AI Investment Backfires: Execs Admit They Value Human Workers Less Despite Poor Returns

Execs admit AI makes them value human workers less

As suits say they're burning cash on brainboxes without seeing results Executives have leaned in to AI, only to stumble before reaching any return on their investment. "Most AI spending has under-delivered, leaving execs feeling like they're burning cash," says employment biz G-P (Globalization Partners) in its third annual AI at Work Report. The report finds corporate leaders' enthusiasm for AI waning as ROI proves elusive. Sixteen percent of companies saw a negative ROI from AI investments last year, and 73 percent of executives whose AI efforts did pay off said ROI fell short of expectations, according to the report. These findings are based on a survey of 2,850 executives (VP level and up) in the US, Germany, Singapore, Australia, and France, including a separate set of 500 US HR professionals. The AI at Work Report is a little cheerier than last year's findings from MIT NANDA researchers who discovered only five percent of organizations have managed to successfully put AI projects into production. Regardless, execs anticipate scaling back their AI budgets if organizational goals aren't met this year. Beyond their worries about financial benefits, corporate execs in the G-P survey have doubts about the reliability of AI, a concern borne out by recent Microsoft research. Only 23 percent of the G-P respondents said they have total confidence in AI accuracy. Those concerns mean 69 percent said they spend more time monitoring and reviewing AI, while 61 percent expressed concerns about using AI to craft sensitive documents because they doubt the output is legally accurate. Moral unease doesn't appear to be doing much to help corporate leaders empathize with workers, however. The survey found that "82 percent of executives admit AI has lowered the value they place on human employees." In fact, these leaders appear to have become somewhat suspicious of their people - about 88 percent expressed concern that employees are using AI performatively rather than adding business value. But among such misanthropic, skeptical managers, there's enough lingering humanity to ensure that only 12 percent strongly agree "that sacrificing employee privacy for AI monitoring is worth it to reach business goals." Despite the sense that AI has reduced how human workers are valued, about half of execs still cite the scarcity of employees with AI skills and the lack of data literacy as barriers to their AI goals. You still need human talent to stand up money-losing AI projects. ®

Bad news employee -- most executives admit using AI makes them value human workers less

* Four in five execs say they were less likely to value human employees after using AI * AI still requires human oversight, and many struggle to fully trust it * Poor and even negative ROI continues to plague many A new study by Globalization Partners has revealed more than four in five (82%) company execs say they are less likely to value human employees after using AI tools, positioning human workers as secondary assets after more capable systems. This sentiment differs from the current state of affairs, whereby 60% of the 2,850 surveyed senior execs agreed humans still lead work operations with AI merely serving as a productivity booster. The difference could imply that, while humans remain integral today, managers may place less of an emphasis on the human workforce in the future as AI gets more work done autonomously. AI is impacting how much top managers value their human workers The shift likely positions humans as AI managers, rather than administrative workers, with two in three (69%) now spending more time than ever before monitoring and reviewing AI-generated work. The sense of a lack of trust still lingers, too, with only 23% having total confidence in AI's accuracy and 61% worries about legal accuracy when using AI on sensitive documents. However, while some execs see AI as a human replacer, many others are still dissatisfied with their returns. Three-quarters (73%) say ROI has fallen short of expectations, with 16% even reporting negative ROI. As a result, around seven in 10 execs say they're prepared to cut AI budgets this year if goals are not met. Separately, Gartner VP Analyst Padraig Byrne explained, "AI is everywhere, but most organizations are still figuring out how to monitor and trust these systems." Giving a sneak peak into where companies might be getting it wrong, the research firm implied that those building AI agents without strong semantic and contextual data foundations are most likely to see hallucinations, unreliable outputs and biases. Together, the two reports indicate that while execs are increasingly seeing AI as unavoidable, many are still struggling to trust it. Looking ahead, Gartner calls for the implementation of model monitoring policies to provide quite quality metrics and an increased focus on infrastructure to handle high-volume model telemetry. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

