As HR leaders grapple with how best to incorporate ChatGPT and other generative artificial-intelligence tools into their workplaces, two new pieces of research illuminate potential use cases.
The first, a National Bureau of Economic Research (NBER) working paper published in April, found that using generative AI made call-center employees an average of 14% more productive. Lower-skilled and newer workers received a 35% productivity boost—likely, the authors posited, because the tool “work[s] by capturing and disseminating the patterns of behavior that characterize the most productive agents.”
The second, a March study of telemarketing employees in the Academy of Management Journal, highlighted a similar skills-based divide: The researchers found that AI can enhance the creativity of higher-skilled workers—in this case, helping them to come up with better answers to difficult customer questions— but lower-skilled workers in their study ended up feeling demoralized when using the same tool. Both groups’ effects were caused by the same phenomenon: AI automated the more routine tasks, making the more challenging tasks that required creative problem-solving a bigger part of the job.
For insights on what leaders can glean from these studies for formulating their own workplace AI plans, we spoke with a co-author of each: From the productivity study, Lindsey Raymond, a researcher and PhD candidate at MIT Sloan’s Technological Innovation, Growth, and Strategic Management Group; and from the creativity study, Nan Jia, an associate professor of business administration at the University of Southern California’s Marshall School of Business. Based on our conversations, here are five potential use cases and related considerations for generative AI:
Relieve some of the burden of middle managers.
Employees at this level are notoriously burned out at higher rates than either individual contributors or more senior leaders. One way to help them streamline their workloads: Have managers delegate some knowledge transmission to AI, relying on it as a messenger that can provide employees with simple coaching.
For example, rather than walking a new hire through what needs to go into a memo, a manager might encourage them to use AI to draft a memo using the internal knowledge the system has absorbed from other employees doing similar work. “Think about this as substituting for some of the human manager's activities—maybe they can manage larger teams, maybe they can spend time doing other sorts of things besides training or coaching their workers,” Raymond says.
Especially as a cooling economy pushes organizations to focus on doing more with less, equipping managers with another way to share high-quality information can help buffer them against feelings of overwhelm.
Increase the depth and quality of development conversations.
“If you get a tool that gets you up to speed faster, that means your manager can spend less time helping you learn,” Raymond says—which, in turn, means managers can spend more time in 1:1s covering more human-touch topics, such as having deeper, more goal-focused discussions with employees about how to support their growth.
Make onboarding more comprehensive.
Raymond cites organizational writing style as a type of knowledge that less seasoned employees often learn by doing: “Often as a new worker, you're kind of like, ‘I guess I need to figure it out as I get feedback from people.’ And probably the first time you do it, you're much worse than the second time,” she says. “Those are places where an AI could generate that first draft for you that encompasses the right tone, the right style, the right format, and then you just have to tweak on top of it.”
To speed up new employees’ familiarity with organizational voice, especially for roles that require heavy written output, consider having them use a chatbot to generate a first draft for discussion as part of the onboarding process.
Adjust your reward and recognition structures.
In the call-center context of the NBER study, “the high-skilled workers are generating what is essentially intellectual property about what works when you talk to customers, what works in terms of solving technical problems,” Raymond notes. Those high-skill workers are indirectly improving their colleagues’ ability by teaching AI to think like them, but they “aren’t being compensated for what is a positive externality on everyone else in the firm,” she adds.
If AI is leveling the skills playing field, managers and organizational leaders should determine how best to identify the high performers improving the tool. Until that new identification system is in place, consider making changes to the way excellence is recognized to be more team-based versus individually based.
Pair specific uses with specific employees.
“Employees can benefit from AI's help, but in very different ways,” Jia says. For example, in the context of her study, “If your hope is to use AI to make your workplace more creative, then you give AI to top-tier employees and you give them tougher customers,” while lower-skilled employees in the same setting might benefit from using AI to move more quickly through easier calls.
Equip managers to guide each of their reports through the most effective ways that person can use generative AI in their work, and avoid issuing blanket directives about how AI tools should be used: “It's not just, ‘Oh, I'm going to introduce this technology into my organization,’” Jia counsels. “The match is really important.”
Key takeaways:
Use generative AI tools to upskill your lower-skilled employees by enabling them to learn from the work of their higher-skilled peers.
AI can relieve some of the burden on middle managers and allow for more in-depth development conversations with reports.
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