Companies slash jobs for AI investments, but Gartner data reveals layoffs fail to deliver returns

Companies Don't Have to Slash Jobs Because of AI | Andrew Winston | MIT Sloan Management Review

Many entry-level white-collar jobs are at risk in the near future, thanks to AI's capacity to execute many of the tasks young workers have traditionally done. But companies that hold back from swapping artificial intelligence for real people may end up with an advantage: If they maintain the talent pipeline that has always served them, they could have a strategic advantage over their competition. There's just one challenge: getting corporate leaders' buy-in. "If AI is going to destroy all the jobs, why don't we just stop?" That was the rhetorical question my college-age son asked after we talked about the possibility of drastic changes to career paths and society thanks to AI (technically, generative AI). It was in line with what I've been worrying about myself. Nobody really knows how disruptive AI will be. But young people and their parents would be foolish not to prepare for deep, unprecedented change in how we work. A huge portion of entry-level white-collar jobs -- the kind that college graduates normally flock to and count on as career springboards -- may not exist in the near future. I'm not alone in these estimations, obviously. Dario Amodei, the CEO of Anthropic, has been brutally honest about what he believes his products will do to hiring. He has (repeatedly) said that half of entry-level jobs -- especially in fields like finance, consulting, law, and tech -- are likely to disappear within a few years. Interestingly, he's changed his tune very recently, suggesting that there's an opportunity for job growth. But either way, the facts on the ground bear out the concerns. Reductions have begun: Goldman Sachs estimates that 16,000 jobs are evaporating every month. So, what's to be done? In a widely circulated clip from a May 2024 interview, former Google CEO Eric Schmidt put it plainly: Once AI agents develop a suite of skills that allow them to start working together on their own, away from our guidance, "we won't understand what the models are doing." His suggested solution? "Pull the plug." It's a gut response that I feel a strong affinity toward, even as I dive deep into using AI myself. As I watch the world barrel toward a truly unknown future and the potential devastation that AI could wreak on job markets and young workers, I feel a mounting unease about how companies are starting to respond. What makes this challenge particularly hard to solve is that the executives making decisions about AI deployment and jobs will be fine regardless of how this plays out. They have capital, seniority, and options -- financial and otherwise. It's sadly uncommon for leaders to think beyond market cap and their own vesting schedules and consider whether we all can thrive. That inequality in exposure to risk is part of what makes this more than just a business question. When society faces deep risk, companies and leaders tend to make choices that seem optimal for their short-term interests. From a pure short-term-profit perspective, bringing in fewer workers is probably the financially smart thing to do. But thinking about only the short term poses significant danger. With this latest existential challenge, if companies continue to head down a "people-light, AI-token-heavy" path, the risks aren't just to young workers but to businesses, too. The microeconomic case for some degree of caution is this: If companies decimate entry-level roles, what happens to the pipeline for leadership? Service businesses have long had a pyramid model where lots of young, smart kids come in and get trained and tested, and then a small subset make it to partner or other senior roles. So, what if companies just didn't eliminate as many jobs? Yes, we're about 40 years into this model of businesses announcing cuts and their stock rising -- investors often love companies that fire people. But what if, this time, they just didn't? The companies that preserve human judgment, build institutional knowledge, and keep developing talent may find themselves with the advantage down the road. Parallels to Inaction on Sustainability Watching the march toward the job slashing unfold, I feel a sense of déjà vu. It's a collective action (or inaction) problem, much like climate change. We have watched the scale of potential environmental devastation rise fast, in real time, and have still struggled to respond with urgency. In both cases, with society facing deep risk, many companies have made choices that seem optimal for their short-term interests. The results could be catastrophic for everyone. I'm haunted by a conversation I had a decade ago with the COO of a major corporation. I did my normal spiel about all the ways that sustainability can create value over time. His response: "Yeah, I understand there are reasons to do sustainability, but we can't go under." For context, this company had netted $10 billion the previous year (not revenue, profit). Let's say that the company had promised Wall Street it would grow those profits at a modest 4% in the next year, that is, to $10.4 billion. Let's imagine now that the company had taken $100 million, an absolutely outrageous sum in the sustainability world, and invested in decarbonization or materials innovation or circular models for its products. If it had made real progress on decarbonization over the next 10 years, it would have less to worry about if, say, the price of oil suddenly spiked (to pick a metric of the moment). Today the company would be far more resilient, and it would be serving shareholders very well. And in that first year, its profits would have been $10.3 billion -- quite a ways off from bankruptcy. One of the main reasons we keep finding ourselves in this situation is a huge misperception about collective risk and the costs of action. Executives have long said some version of "but my shareholders" when faced with longer-term, collective challenges. The narrow focus on short-term shareholder value has resulted in the business community having a really poor