Large Study Finds That Replacing Workers With AI Is Backfiring Badly

Can't-miss innovations from the bleeding edge of science and tech As AI continues to weave its way into every corner of daily life, one of the public's chief fears is what it will mean in the workplace. They're not irrational to worry. Many name-brand big tech companies have already sacked thousands of workers in favor of the technology, from Meta to Square -- a trend that sets up a natural experiment: are these AI layoffs actually resulting in positive business outcomes? That's why a new study from Gartner immediately caught our eye. As Fortune reports, the research and advisory firm surveyed 350 global business executives whose companies are pulling in at least $1 billion annually to investigate whether all these AI layoffs are paying off in the real world. The first takeaway is that the trend is real, with a total of 80 percent admitted to trimming their human staff to make investments in AI or autonomous technology. But they say they had no idea if AI would actually generate any benefits -- they were simply buying into the promise of automation via AI. That's where things get interesting. The Gartner survey found that execs who slashed staff to invest in AI have seen the same financial gains as those who held onto their employees. In othe words, attempting to replace workers with AI isn't showing any detectable returns for these companies. And to make matters worse, many of these businesses specifically reduced their headcount to free up the cash needed for AI technology, meaning they sacrificed valuable institutional knowledge and employee goodwill for nothing. The findings aren't entirely surprising. An MIT study last year found that AI is failing to generate meaningful revenue growth at the vast majority of companies that embrace it. Still, not everyone believes that all investment in AI is destined to backfire. Gartner analyst Helen Poitevin told Fortune that these seemingly drastic moves by execs may simply be attempts to trial AI, not to structurally reset the whole company. "It seems to us to be a kind of one-time exercise by many in small amounts, but not what translates to getting full ROI from their AI investment," Poitevin told Fortune. So which companies are seeing an actual bump from AI? The Gartner survey found that companies leveraging AI as a form of "people amplification" -- meaning they give their employees AI tools to boost efficiency, instead of replacing them outright -- are seeing the most significant gains. Even that strategy is fraught, though: previous research has suggested that the majority of employees aren't keen on using AI just yet, with one survey revealing 54 percent avoid using in-house AI tools altogether.

AI isn't paying off in the way companies think. Layoffs driven by automation are failing to generate returns, study finds | Fortune

The ongoing dialogue regarding the ever-imminent displacement of white-collar workers by AI is predicated on the assumption that the technology will become as skilled as the very workers it threatens to displace, thereby cutting labor costs. But a new study found that's not quite what's playing out in many companies that have carried out AI-related layoffs. A survey of 350 global business executives with an annual revenue of at least $1 billion by the research and advisory firm Gartner found that many have reduced their workforce irrespective of AI adoption. While 80% of those surveyed who have piloted an AI or autonomous technology have reported workforce reductions, the businesses cut jobs due to automation regardless of whether the technology was actually generating returns. "Looking only at layoffs is shortsighted in terms of getting value from AI," Helen Poitevin, VP analyst at Gartner and a key researcher of the study, told Fortune. "Chasing value only through headcount reduction is likely to lead most organizations down a path of limited returns." The looming threat of AI automation has many employees fearing for their jobs. But a growing number of business leaders and economists are skeptical that the technology will actually spur layoffs. Apollo chief economist Torsten Slok recently argued the Jevons paradox: a 19th century theory that explained why the demand for coal increased even as steam engines became more efficient and coal became cheaper. The paradox also applies to the AI age, Slok argued, and it predicts the technology will lead to more jobs, not less. Poitevin said the companies reporting high ROI were not the same ones reporting AI-related workforce reductions. In fact, workforce reduction rates were nearly equal for those reporting higher ROI and those with smaller returns or even worsened outcomes from autonomous operations. "That's not where the value is," she said of layoffs. "That's not where the productivity gains are going to be." Instead, the study found companies with the highest gains were those using AI as a form of "people amplification," implementing the technology to make workers more productive rather than outright replacing them. There's a growing divide today in how global business leaders are approaching AI adoption. In a separate Gartner survey of CEOs and other business executives, about one-third said they expect autonomous AI to help humans make decisions, but stop short of making those decisions independently. But another 27% said they expect AI to do exactly that, with minimal or no human involvement. Anthropic CEO Dario Amodei recently walked back his controversial claim from last year that AI would wipe out half of white-collar entry-level roles. He instead said AI could augment work, referring to the Jevons paradox, though cautioning that AI is evolving at a faster rate than previous technologies and could consequently lead to different outcomes. "When you strain a system more than, you know, than it's usually strained, it's possible you get these weird behaviors and this big disruption," he said. Layoffs attributed to AI have become a common practice, at least across Silicon Valley. Outplacement services company Challenger, Gray and Christmas found that AI was the leading reason for layoffs in March and April, and the total number of layoffs attributed to AI hit 49,135 for the full year. That's nearly as much as the total for all AI-related layoffs the firm reported in 2025. However, AI innovation isn't the sole reason for layoffs in this category; layoffs attributed to heightened AI spending has become a trend across hyperscalers allocating a high percentage of their budgets on the AI infrastructure buildout. As a result, companies like Microsoft and Meta have said they needed to cut headcount to free up cash. There's also the possibility that many of these layoffs are attributed to AI but are in reality inspired by other underlying motivations in a stunt known as "AI washing." That's what Sam Altman said in an interview earlier from February. "I don't know what the exact percentage is, but there's some AI washing where people are blaming AI for layoffs that they would otherwise do, and then there's some real displacement by AI of different kinds of jobs," he said. But Poitevin said the data shows these layoffs, even if related to AI, appear to be a way companies are testing the waters with AI rather than initiating a structural reset. "It seems to us to be a kind of one-time exercise by many in small amounts," she said, "but not what translates to getting full ROI from their AI investment."

