The use of artificial intelligence (AI) in pricing strategies is becoming more common among firms, according to research by Jonathan Adams and Sydney Miller from the Federal Reserve Bank of Kansas City, and Zheng Liu from the Federal Reserve Bank of San Francisco. The researchers analyzed public job postings data provided by Lightcast to measure how firms are adopting AI-enabled algorithms for price setting.
Their method involved classifying job postings as “AI pricing jobs” if they met established criteria for AI-related roles and included the keyword “pricing.” This approach builds on earlier work by Acemoglu and others (2022), who defined AI-related jobs based on specific keywords such as “machine learning” and “neural networks.”
Findings show that between 2010 and 2024, the share of AI pricing jobs increased more than tenfold, even as overall employment in pricing roles declined by over one-third during the same period. The information sector, which includes large technology companies like Uber and Amazon, leads in both general AI jobs and AI pricing jobs. However, industries with traditionally low levels of general AI adoption—such as construction, transportation/warehousing, and arts/entertainment—are also using AI disproportionately for price setting.
Larger firms have been more likely to adopt AI pricing technology. Data indicate that companies with higher sales in 2010 were much more likely to implement these technologies between 2010 and 2023. The researchers suggest this trend may be due to the high upfront costs associated with adopting AI systems, which can be a barrier for smaller businesses.
The study also links adoption of AI pricing to financial performance. After implementing AI-based price setting, firms tend to see significant changes: a one percentage point increase in the ratio of AI pricing jobs predicts cumulative sales growth exceeding one percent during the sample period. This increase is partly attributed to scale effects but also reflects higher profitability; employment grows nearly three percent cumulatively while markups rise by about 0.3 percent.
“AI pricing is on the rise. 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,” write Adams, Miller, and Liu. “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.”
They add: “When firms use AI for price discrimination, it makes them larger and more profitable, as seen in the data. 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.”
The top companies posting for these roles span various sectors—including Deloitte at number one—followed by Johnson & Johnson (2), JPMorgan Chase (7), General Motors (14), and UnitedHealth (17).
The authors note that their views do not necessarily reflect those of their respective institutions or the Federal Reserve System.
