This article is grounded in the latest scientific research on how AI affects motivation and performance. Research insights are from institutions including Harvard Business School, Boston University, USC Marshall School of Business, and the University of Pittsburgh.
With new AI continually being introduced into the workplace, people can feel uncertain about their jobs, and that anxiety can harm employee motivation.
“AI can hurt employee motivation, and it tends to be invisible to managers until performance is impacted,” says Narayan Ramasubbu, professor at the University of Pittsburgh.
The ServiceNow Enterprise AI Maturity Index 2026 found that 47% of employees believe their jobs will become less necessary as AI evolves. It doesn’t have to be this way.
According to research led by Macau University, it’s not AI itself that triggers uncertainty, but rather how it’s presented. The study revealed that organizations that invested in helping their people understand and use AI shifted their workforces toward engagement. Those that didn’t created the conditions for anxiety.
Consider two teams given the same AI coding assistant. On the team that trains and encourages sharing what works, developers take on more complex projects and grow their skills faster. On the team left to figure it out alone, developers feel threatened, avoid the tools, and disengage.
“Leaders assume they're deploying technology, but their workforce is absorbing it as reorganization,” says Spencer Beemiller, an innovation officer for the Americas at ServiceNow. “The gap between those two realities is where motivation collapses, and fixing it has almost nothing to do with the product itself. It has to do with whether you bring your workforce into the redesign or impose it from above."
A study of 758 employees led by Harvard Business School found that when AI was used as an aid (e.g., brainstorming, drafting copy), employees completed 12.2% more tasks, worked up to 27.6% faster, and produced up to 33.9% higher-quality outputs.
However, when AI was used as a replacement for human judgment (e.g., strategy and complex creative analysis), AI reduced the correctness of work by 19%.
“Letting AI take over more and more tasks carries the risk of devaluing those tasks that previously gave a sense of meaning to employees,” says Cheryl Wakslak, associate professor of management and organization at the USC Marshall School of Business.
Most organizations ignore this distinction and add AI on top of existing workflows. Only 5% of them have consciously redesigned work around AI, according to the Enterprise AI Maturity Index. The latter can help reduce risk.
A Boston University study of 1,261 managers found that framing work as coming from an "AI employee" rather than an "AI tool" negatively affected motivation and quality. Managers caught 18% fewer errors when reviewing AI-generated documents.
“We found that managers overseeing work that came from an ‘AI employee’ as opposed to an ’AI tool’ felt less personal accountability toward their work,” explains Emma Wiles, an assistant professor at Boston University and co-author of this research.
More than two-thirds (67%) of employees believe AI enables them to focus on higher-value work, according to the Enterprise AI Maturity Index.
Research from Stanford and MIT explains why that thinking has substance. It found that access to a generative AI tool increased productivity by 14% on average. For novice and lower-skilled workers, gains reached up to 34%.
The perceived impact of people’s work increases when AI handles repetitive tasks and humans handle judgment, interpretation, and customer interaction.
“When AI allows employees to do things that they could not attempt before, it becomes incredibly empowering,” says USC Marshall School of Business’ Wakslak.
Research shows that the same mechanisms that enable lower-skilled workers to gain from AI can also put top performers’ motivation at risk.
“AI trains on people whose skills it can copy and equalizes know-how,” explains the University of Pittsburgh’s Ramasubbu, co-author of this research. “When the system learns from top performers and hands their playbook to everyone else, the star performers lose the edge that made them stars, and their motivation goes with it.”
The good news is that managers can prevent this by changing how they reward top performers. As AI takes on more of the execution, the work that distinguishes a top performer shifts toward judgment: editing AI outputs, catching errors, and knowing when it’s gotten something wrong.
A manager who recognizes the person who flagged a flawed AI result, rather than the one who simply completed the most work, signals that this judgment is what the organization values. Everyone gains when AI is integrated in a way that both elevates lower-skilled workers and motivates top-performing employees.
To help ensure AI enhances rather than hampers motivation, organizations can take three key steps:
Before scaling AI, study where it adds value and where it gets in the way. Then determine what the work should look like when humans and AI share it.
Design systems that enable top performers to focus on what creates their competitive advantage. “Organizations need to reward people for what they teach the machine, not just what they produce,” Ramasubbu advises.
Humans should be held accountable for the work, even when AI does most of it. Avoid framing AI as an employee. Build review steps into each workflow, clarify who signs off, and hold that person to the same standard of quality they would apply to their own work.
Boston University’s Wiles cautions that “employees should know clearly that AI is a tool they use, not a mysterious agent imposed from the organization top down.”
When AI gets new capabilities every quarter and the workforce gets infrequent training, employees draw their own conclusions. Invest in people at the same rate you invest in technology. Train them for new skills, adapt their roles, and recognize their achievements.
“If employees don’t know how the system can work for them, they won’t fight it,” says Bhavin Shah, senior vice president and general manager of Moveworks and AI at ServiceNow. “They’ll just quietly stop reaching for it. And that’s the adoption gap that never shows up in your metrics.”
Find out how ServiceNow can help you put AI to work to improve employee motivation.
