human resources executives said that treating A.I. agents like real employees was a way to increase productivity and to put their companies on the cutting edge. But when Dr. Wiles and three collaborators from Boston Consulting Group investigated further, they discovered a pitfall. In an experiment involving dozens of companies with A.I. employees, the researchers found that managers tended to vet documents less carefully when told an A.I. employee had produced them. The managers missed errors that other managers caught when told they were vetting the work of a human. Dr. Wiles speculated that managers didn't think sussing out mistakes made by A.I. employees was their responsibility. If something went wrong, they could dismiss it as the fault of the tech team, or of the executives who wanted A.I. employees in the first place. "But it's not your problem," she said, channeling the managers' mind-set about their own roles. In the years since A.I. burst onto the scene, many companies have become aware of flaws produced by the technology and, at times, taken steps to offset them. They know that A.I. models can be biased against certain groups of people, like nonwhites. They know that chatbots can provide confident but incorrect answers to queries. They know that the bots sometimes spill the beans on information that should remain private. But as companies race to bring A.I. into their day-to-day operations, researchers are discovering more subtle defects. In principle, these flaws could be corrected, too. For example, companies could hold managers directly responsible for the mistakes of A.I. subordinates. But in practice, most corporate users appear to be blissfully unaware of these issues, raising the possibility that A.I.'s promise of increased productivity and vast cost savings could be undermined. Even researchers who study A.I. may be aware of only a fraction of the problems that the technology introduces. "There are a whole host of unknown unknowns," Dr. Wiles said. One well-documented but underappreciated flaw of artificial intelligence models is that they tend to favor work produced by artificial intelligence. A 2025 paper in The Proceedings of the National Academy of Sciences found that several large language models had a low opinion of text written by humans, creating a "potentially consequential form of implicit 'anti-human' bias." But many companies seemed unaware of this problem, or at least unable to imagine how it might wreak havoc on their operations. When a team of scholars spelled it out in a subsequent paper, finding that the A.I. models that companies use to evaluate résumés tend to favor those written with the help of A.I. over those written entirely by humans, it caught the attention of some corporate recruiters. Jane Yi Jiang, an operations professor at Ohio State University who is an author of that subsequent paper, said that she and her co-authors were happy to help when recruiting firms inquired about "how to improve their processes." But they noted that this was almost certainly not the only problem companies were inadvertently introducing in their rush to adopt A.I. "People are moving so fast to use L.L.M.s without thinking too much about the implications, biases," she said. For example, some companies now use A.I. to help answer questions like how much to charge for a product, or where to open a new location. Relying on the technology for such purposes, however, can quickly go off the rails. When left to their own devices, humans often cooperate and seek win-win outcomes. But when A.I. models assess a situation, they tend to adopt the more coldly calculating, "rational" mind-set that arises from basic game theory. They might, say, lead a company to aggressively undercut a competitor, even though it risks a damaging price war. "Most of the L.L.M.s we test think that human beings are more rational than they actually are," said Jiannan Xu, a Ph.D. candidate at the University of Maryland and collaborator of Dr. Jiang's. "But the most rational response leads to a bad situation for all" in many cases. In principle, developers and users of A.I. can correct for these biases. Dr. Jiang and Mr. Xu, for instance, found they could reduce anti-human bias by simply instructing models to focus on the quality of the written material they evaluate, and to avoid considering the author. But A.I. researchers can't correct for biases they aren't aware of, and several scholars said the impact of these undetected biases could grow. One way is if future models are trained on data produced by today's models without sufficient care, creating a kind of self-reinforcing loop. In that case, "the tendency to consolidate on existing perspectives and behaviors seems likely," said Shayne Longpre, an A.I. researcher and founder of the Data Provenance Initiative, a group that monitors A.I. infrastructure. And then there are the blind spots that arise not so much from A.I. itself, but from the way humans use it. Scholars who turn to A.I. at every stage of the research process -- asking A.I. what questions are worth studying; seeking its advice on how to answer these questions; enlisting it to analyze data; relying on it to help write up findings -- could inadvertently narrow the scope of their work. "We don't necessarily notice it at the individual level," said Cecilie Steenbuch Traberg, a psychologist at the Copenhagen Business School and an author of a recent paper on the topic. "You're sparring with a chatbot, it's helping me come up with ideas, you might think it sounds great. But at the collective level, it looks pretty similar. Everyone is sounding alike." Dr. Wiles, the Boston University professor who examined the way humans manage A.I. employees, said the shortcomings weren't necessarily intrinsic to the technology, but arose when humans adopted it with little attention to what could go wrong. She and her colleagues surveyed more than 1,000 corporate managers, and found that about one-third said their organizations referred to A.I. as a "teammate or employee," and that nearly one-quarter said their employer included A.I. agents on its organizational charts. "We call it Scout," one manager told the researchers in an interview, referring to an A.I. agent. "It's technically an equivalent peer on your team." Dr. Wiles and her colleagues gave all the managers they surveyed a set of five documents that contained errors, and gave them 20 minutes to review as many as possible. In some cases they told the managers that an A.I. employee had done the work; in some cases they said that an A.I. tool had done the work; and in some cases they said that a human had done the work. In general, the stated source of the documents didn't make much of a difference in how closely managers vetted them. But managers at companies that included A.I. agents on their organizational charts caught substantially fewer mistakes when told they were reviewing the work of an A.I. employee. People who manage humans tend to assume that "if someone on my team makes a mistake, that's on me," Dr. Wiles explained, which is why they closely check the work of these subordinates. Managers also seem to assume that they're on the hook for work produced by an inanimate A.I. tool. But managers at companies with A.I. employees don't seem to feel the same responsibility for the work of those employees. Her takeaway: Over the past few centuries, scholars and business leaders have developed a reliable set of practices for managing humans. But the psychology of managing anthropomorphized A.I. is vastly different, and "we're going out there blind." She worries that the problem is about to get worse. At the same conference where she first heard H.R. officials talk up the virtues of their A.I. employees, one went even further, saying her company would soon have A.I. employees managing humans. "A hush went over the room," Dr. Wiles recalled. "We'll need someone to study that, too," she added.

