• Automation of tasks performed by labor is a more common motivator for firms adopting AI and robots than other advanced technologies (Figure 1).
• Around 30% of all workers are potentially exposed to advanced technologies used for automation purposes, with manufacturing workers much more exposed than other workers (Figure 2).
• Demand for worker skills appears to be much more positively affected by technology use than overall employment levels, but those effects vary by technology, with ambiguous employment effects of robot use and more positive employment effects for AI (Figure 3). 

We start by looking at why firms adopt advanced technologies. Figure 1 shows the share of workers at technology-using firms by adoption motivation. Improving the quality of production processes is the most common motivation reported by firms accounting for 68% to 80% of workers at technology using firms. As compared to other technologies, AI and robots have the highest share citing automation as a motivation (applicable to technology-using firms that account for 54% and 66% of workers, respectively). Since technology-using firms can choose more than one motivation, the shares of workers at firms that reported improving quality and upgrading methods but not automation or the introduction of new goods are shown separately; technologies other than AI and robotics are more apt to cite these motivations. 

We next turn to broad measures of how many workers are exposed to advanced technologies. Figure 2 shows the shares of workers in firms that adopted advanced technologies to automate production tasks. Note that Figure 2 reports “potential exposure” of workers to advanced automating technologies; exposures should be interpreted as upper bounds to the actual share of workers potentially affected by advanced technology adoption and use. In the manufacturing sector there is more exposure to any automating technologies than in the rest of the economy (52% of workers compared to 28%). AI and robots are associated with much more worker automation exposure in manufacturing than in nonmanufacturing, in part because robots are more widely adopted in manufacturing than in other sectors, and manufacturing is among the sectors with the highest AI adoption rates. However, it is noteworthy that worker exposure to automation is about the same in manufacturing firms using dedicated (nonrobotic) equipment as it is in manufacturers using robots. Furthermore, dedicated (nonrobotic) equipment, specialized software (other than AI), and cloud computing are also associated with much greater worker automation exposure in manufacturing than they are in nonmanufacturing. Next, we turn to how technology affects demand for workers.

Figure 3 shows the self-reported impact of advanced technology adoption on firm-level demand for workers and skills. The shares of workers at technology-using firms that attribute decreases in employment to technology adoption are generally outweighed by the shares of workers at such firms reporting increases; and 67% to 78% of firms report no change in employment due to technology adoption (this is the difference between 100% and the shares denoted by the triangles in the figure). AI-using firms are more likely than other technology users to report increases in employment due to the technology. In contrast, the share of firms reporting negative effects of robot use on employment is nearly the same as the share reporting positive effects. In terms of skill requirements, the share of workers at firms reporting that technology adoption increased demand for skills far outweighs shares of workers at such firms reporting a decrease in demand for skills; this finding is especially true for AI-using firms. However, respondents may be averse to reporting employment reductions even in a confidential survey. Note also that we do not have quantitative measures of the extent of employment change because of using a given technology. We measure only the direction of change at the firm level.

This research shows that while the potential exposure of workers to automation technologies is large, the reported direction of change in employment levels of firms using technologies for automation is generally more positive than negative (except for robotics), while the reported direction of impact on skills is overwhelmingly positive, suggesting that technology-using firms increase their demand for skilled labor. However, the paper that is the basis for this blog also shows that adoption of advanced technologies is associated with higher labor productivity. Thus, the impact on employment in nonadopting competitors — through reallocation of production to higher-productivity adopters — may be large enough to cause negative and significant longer-term impacts of advanced technology use on overall employment. Further research is needed to assess this possibility.

Finally, given the recent acceleration in AI advances, the Census Bureau will add questions to the newly expanded biweekly Business Trends and Outlook Survey (BTOS). Starting Dec. 4, 2023 (for one cycle), BTOS will collect near real-time information about firms’ current and prospective uses of AI and their implications for automation.