The emergence of generative artificial intelligence (AI) has attracted significant attention, but
there have been few studies of its economic impact. While various generative AI tools have performed well in laboratory settings, excitement about their potential has been tempered by concerns that these tools are prone to “hallucination,” and may produce coherent-sounding but inaccurate information (Peng et al., 2023a; Roose, 2023).
In this paper, we study the adoption of a generative AI tool that provides conversational guidance
for customer support agents. This is, to our knowledge, the first study of the impact of generative
AI when deployed at scale in the workplace. We find that access to AI assistance increases the
productivity of agents by 14 percent, as measured by the number of customer issues they are able to
resolve per hour. In contrast to studies of prior waves of computerization, we find that these gains
accrue disproportionately to less-experienced and lower-skill workers.We argue that this occurs because ML systems work by capturing and disseminating the patterns of behavior that characterize
the most productive agents. Computers and software have transformed the economy with their ability to perform certain tasks with far more precision, speed, and consistency than humans. To be effective, these systems
typically require explicit and detailed instructions for how to transform inputs into outputs: when
engineers write code to perform a task, they are codifying that task. Yet because many production
processes rely on tacit knowledge, these processes have so far defied automation (Polanyi, 1966; Autor, 2014).