record of managing systemic risks (or even just not making them worse). Of course, with AI job displacement, the harm may accrue to society without ever landing back on the specific companies doing the displacing (unlike climate, where physical risk and regulation eventually hit the balance sheet). That asymmetry is what makes voluntary restraint so hard to enact and sustain, and why this may ultimately require policy, not just persuasion. The positive interpretation for the selective blindness about collective risk and the undermining of shared resources is that every new direction, like AI, is exciting and impossible to forgo; a more realistic interpretation is that there's just money to be made in the current path, so collective well-being be damned. A Call for Human-Focused Strategy Could companies just decide that they won't trade people for AI? What if they didn't cut as many jobs? It's possible that some companies would be less competitive, but it's unlikely that they would "go under," as my COO friend worried. There's a pretty big gap between today's record corporate profits and significantly worse results (let alone bankruptcy). At the same time, we truly and profoundly don't know what business will look like with AI acting as everybody's assistant -- that is, "augmenting" their work, to use a rising phrase, instead of replacing it. With the relentless pressure to cut costs and maximize profits, companies may feel like they're not in control. Talking about the role of business in society, shared prosperity, or everything under the banner of "sustainability" has been in retreat. Yes, there are a few signs that companies may be held accountable for more than their profits; the recent legal action against Meta for putting click and eyeball maximization ahead of children's well-being is one. But even with, for instance, significant financial benefits from transitioning to the clean economy, companies have collectively underinvested in action on climate change for decades. Can we figure out how to not make similar mistakes with AI? In the end, every decision to invest or to not invest is a choice. I'll be honest about the tension here: I'm asking companies to accept potential (short-term) competitive disadvantages on the basis of uncertain future benefits and collective responsibility. That's a hard sell, and I don't want to pretend otherwise. But it's also exactly what we in sustainability have been asking from companies for years regarding climate change. As with climate, we need policy changes around AI to encourage collective action, but policy moves slowly, and decisions about AI displacing workers are being made now. We know AI isn't going away. What it can already do can feel like magic. And its use will rise as companies mandate it and people discover what it does well, acting as their assistant, researcher, editor, and more. But GenAI has some serious issues and flaws, such as its tendency to hallucinate. And its footprint and effect on communities is enormous. I write this as a practitioner watching this unfold up close, and as someone who uses AI every day and is actively working on how to reduce its energy footprint. But I have a sneaking suspicion that we will look back at early 2026 and kind of wish we had just stopped. Of course, this won't happen writ large. There is global geopolitical competition, and there are stunning amounts of money to be made. We have the option to make wise, thoughtful choices about how we treat employees -- you know, the people who actually make up a thriving economy by having jobs and disposable incomes to buy things. The leaders with the power to make the call about how people are cared for will land on their feet either way. The ones just entering the workforce -- my son's generation -- may not have that luxury.

The AI blindspot: Layoffs are piling up, but where are the returns?

Companies are cutting jobs to fund AI, but this strategy may not work. A global survey shows that reducing staff does not guarantee better returns on AI investments. Instead, businesses that invest in their people to guide and scale AI systems see greater financial success. The future of work involves humans and AI collaborating, creating new opportunities. Corporate boardrooms are currently swept up in a massive wave of layoffs as executives scramble to offset their expensive investments in artificial intelligence (AI). The latest are tech giant Meta and banking heavyweight Standard Chartered which have announced fresh rounds of job cuts aimed at tightening operations in this new automation era. However, a major global survey by the technology research firm Gartner reveals that the corporate rush to fire workers can be a misplaced strategic move. According to their data, cutting staff might temporarily free up cash in a budget, but it completely fails to deliver actual financial returns on AI investments. This growing contradiction shows that real business value comes from magnifying what human workers can do rather than getting rid of them entirely. The Gartner warning: Why firing staff might fail to fuel AI profits The Gartner survey sends a clear warning to corporate leaders who look at staff cuts as a shortcut to tech profitability. The core message of the report is that autonomous business and AI layoffs may not actually deliver returns. Instead of eliminating positions, Gartner advises that organisations should invest heavily in the skills, roles and operating structures that let people guide, govern, expand and transition to autonomous capabilities. Also Read | Meta lays out plans for May 20 layoffs, restructuring; closes 6,000 open roles The data highlights a massive disconnect between cutting headcount and making money. Among organizations that are currently piloting or deploying autonomous business capabilities, approximately 80% percent reported workforce reductions. Yet, these reductions do not appear to translate into a better return on investment. In fact, the survey found that workforce reduction rates were nearly equal among respondents reporting higher financial returns from autonomous technologies and those experiencing only modest