AI-driven layoffs aren’t making business sense

A huge majority of large enterprises have laid off employees after rolling out AI initiatives, but reducing headcount doesn’t lead to the ROI executives may expect.

Eighty percent of large enterprises surveyed by Gartner have reported workforce reductions after launching automation projects, with the average reduction between 1% and 15%. The IT analyst firm, however, has found no correlation between layoffs and AI ROI.

Enterprises reporting significant ROI from automation initiatives have laid off workers at a similar pace as enterprises reporting modest ROI gains, or negative ROI, from automation initiatives, indicating that layoffs and returns aren’t connected, says Helen Poitevin, digital workplace analyst at Gartner.

Laying off employees may lead to short-term profit gains, but the late 2025 survey of business executives at large enterprises goes against a common perception that workforce reductions driven by AI-enabled productivity leads to ROI, she notes.

“You would anticipate that those who are getting the most ROI maybe then are able to cut the most, but that’s not what we see,” she adds. “There seems to be no link between laying people off and getting ROI from AI investments.”

Investing in training

Instead, ROI for AI projects is driven more by reinvestments in the workforce, rather than replacing employees with automation, Poitevin says. Enterprises achieving the most ROI from AI have been training their employees on how to use it, she adds.

“They’re investing in upscaling people to be able to actually build their own agents or their own automations to get things done,” she says. “They’re enabling people to do some innovation on their own.”

Over the long term, Gartner predicts that AI will create more jobs than it replaces, but in the meantime, there will be job chaos, with AI significantly transforming 32 million jobs a year.

Smart companies will turn to their current employees to fill new AI-related roles, Poitevin says. Enterprises leading in the ROI race have already begun to create new AI-related roles for current employees, for example, to orchestrate AI agents.

“They were looking at skills and being able to set up the types of career paths for those who would be impacted by AI into other areas,” she adds. “It is a role in itself to orchestrate all that work and ensure that the workflows that agents are taking on are done appropriately.”

Poitevin does acknowledge that many leading IT companies have announced AI-connected layoffs in recent months, potentially raising questions about Gartner’s conclusions. But in most cases, layoffs at IT companies have been more related to major shifts in corporate strategies and spending to focus on AI as a product or foundational technology, rather than replacing employees with AI, she notes.

For companies outside the IT industry, replacing workers with AI doesn’t lead to the results executives may expect, Poitevin says. Gartner recommends instead amplifying their workers by investing in skills and roles that allow humans to scale autonomous systems.

“My direct advice would be do not use AI as an excuse, especially if you want to get AI value,” she adds. “People fear that AI could really do their whole job a lot less than they fear that their CEO thinks AI could do their full job.”

Brian Behe, CTO of AI-focused cybersecurity vendor RIIG Technology, agrees that investing in employees is a better strategy than laying them off.

“The organizations getting real returns are the ones that took the people who understood their business deeply and gave them AI tools to do more with that knowledge,” he says. “The ones that cut first and automated second are now discovering that the institutional knowledge they eliminated was exactly what the AI needed to work properly. You cannot automate expertise you no longer have.”

Build it better

Many companies have used layoffs as an easy way to justify AI spending, when they should have focused instead on building better AI systems, Behe adds.

“Organizations treat workforce reduction as proof of AI progress, when it is actually a signal that they have skipped the hard part,” he says. “Cutting headcount is easy to measure. Building the operating model that makes AI actually deliver value is not.”

Many organizations have confused the workforce reduction itself with AI value, he adds. “Layoffs are being used as a proxy for progress,” he says. “They’re not.”

Some recent layoff announcements connected to AI appear to be misplacing the blame for over-hiring in previous years, notes Andy Williamson, CEO and director of AI strategy for IT education vendor ONLC Training. In some cases, companies are hiring again after layoffs because they misjudged the power of AI to replace employees, he adds.

Layoffs can reduce short-term costs but ultimately are shortsighted, Williamson adds. “Talent and experience are among the largest assets an organization has,” he says. “Leadership that understands what AI can do today can find expansion opportunities that might have been too difficult without the aid of AI. Without this insight, there’s a real risk of losing talent that could shape future product innovations.”

Instead of layoffs, most organizations should focus on an “automate-to-augment” strategy, Williamson recommends.

“Most of the organizations we work with have very busy workforces, and automating low-risk routine tasks frees up time,” he says. “The strategic question is what comes next: How do you put your people to their best use, either to deliver higher levels of service to existing customers or by expanding into markets the organization couldn’t pursue before?”

Many companies also ignore the human costs of layoffs, he adds. In February, IT and financial services company Block announced layoffs of 40% of its employees to refocus on AI, after projecting gross profits of nearly $12 billion for 2026, he notes.

“While people on social media noted how magnanimous Block was in giving the fired employees an extra week on Slack to say goodbye to their peers, very little was said about how unnecessary the move was, or about how it was done,” he says. “Who at that decision table was talking about the human cost of firing 4,000 people? I don’t think we’re talking enough about the human impact of these decisions as we move into AI adoption.”