AI layoffs do not result in returns for companies, finds report

Gartner's report found that organisations need to invest in a workforce that can lead the transition to autonomous capabilities. Over the course of the last year, there have been a range of high-profile layoffs as a result of the continued investment into AI and its capabilities. Recently Cloudflare announced plans to cut 20pc of its workforce after AI usage at the company grew by 600pc in three months and in April social media and tech platform Meta told staff that it will be laying off 10pc of its workforce, roughly 8,000 employees, reportedly as a means of mitigating the costs of heavy AI spending. Similarly, Snap is laying off 16pc of its workforce to cut costs and focus on AI. Gartner surveyed 350 globally dispersed business executives in the third quarter of 2025, to better understand the state of autonomous business at enterprises. Qualifying organisations reported enterprise-wide annual revenue of at least $1bn or the equivalent and had been piloting or had already deployed either an AI agent, intelligent automation or autonomous technologies. Of the organisations taking part in the piloting or deployment of autonomous business capabilities, roughly 80pc admitted to reducing their workforce. Gartner's research found that these reductions do not appear to translate to a return on investment (ROI) for the organisations making the changes. The survey found that workforce reduction rates were nearly equal among respondents that reported a higher ROI from autonomous technologies and those that experienced only modest gains or negative outcomes. Commenting on the findings of the report, Helen Poitevin, a distinguished vice-president and analyst at Gartner said, "Many CEOs turn to layoffs to demonstrate quick AI returns, however, this disposition is misplaced. "Workforce reductions may create budget room, but they do not create return. Organisations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles and operating models that allow humans to guide and scale autonomous systems." Despite the increased layoffs as a result of AI adoption, Gartner's research suggested that, while autonomous business will continue to increase alongside AI agent software spending, "the need for people will go up, not down". To that point, Gartner predicts that "autonomous business will be a net-positive job creator by 2028 to 2029, driven by new forms of work that AI cannot absorb". "Long term, autonomous business will create more work for humans, not less. Lasting structural factors such as demographic decline and high-stakes, trust-dependent consumer moments will ensure human talent remains central to running, governing and scaling autonomous business," said Poitevin. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

Beyond the layoffs - will companies live to regret their AI-related job cuts? (Spoiler - they just might...)