gains or even negative outcomes. To map out these trends, Gartner surveyed 350 global business executives in the third quarter of 2025 to understand the current state of autonomous business at enterprises. The study focused strictly on large corporations, meaning every qualifying organisation reported an enterprise wide annual revenue of at least $1 billion or the equivalent. Additionally, these companies had already been piloting or had fully deployed at least one of three major advancements, which included AI agents, intelligent automation or autonomous technologies. When businesses deploy tools like AI agents, intelligent automation, robotic process automation, digital twins and tokenized assets, they are trying to push their operations into true autonomy. This moves a company far beyond simple everyday automation. In a fully autonomous setup, both machines and people operate with a much higher level of independence. The analysts emphasise that this shift does not mean human-less business, but rather it means human-amplified business. Also Read | Standard Chartered plans to cut thousands of jobs in AI push "Many CEOs turn to layoffs to demonstrate quick AI returns; however, this disposition is misplaced," said Helen Poitevin, Distinguished VP Analyst at Gartner. "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." The study notes that autonomous business will create more work for humans over the long term. This momentum is set to accelerate because corporate spending on artificial intelligence agent software is absolutely skyrocketing. Gartner forecasts that spending on this software will reach $206.5 billion dollars in 2026 and jump to $376.3 billion in 2027, which is a massive leap from the $86.4 billion spent in 2025. Because autonomy will increase for both software and humans, the broad institutional need for actual people will go up instead of down. As a result, Gartner predicts that autonomous business will become a net-positive job creator by 2028 to 2029, a turnaround driven entirely by new forms of work that artificial intelligence simply cannot absorb. Helen Poitevin summarised the deep structural realities that will keep human talent at the very center of the modern enterprise. She noted: "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." Facing the reality of the J-curve The Gartner study finds an echo in another recent study published by the Stanford Digital Economy Lab. The report titled 'The Enterprise AI Playbook' looks closely at what happens when large companies try to put automation to work. By tracking real corporate outcomes, the Stanford researchers explain why the quick-fix layoffs fail to generate real profits. A central takeaway from the Stanford playbook is a concept known as the productivity J-curve. This economic principle explains that when a company adopts a powerful new technology, its overall performance and profits usually drop first before they shoot upward. This initial dip happens because true technological transformation requires massive, invisible investments. Companies cannot just buy software, they have to spend heavily on reshaping their daily workflows, rewriting corporate handbooks and retraining their staff to use the new tools effectively. Because traditional corporate accounting fails to measure these hidden organisational costs, executives often miscalculate how long it takes to see a real financial return. The Stanford study shows that if a company fires workers without completely fixing and redesigning its internal processes, the new AI tools simply cannot scale. The highest financial returns happen when companies stop trying to replace human workers and instead build models where software handles standard tasks while humans are specifically trained to manage complex exceptions and oversee the systems. The job market resists the AI shock While individual corporate leaders make headlines by cutting staff to fund their tech budgets, broader economic data in the US shows that these layoffs are not destroying the wider job market. In a research note published in March -- 'AI Adoption and Firms' Job-Posting Behavior' -- economists at the Federal Reserve looked at the direct relationship between corporate automation and overall hiring trends. Using millions of real-world job advertisements, the central bank analysed whether companies using heavy automation were actually closing their doors to human workers. The findings from the Federal Reserve offer a reassuring reality check that aligns with Gartner's optimistic long-term forecast. The study states clearly that there is no evidence of an overall drop in job postings within industries or firms that show high levels of AI adoption. While specific, highly repetitive jobs are certainly feeling the pressure of automation, forward-looking employers are balancing out these losses. Instead of shrinking their total number of employees, automated companies are dynamically shifting their hiring priorities. They are pulling back on routine data-entry roles and actively looking for new staff to handle strategy, system oversight and human-centric problem solving. The Federal Reserve emphasises that the job market is not shrinking under the weight of new technology, it is simply rewriting the rules of who it needs to hire. The human-amplified future of enterprise value When you connect the dots between the insights from Gartner, the Stanford Digital Economy Lab and the Federal Reserve, the narrative around corporate automation changes completely. AI is not a simple cost-cutting tool designed to replace a human workforce. Executives who treat their employees as disposable liabilities to show quick quarterly returns are actively damaging their own long-term profitability. The data across all of these recent studies proves that the most successful and profitable corporations are those that use new technology to upgrade, rather than replace, their human talent. By looking past immediate budget pressures and investing heavily in a human-amplified operating model, businesses can successfully survive the initial challenges of adoption and build a lasting foundation for financial growth.