Want Employees to Embrace AI? Stop Selling It as a Productivity Tool

Adoption of generative AI tools has been swift. In 2023, 55% of organizations worldwide reported using AI. Two years later, that figure hit 88%. Yet despite this rapid growth — and the sizable investment it represents — employee feedback on the technology has been mixed.

“How these tools are framed for employees when they’re rolled out is an important part of this story,” says Arvind Karunakaran, an assistant professor in the School of Engineering at Stanford and a faculty affiliate at the Stanford Institute for Human-Centered AI (HAI). “Even more important is whether managers follow through on the claims that they make. If they say AI is for job enrichment, then are they committing resources to make this true?”

Karunakaran, together with Roshni Raveendhran of the University of Virginia and Tami Kim of Dartmouth College, studied AI deployments at a law firm, an ad agency, and an IT services firm to better understand what drives employee adoption. They found a consistent pattern: When AI was framed as a productivity tool, employees tended not to engage with it. However, when it was presented as a way to enrich their jobs, workers were motivated to figure out how best to use it — assuming they were given the time and opportunity to explore.

Making Work More Meaningful, Not Just Efficient

The researchers conducted two workplace studies. In the first, they interviewed 184 employees at the three companies that had adopted generative AI tools. Across administrative, creative, and technical roles, employees who were required to use AI described feeling that their work had been stripped of meaning. That loss, in turn, sapped their motivation to learn new skills. “I’m hesitant to learn new skills related to my job,” one explained, “because it feels like whatever I learn today will be outdated by next month.”

The most revealing evidence emerged from the second study, in which the team spent nearly two years at the law firm observing two divisions that were virtually identical in practice area, size, and pay and promotion structure. Administrators granted both groups the same firm-wide license for an AI tool — dubbed “LawBot” by the researchers — designed to help draft contracts and nondisclosure agreements. The critical difference between the divisions was how the managers introduced the tool.

In the first division, the manager pitched LawBot to paralegals as a way to draft documents faster and shorten turnaround times. After using the tool, paralegals reported feeling like “just another cog in the machine” and used it only for existing tasks rather than exploring novel applications.

In the second division, the manager asked paralegals: Which tasks do you find most boring, and what have you always wanted to do but never had time for? Could LawBot help take care of the routine tasks and free up time for more interesting work? Given time to play around with LawBot, the paralegals began using it for legal research and case analysis, exchanged tips about what worked, and lobbied to attend legal strategy meetings with the attorneys. Paralegals in this division used LawBot 58% more than their peers in the other division and experimented with it 70% more.

“When AI tools are introduced, managers are often obsessed with productivity gains,” Karunakaran says. “But if you aren’t also working to answer how people’s jobs are going to improve or be enriched, the initiative will most likely backfire.”

How managers frame the reason for introducing AI plays a decisive role in its successful deployment, but it’s not the only factor. Managers in the second division offered the paralegals formal training on LawBot, weekly knowledge-sharing lunches, Q&A sessions with the vendor, and informal mentorship on more complex tasks, such as legal research. In addition, time each week was set aside for paralegals to explore and learn about the tool on their own.

“You need to put this scaffolding in place to make the commitment feel credible,” Karunakaran says. “Without that, employees recognize it as little more than cheap talk.”

Measuring Value, Not Volume

As AI reshapes job responsibilities, the way employees are evaluated must also change. Paralegals, Karunakaran notes, have traditionally been judged by their annual or quarterly case volume.

“But that was not the right metric for the new type of work they were doing with AI,” he says. Paralegals who used the technology to conduct in-depth legal research and case analysis produced high-value work, but it was also more time-consuming, which meant they took on fewer cases. “So, the metrics need to be realigned to suit the new job profile and task portfolio.”

The wrong metric can also trigger counterproductive behavior. Some companies track employee AI token consumption, or the amount of text processed or generated, and equate heavy use with value. But that strategy encourages workers to use AI for its own sake — generating cat memes to hit a quota, as Karunakaran puts it — rather than deploying it for legitimate business objectives. 

The Best Jobs for AI

One more factor contributing to successful AI adoption is whether a particular job consists of strongly or weakly bundled tasks.

Sales roles, for example, are made up of strongly bundled tasks like researching customers, building relationships, negotiating, and closing deals. “You can’t randomly unbundle and rebundle these,” Karunakaran says. A company might deploy AI for customer research, but the salesperson remains involved throughout the process. “The salesperson still needs to understand the customer and be there to close the deal, so it’s a strongly bundled job.”

Conversely, jobs consisting of weakly bundled tasks — such as paralegal work or medical transcription, which includes disparate tasks related to document management — may be more vulnerable to AI automation, but their modular nature makes adoption far easier.

Regardless of how far or fast AI is deployed, its success in the workplace will hinge less on the technology itself and more on the reason for its deployment, the metrics used to reflect real value, and the time and resources afforded employees to experiment and engage with it in meaningful ways.

“Though the promise of AI is that it will take care of routine tasks and free people up for higher-level work, what often happens is that managers see the time savings and raise expectations for how much of the same work people should do,” Karunakaran says. “If managers want AI to enrich people’s jobs and create organizational value, they have to create room for employees to learn, take on new responsibilities, and be recognized and rewarded for the value of those contributions. Realizing this possibility takes a commitment to developing people alongside a commitment to adapting the technology.”