All too many employers are placing their businesses at strategic risk by cutting headcount before being sure AI can perform the tasks they need it to, an AI expert has warned. Shomron Jacob is head of Applied Machine Learning and Platform at enterprise AI applications platform provider, iterate.ai. He believes the big question organizations should ask themselves today is whether they are dealing with job cuts "strategically or reactively". He explains: The pattern is real: companies are restructuring workflows around AI capabilities, which inevitably changes headcount requirements. The critical question isn't whether this is happening, but whether companies are doing it strategically or reactively. From what I've seen evaluating enterprise AI strategies, most organizations are making these decisions without proper readiness assessments. They're cutting roles before they've validated that AI can actually perform those functions reliably. But this approach will inevitably create "strategic risk" for the business, Jacob warns: We're going to see a wave of regret [due to premature redundancies] similar to what the Orgvue research suggests. Companies that eliminate expertise before building proven AI capability end up with skills gaps, institutional knowledge loss, and failed automation initiatives that cost more than the headcount savings. Matthew Baden, Managing Director for Technical at tech recruitment consultancy The Search Experience, agrees: A lot of companies rushed to replace people with AI to capture quick wins, only to find that current models still produce fairly generic output that needs significant human oversight. When you cut experienced people too quickly, you lose tenured knowledge and the ability to handle edge cases - the exact areas where AI struggles to keep up. We're already seeing some quiet regret and selective rehiring. AI works best when it amplifies strong people, not when it replaces them outright. As a result, he believes that most job roles are more likely to be re-defined rather than disappear entirely, especially if they combine technical work with judgment, context, or customer insight. The upside of this situation, Baden says, is that companies will be able to operate leaner teams with better output per person. The downside is that if cuts are made too quickly, institutional knowledge disappears and employees end up dealing with a lot of AI-generated output that still needs fixing. Loss of institutional knowledge and skills gaps are, in fact, "the most underestimated risks" when employers undertake AI-based re-structuring, Jacob indicates: When you eliminate experienced staff, you don't just lose their task execution. You lose their pattern recognition, their understanding of edge cases, their ability to detect when something is wrong. AI systems don't develop intuition about 'this answer seems off' the way experienced humans do. The companies experiencing regret are predominantly those that treated AI deployment as a headcount reduction exercise rather than a capability transformation project. They optimized for short-term cost savings rather than long-term system reliability and performance. This pattern of regret, meanwhile, follows a clear sequence: As for the kinds of jobs most likely to be affected by this situation, Jacob indicates it is not as simple as saying 'automatable tasks will be eliminated'. Instead, he points to three key categories of roles employers need to think about: But he indicates that a big danger today lies in employers replacing roles that should actually be re-defined. He explains: You can't just eliminate analysts and have AI do their job. You need different analysts who can evaluate AI output, catch hallucinations, and maintain institutional knowledge. Companies that miss this distinction end up regretting [having made staff redundant]. As a result, while Jacob believes a "permanent shift toward AI-augmented work" is taking place, he also forecasts: Significant near-term volatility as companies learn the hard way which roles AI can actually handle versus which require human expertise...From enterprise evaluations I've conducted, I'd estimate fewer than 20% of companies making AI-driven headcount decisions have actually validated that their AI systems can perform at the required reliability and safety levels. That's not an AI-first strategy. That's speculation dressed up as transformation. As a result, in his view, the winners will be those organizations that focus less on "cost reduction through replacement" and more on workforce transformation supported by a suitable investment in re-skilling. To take a strategic rather than reactive approach to implementing AI, meanwhile, requires employers to be thoughtful and measured in how they approach change. This includes headcount cuts. As Baden says: The key is to treat it as a proper team redesign, not just cost-cutting dressed up as AI strategy. The most successful approach Jacob has seen, on the other hand, is to 'pilot before cutting, validate before scaling, and reskill during the transition'. In other words: Other key considerations that are all too often forgotten about here, Jacob believes, are change management, governance, and the use of sound evaluation systems. For instance, he says, most organizations introduce their AI systems without suitable guardrails, including which decisions they can make autonomously and which require human approval. Such guardrails only tend to emerge after an incident has occurred. Another common challenge is skills mis-matches. The problem here is that companies may choose to sack the employees who used to do the work. But they subsequently often find that new people are required to evaluate if the AI is performing its assigned tasks correctly. A third widespread problem is that many organizations are unable to measure the performance of their AI systems reliably. This is because they have no frameworks for measuring AI hallucination rates or the quality of the tools' decision-making. But as Jacob points out: The key to success is found in the ordinary and unexceptional: run proper pilots, build evaluation frameworks, establish governance before deployment, and invest in re-skilling. Companies that skip these steps to move fast invariably pay for it later through failed deployments, quality issues, and the costs of rebuilding institutional knowledge they eliminated prematurely. As to what the next 12 to 18 months are likely hold though, Jacob expects to see a "reckoning between AI hype and AI reality in production environments". This is because the gap between what AI systems can do in controlled demos and messy production environments is "substantial", with many companies about to "discover this the hard way". He also anticipates some companies starting to "quietly rehire" for roles they previously cut too aggressively, particularly in those cases where AI systems have underperformed against expectations or had quality issues. This, he says, will not be framed as 'we were wrong about AI'. Instead, it will be pitched as 'evolving our AI strategy' or moving to 'hybrid human-AI models'. As Jacob concludes though: The longer-term trend is toward AI-augmented work rather than wholesale replacement but with significant near-term volatility as the market separates hype from capability. Companies that survive this transition successfully will be those that treated AI deployment as a strategic capability transformation requiring readiness assessment, governance, and change management, and not just as a cost-reduction exercise. It has been said before (repeatedly) but I'll say it again: those organizations that focus on cost-cutting and see AI as an easy means of getting rid of headcount may live to regret their hasty decisions. As Jacob points out, the secret to real success going forward lies in investing in reskilling to support a broader workforce transformation.