'It's just his AI and my AI going back and forth' The workplace phenomenon that's undermining human relationships | Fortune

Stop if you've heard this one before: An employee received a message from her boss and didn't quite understand its meaning. Suspecting it was written by AI, the employee asked her AI tool to interpret the message. The AI responded and then asked if she wanted a draft response back to her boss. The employee paused. "'I literally think [my boss'] AI is talking to my AI. That is the actual conversation happening right now,'" the employee told Leena Rinne, vice president of leadership, business, and coaching at Skillsoft, an edtech and skills management platform. She told Rinne, "'I can't crack the code of working with [my boss], because it's just his AI and my AI going back and forth.'" Rinne calls this phenomenon "socially offloading," or when interpersonal skills that require human judgement, empathy, or courage gets outsourced to AI. It's similar to "cognitive offloading," or shifting often menial tasks to technology like AI to reduce mental effort, and has the potential to disrupt workplace culture. Social offloading can look like a boss is preparing for a performance review and asking AI how to have the conversation. Or, it could be an employee asking to craft a response to a stressful email from a manager. "If I'm always asking AI how do I respond to my boss," Rinne told Fortune, "I don't actually learn how to engage with my boss. I don't actually learn how to build a relationship with my boss." Humans are increasingly using AI in more human ways, with the most common use being for therapy and companionship, according to a Harvard Business Review analysis of AI usage patterns. The problem is not that AI doesn't give helpful advice, Rinne said, but the skills we lose when we rely too much on it. "The risk is then that we don't develop these critical skills that we can use in the moment, because we don't know how to navigate emotional intelligence, if AI is navigating emotional intelligence for us," Rinne said. Skillsoft uses and sells AI tools to their customers, but their tools aim to coach people through how to have real-world conversations. Its product, CAISY, allows people to practice having conversations and provides feedback, before they have important work conversations. Instead of "here's the answer, here's what you should say," Rinne said, the AI instead teaches the person how to develop those intrapersonal skills. "I'm actually building my skill of navigating a difficult conversation or navigating a client conversation because I've had the practice," Paying the price of cutting middle management AI isn't the cause of the problem, but rather a leadership vacuum, Rinne said. As organizations have flattened their organizational structures and cut out middle managers, mentorship and coaching have fallen by the wayside. A prime example of this strategy is Meta, which has cut 25,000 jobs since 2022 and touts an AI team that has one boss for every 50 engineers. Traditionally, a 25-to-1 employee-to-boss ratio is usually seen as the outer limit of the so-called span‑of‑control scale, but the company is going all-in on AI. With AI, some organizations are pushing the limits of management. The recent uptick in younger hires seems to be a common approach, similarly taken by Cognizant, an IT consulting firm that boasts more than 350,000 employees globally on their site, and is on an entry-level hiring spree. "If you can equip these people with AI, you have commoditized expertise. You've handed over expertise on the fingertips. So you could have more entry-level programs, and you could do more school graduates and take them to expertise faster," Cognizant CEO Ravi Kumar S told Fortune earlier this year. While it does flatten the workplace pyramid, "the asymmetry is not going to come from expertise. It's going to come from interdisciplinary skills," he said. Rinne sees the upside from an organizational perspective as fewer managers can lead to quicker decisions and more autonomy. However, managers are still needed to turn strategy into results and into execution, develop talent, and hold a team together, she said. "There's a risk that organizations start treating the span of a leadership's role like it's a math problem, when this is really a capability problem," she said. While other generations have had decades to learn how to navigate change and the organizational dynamics that come with change, now "young people enter the workforce, and they're just thrown into the deep end," Rinne explained. Some have blamed young workers' struggle to navigate the workplace on being generally less social. They're dating and socializing less, and Tessa West, a professor of psychology at New York University whose research focuses on communication between employees and bosses, says that is affecting their ability to perform at work. "You learn a lot of skills in those early relationships that you then leverage in the workplace," West said. "Negotiation is a huge one, and so is compromise." Even romantic relationships can't fill the gap Rinne sees forming between employees and their bosses. She points to her own experience coming up as helping her prepare for her current role as an organization's leader. "I've had amazing opportunities to be coached and to have investment in my development," she said. "The contrast of that is you've got Gen Z coming in, and I think there's this assumption as a digital child, that they are already ready for the pace of change, or they're already ready to navigate." But leaders are not actually equipping younger employees to navigate change, communicate effectively, and have good judgment, she said, which lowers their competitive advantage when human-centric skills are driving success in the AI era. "We're just kind of expecting them to enter this crazy whirlwind moment and be able to navigate it effectively," she said. A version of this story was published on Fortune.com on March 28, 2026. More on AI in the workplace: