• Chicago Fed President Austan Goolsbee argues the real-world business adoption of artificial intelligence is lagging behind the hype.
  • Recent surveys indicate only modest penetration into core business operations, with few companies achieving transformative deployments.
  • The near-term economic impact is expected to be constrained by slow diffusion and organizational hurdles, despite optimistic long-term GDP forecasts.

Chicago Federal Reserve President Austan Goolsbee struck a cautionary note on the artificial intelligence revolution Tuesday, suggesting that the actual integration of AI into business processes has been more measured than public discourse implies. Speaking at an event, Goolsbee pointed to a gap between the intense media focus and investment flows into AI and its tangible, widespread application across the economy.

"The adoption of AI by businesses hasn't been as big as you think," Goolsbee said, according to people familiar with his remarks. This sentiment aligns with a growing consensus among economists who are scrutinizing productivity data for signs of an AI-driven boom. While technologies like generative AI are widely available, recent industry surveys show that many firms remain in an experimental phase, piloting tools for specific tasks rather than overhauling core operations.

Efforts to quantify the economic impact are yielding mixed results. Some studies project AI could boost global GDP by as much as 15% over the long term, but near-term effects appear muted. Data suggests that only about 10-15% of current GDP is likely to be directly impacted over the next two decades. In selected case studies, such as customer service and code generation, productivity gains have been significant—sometimes exceeding 50% in task completion speed. However, these appear to be outliers rather than the norm for most businesses, which are reporting incremental improvements.

A major hurdle continues to be organizational inertia. Integrating AI at scale requires significant changes to workflows, employee retraining, and data infrastructure, a complex process that slows adoption. The pace varies sharply by sector; technology and financial services are leading, while manufacturing and healthcare progress more slowly due to regulatory hurdles and the complexity of physical processes.

When reached for further comment, a spokesperson for the Chicago Fed did not immediately provide additional details. The cautious assessment comes as governments in the US and EU advance new AI policies, adding another layer of consideration for businesses weighing large-scale investments. For now, it seems the transformative potential of AI remains just that—potential, awaiting a broader and deeper adoption curve to truly reshape the economic landscape.