CEOs Say AI Gives Them Only Two Options, and Both Are Bad News for Employees

Can't-miss innovations from the bleeding edge of science and tech In an age of AI, our hardworking CEOs are being tortured with a tough decision, according to new Wall Street Journal reporting: they can embrace AI and lay off scores of employees -- or keep their employees, but use AI to make them work even harder. If those options sound like a false dichotomy to you, you're probably not wrong. But it's emblematic of the logic that business leaders, gripped by AI fomo, are operating with. No one wants to be left behind by the latest technological leap, and few would pass up the opportunity to produce more for less -- which is what AI, however dubiously, promises. Exemplifying this logic, Spotify co-CEO Gustav Söderström opined during a recent earnings call that businesses can either translate AI "straight into cost savings and cut headcount," or they could "say we're going to be roughly the same amount of people, we 're just going to do more." Spotify, for its part, is "keeping our head count roughly flat and just doing much more shipping, more value to consumers," Söderström said. Many CEOs are opting for the AI layoffs route, at least nominally. Jack Dorsey's fintech company Block said it was laying off 4,000 employees, or 40 percent of its global workforce, citing efficiency gains from AI. Atlassian fired 1,600 while also extolling a pivot to AI. The crypto hub Coinbase said it would slash its headcount by 14 percent this week, with its CEO Brian Armstrong telling employees that AI would allow them to be more productive. "Over the past year, I've watched engineers use AI to ship in days what used to take a team weeks," Armstrong wrote to staff, per the WSJ. "This is a new way of working." In all, AI was cited in the announcements of more than 54,000 layoffs last year, one survey found. The trend doesn't look like it'll slow down either, as many companies are jumping on the latest crop of AI models' ability to churn out code. A recent Gartner survey cited by the WSJ suggested that 80 percent of companies using AI agents and other autonomous tools said they're also cutting staff. Yet, forcing employees to use AI to do more work isn't the uncomplicated, surefire route to success it's made out to be. Emerging research suggests that AI is actually intensifying work, driving employees towards burnout and causing "brain fry" as they use AI tools to multitask to a ludicrous degree. An MIT study that found the overwhelming majority of companies saw zero growth in revenue after adopting AI raised major doubts that embracing the tools would bring a return in investment. Tech hirings often come in dramatic boom and bust cycles, so it's difficult to say how much of these cuts are truly down to AI or business leaders responding to other economic trends. It's likely that even the shot-callers don't truly have a grasp on how AI will shape their workforces. "We don't really know what the optimal size of the company will be in the future," Meta chief financial officer Susan Li told investors last week, per the WSJ.

