By Adam Mills
AI Editor
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Employees are saving time with artificial intelligence. That doesn't mean companies understand what those efficiencies are costing them.
"Pulse of Work in 2026," a new study from GoTo, a business communications and IT software company, and Workplace Intelligence, a workplace research and thought leadership firm, found that employees using AI save an average of 2.3 hours a day. But the same research also surfaced a less comfortable finding: half of employees say they rely too much on AI, while 39 percent say relying on it is making them less intelligent.
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Rich Veldran, CEO of GoTo, sees that combination as a sign workplace AI has entered a new phase.
“We’ve reached an inflection point with AI adoption,” he told Newsweek. “A year ago, everyone was using AI, but they weren’t quite getting real productivity gains. That’s changed now.”
The time savings are arriving with risks companies may not be prepared to manage.
“The productivity gains are real and measurable, but there’s real concern that when not used properly, AI is also eroding skills, judgment and accountability,” Veldran said. “Companies need to understand that productivity and capability are not the same thing. You can get faster without getting better.”
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That distinction is becoming harder for companies to ignore. Many businesses have encouraged employees to adopt AI in the name of speed and efficiency. But the findings suggest faster work may carry hidden costs if employees begin outsourcing too much thinking to systems they do not fully understand or verify.
Dan Schawbel, managing partner of Workplace Intelligence, described the findings as a warning about a less visible workplace cost.
“Productivity gains are real, but they’re arriving alongside a quieter crisis of confidence that most leaders aren’t tracking,” Schawbel told Newsweek. “The gap between AI’s visible outputs and its invisible psychological costs is the story organizations are missing entirely.”
That issue is especially pronounced for younger workers. According to the report, 46 percent of Gen Z employees say AI is eroding their skills and making them less intelligent, while 50 percent believe that relying on AI will hurt their long-term career prospects and 35 percent think AI is doing their job better than they can.
The findings are based on a global survey of 2,500 people, including 1,250 full-time knowledge workers and 1,250 IT decision-makers, conducted between November 2025 and January 2026.
Employers should take those numbers seriously, Veldran said, because early-career employees are still building the judgment, context and problem-solving skills that become the foundation for more senior work.
“If AI is short-circuiting that development process, we’re creating a generation of workers who may struggle with the higher-order thinking that AI can’t replicate,” Veldran said.
Schawbel framed the risk in similar terms. Early-career workers, he said, are developing the habits and judgment they will need later “at the exact moment AI is making it easier to skip that building process entirely.”
Many employees are using AI under pressure, with uneven guidance and unclear accountability. The research found that 60 percent of employees feel pushed to use AI to boost productivity, including 76 percent of Gen Z employees.
“When people feel compelled to use AI but lack a clear framework for when or how, they default to using it to look productive rather than to actually be productive,” Schawbel said. “The result is a workplace full of AI-generated outputs that no one fully owns and everyone quietly doubts.”
The consequences are already showing up in the quality of work moving through organizations. The report found that 43 percent of employees have used AI-generated content even when they suspected it was low quality, contained errors or included fabricated information. An additional 20 percent said they might do so.
Veldran pointed to several reasons that can happen: expectations to move quickly, uncertainty about how to check AI outputs and a tendency to treat machine-generated answers as more reliable than they are. Some employees, he said, may also lack confidence in their ability to catch AI errors or may not want to start over after investing time in an AI-generated response.
Companies, he argued, need to make accuracy and quality as important as speed.
“Employees should be responsible for their own AI-generated content and reviewing it as opposed to offloading that to a colleague,” Veldran said.
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That review burden may be one of the clearest signs that AI productivity gains are being overstated. The report found that 59 percent of employees are responsible for reviewing AI-generated content created by coworkers or direct reports. Among those reviewers, 77 percent say AI-generated work takes more time to review than human work, while 66 percent say reviewing other people’s AI “workslop” creates additional work for them.
Leaders need to stop measuring AI productivity only at the individual level, Veldran said.
“If AI helps you create a document in five minutes instead of an hour, but then it takes your colleague 45 minutes to review and fix it instead of 15, where’s the net gain?” he said. “Leaders need to think about productivity at the system level, not just the individual level.”
The same burden can also affect collaboration and trust.
“AI-generated work that’s sloppy, generic or simply wrong doesn’t disappear—it lands on a colleague’s desk and becomes their problem,” Schawbel said. “This creates a hidden productivity tax that never appears in the ROI calculations organizations use to justify AI adoption, but shows up in eroded trust and quiet resentment between coworkers.”
AI is also complicating workplace accountability. The report found that 83 percent of employees worry they could be blamed or fired for an AI mistake that negatively affects their company. Nearly one-third said that fear has made them hesitate to use AI.
Companies need to build a culture of “informed accountability,” not fear-based compliance, Veldran said. Employees should be expected to use AI thoughtfully, apply judgment and verify outputs. But when something goes wrong after those practices are followed, the response should focus on learning rather than punishment.
“We need to separate honest mistakes made while following good practices from careless mistakes made by cutting corners,” Veldran said.
The alternative, Schawbel warned, is an “accountability vacuum.”
“When people fear being blamed for AI mistakes, the rational response is to distance themselves from AI decisions, which directly undermines any organizational push toward confident, productive AI use,” he said.
Adoption alone is not enough. Companies also have to make sure AI leaves employees more capable, not more dependent.
Veldran recommended practical policies, role-based training, clear guidance and measurement that goes beyond tool deployment or adoption rates. Companies should track whether AI is being used in the right contexts, whether work quality is improving and whether employees feel more confident and capable.
Schawbel said companies also need to protect the human skills that AI can quietly weaken.
“Employers need to treat skill development with the same urgency they brought to AI deployment,” he said, “or they’ll find themselves with highly automated organizations full of people who’ve forgotten how to think without a machine.”
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