AI Expectations and Outcomes
TL;DR
Whether businesses’ expectations about their use of AI have translated into the desired outcomes is studied to find some evidence that stated motivations for using AI are linked to related changes in production processes; importantly, use cases for AI are associated with increased intensity of R&D use.
Abstract
Evaluating the accuracy of predictions about AI use and outcomes is important given their potential influence on policy and decision making. In this paper, we study whether businesses’ expectations about their use of AI have translated into the desired outcomes. First, we use the U.S. Census Bureau’s Business and Trends Outlook Survey for 2023–2026 to compare predictions of AI use with actual AI use to see how well businesses have been anticipating their true rate of AI adoption. We find a learning curve: AI adoption initially happened slower than expected, was followed by a short period of growth that was faster than expected, and more recently has been close to expected rates. Next, we see if the motivations for adopting AI translated to associated outcomes by combining data about the motivations of early AI adopters from the Census Bureau’s 2019 Annual Business Survey with detailed data on output, inputs, and total factor productivity from the BEA-BLS Integrated Industry-Level Production Account. We find some evidence that stated motivations for using AI are linked to related changes in production processes; importantly, use cases for AI are associated with increased intensity of R&D use. This suggests that even if the link between motivations and outcomes is murky at this point, structural change may be in the planning process but not yet observed in the outcome data.