Business leaders are struggling to understand how seriously they should take the latest phenomenon in the world of artificial intelligence: generative AI. On one hand, it has already displayed a breathtaking ability to create new content such as music, speech, text, images, and video and is currently used, for instance, to write software, to transcribe physicians’ interactions with their patients, and to allow people to converse with a customer-relationship-management system. On the other hand, it is far from perfect: It sometimes produces distorted or entirely fabricated output and can be oblivious to privacy and copyright concerns.

Is generative AI’s importance overblown? Are its risks worth the potential rewards? How can companies figure out where best to apply it? What should their first steps be? To provide guidance, this article draws on our research comprising studies of specific generative-AI projects and broad analyses of how the technology will affect tasks and jobs throughout the economy.

A large enterprise-software company that one of us (Erik) studied along with Lindsey Raymond and Danielle Li of MIT illustrates that there are ways to both reap the benefits of generative AI and contain its risks. The company’s customer-service agents, who assist people via online chats, faced a common challenge: New hires needed several months to get up to speed on how to answer technical questions and deal with confused customers, but many quit before they became proficient. The company saw generative AI as a solution. It engaged Cresta (which Erik has been advising), a generative AI start-up, to implement two kinds of artificial intelligence. The first was a large language model (LLM)—designed to understand and respond to humans in their own words—that “listened in” on the chats. It was fine-tuned to recognize phrases that led to good customer-service outcomes in various situations. But because of the risk of confabulations, or plausible-sounding but incorrect responses, the system also used a machine-learning technique called in-context learning, which drew answers from relevant user manuals and documents.

The LLM monitored the online chats for specific phrases, and when one of them occurred, it based its responses on the information in the in-context learning system. As an additional safeguard, it didn’t respond to queries directly. Instead human agents were free to apply their common sense in deciding whether to use or ignore the LLM’s suggestions.

After a seven-week pilot the system was rolled out to more than 1,500 agents. Within two months multiple benefits appeared. Both the average number of issues resolved per hour and the number of chats an agent could handle simultaneously increased by almost 15%; the average chat time decreased by nearly 10%; and an analysis of the chat logs showed that immediately after the new system was implemented, customer satisfaction improved. Expressions of frustration declined, for example, as did TYPING IN ALL CAPS.

It’s especially interesting that the least-skilled agents, who were also often the newest, benefited most. For example, resolutions per hour by agents who had been among the slowest 20% before introduction of the new system increased by 35%. (The resolution rate of the fastest 20% didn’t change.) The generative AI system was a fast-acting upskilling technology. It made available to all agents knowledge that had previously come only with experience or training. What’s more, agent turnover fell, especially among those with less than six months of experience—perhaps because people are more likely to stick around when they have powerful tools to help them do their jobs better.

Given the potential of generative AI to improve productivity in many other functions—indeed, any that involve cognitive tasks—calling it revolutionary is no hyperbole. Business leaders should view it as a general-purpose technology akin to electricity, the steam engine, and the internet. But although the full potential of those other technologies took decades to be realized, generative AI’s impact on performance and competition throughout the economy will be clear in just a few years.

That’s because general-purpose technologies of the past required a great deal of complementary physical infrastructure (power lines, new kinds of motors and appliances, redesigned factories, and so on) along with new skills and business processes. That’s not the case with generative AI. Much of the necessary infrastructure is already in place: The cloud, software-as-a-service, application programming interfaces, app stores, and other advances keep lowering the amount of time, effort, expertise, and expense needed to acquire and start using new information systems. As a result, it keeps getting easier for companies to deploy just about any digital technology. That’s a big reason ChatGPT went from zero to 100 million users in 60 days. As Microsoft, Google, and other technology providers incorporate generative AI tools in their office suites, email clients, and other applications, billions of users will speedily gain access as part of their daily routine.

Generative AI will also deploy quickly because people interact with these systems by talking to them much as they would to another person. That lowers the barriers to entry for some kinds of work (imagine writing software by explaining to an LLM in everyday speech what you want to accomplish). In addition, these systems won’t necessarily require companies to change entire business processes; at first they will be used for discrete tasks only, which will make them much easier to adopt. Using technology to reengineer every aspect of how a company interacts with its customers, for example, is a major undertaking; using it to suggest better chat responses to customer service agents is not. Over time, however, generative AI will bring large and deep changes in how companies do their most important work.

Consequently, business leaders shouldn’t sit on the sidelines and wait to see how the use of generative AI develops. They can’t afford to let competitors steal a march on them.