The use of artificial intelligence (AI) in pricing strategies is increasing across a range of industries, according to recent research by economists at the Federal Reserve Bank of Kansas City and the Federal Reserve Bank of San Francisco. The study examines how firms are adopting AI-enabled algorithms for setting prices, using data from public job postings as an indicator of AI pricing adoption.
Researchers analyzed job postings provided by Lightcast, identifying positions that require both AI skills and a focus on pricing. This approach builds on previous methods developed by Acemoglu and others in 2022, who classified jobs as AI-related if they included keywords such as “machine learning” or “neural networks.” To verify their measure, the researchers compared it with firms that publicly disclose their use of AI pricing technologies.
The findings show that between 2010 and 2024, the share of jobs focused on AI pricing increased more than tenfold in the United States. At the same time, overall employment in traditional pricing roles declined by more than one-third. The trend indicates a shift toward automated and algorithmic approaches to price setting within firms.
While growth in general AI-related jobs has been concentrated mainly within sectors like information technology—which includes companies such as Uber and Amazon—the rise in AI pricing jobs has been distributed more broadly across industries. Sectors with traditionally low levels of general AI adoption, including construction, transportation and warehousing, and arts and entertainment, have shown disproportionately higher increases in hiring for roles related to AI-driven price setting.
Larger firms appear to be leading this transition. Analysis linking firm size with adoption rates found that companies with higher sales volumes in 2010 were significantly more likely to implement AI-based pricing technologies over the following decade. The researchers suggest that large upfront costs associated with adopting new technology may limit access for smaller businesses but allow larger ones to benefit from economies of scale.
The study also connects these hiring patterns to financial outcomes. Firms that increased their ratio of AI pricing jobs saw cumulative sales growth exceeding one percent during the sample period; they also experienced nearly three percent cumulative employment growth and modest increases in profit margins.
According to Jonathan Adams, senior economist at the Federal Reserve Bank of Kansas City; Sydney Miller, research associate at the same institution; and Zheng Liu, senior policy advisor at the Federal Reserve Bank of San Francisco: “We document that larger firms are more likely to adopt AI pricing and that firms that have adopted the technology have also grown larger and become more profitable.”
They add: “These patterns may be explained by the fixed costs of adoption, which discourage small firms from using the technology but allow large firms using AI pricing to reap the benefits of falling computation and data costs over time.”
The implications extend beyond firm profitability: “When firms use AI for price discrimination,” say Adams and colleagues, “it makes them larger and more profitable… This has implications for consumer welfare but also monetary policy: If firms are more sensitive to changes in demand, they may be more responsive to monetary tightening and easing.”
Among top adopters based on job postings are companies such as Deloitte, Johnson & Johnson, JPMorgan Chase, General Motors, and UnitedHealth.
The views expressed by Adams, Miller, and Liu do not necessarily reflect those of either Federal Reserve bank or the wider Federal Reserve System.