Layoffs Don't Deliver AI ROI -- Redeploying Workers Does, Data Shows

Many business owners who've deployed artificial intelligence (AI) tools across their workplaces are feeling pressure to justify that spending with proof the work automating tech has lowered their overall costs. Often, that has been engineered by companies carrying out huge headcount cuts to lower their labor expenses, thereby buoying their profits. But a new study by business consultancy Gartner indicates layoffs are an ineffective way of achieving return on investment (ROI) in AI, with the better method being retention of human employees and refocusing their work on more value-creating tasks. The gritty managerial dilemma now before many leaders was highlighted in a Wall Street Journal report this week titled, "AI Is Forcing CEOs to Make a Stark Choice: Lay Off Workers or Make Them Do More." Frequently, the decision has been the former, as companies counter-balance investments in the tech by slashing staffs -- a move that also has the advantage of thrilling Wall Street analysts and investors. Indeed, just this week crypto platform Coinbase announced a 14 percent headcount cut. That followed earlier moves by Atlassian, Meta, and Amazon to reduce about 10 percent of their workforces respectively, and PayPal saying it plans to eliminate 20 percent of its jobs over the next few years. That steady drumbeat of mass layoffs is increasing fears across the labor force that warnings of "AI apocalypse" are already being substantiated, with the tech continually pushing huge numbers of workers into unemployment. Those worries are likely to increase even more, with Gartner estimating companies that spent $86.4 billion on AI in 2025 will increase those investments to $206.5 billion this year, and $376.3 billion in 2027. Fortunately, some business leaders believe there's a better alternative to lopping off large portions of staffing to offset -- and justify -- the huge sums spent on developing and adopting AI. Results of Gartner's newly released survey of 350 business executives indicates that deploying the tech to put employees to more effective and profitable use winds up being a wiser and more profitable long-term solution than headcount cuts. "Many CEOs turn to layoffs to demonstrate quick AI returns; however, this disposition is misplaced," said Helen Poitevin, distinguished vice president analyst at Gartner in comments accompanying the findings. "Workforce reductions may create budget room, but they do not create return. Organizations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles and operating models that allow humans to guide and scale autonomous systems." Some business leaders have already adopted that alternative to staff cuts. As the Journal noted, Spotify, IBM, and taser manufacturer Axon Enterprise are all pursuing plans to use AI in ways that free employees from repetitive, lower-impact, and gruntier tasks so they can be reassigned more interesting, enriching, and valuable work. That view starkly contrasts the "brutally clear and honest" comments recently made by Bed, Bath & Beyond CEO Marcus Lemonis, who warned that AI will drive a "significant reduction in head count" in coming months. Lemonis has been far from alone in taking that approach to AI and staff management. According to Gartner's survey, fully 80 percent of "organizations piloting or deploying autonomous business capabilities" like apps and chatbots have resorted to layoffs to offset spending on those, and generate savings "that appear to translate into return on investment." However, in looking more carefully at outcomes, Gartner discovered "workforce reduction rates were nearly equal among respondents reporting higher ROI from autonomous technologies and those experiencing only modest gains or negative outcomes." In other words, the tech alone didn't generate increases or declines in productivity, nor did cuts in staffing. Instead, Gartner found that companies that deployed AI and other automation tech most profitably usually maintained headcount, and assigned employees to new, farther-reaching, and more valuable work tasks using those tools. Doing so, Gartner's report on the survey said, resulted in "human-amplified" businesses harnessing AI to empower rather than replace staff -- and attain benefits over far longer periods than the brief financial sugar-boosts that layoffs usually produce. "Long term, autonomous business will create more work for humans, not less," said Poitevin. "Lasting structural factors such as demographic decline and high-stakes, trust-dependent consumer moments will ensure human talent remains central to running, governing and scaling autonomous business." Get 1 Smart Business Story delivered straight to your inbox when you subscribe to Inc.'s free daily newsletter.