Investment in artificial intelligence surged exponentially in recent years, fueling a boom in chip manufacturing, data center construction, and the development of novel AI models. Despite this rapid advancement and continuous spending increases, widespread AI adoption across various sectors faced significant hurdles.

While the technological capabilities of AI systems expanded, organizations struggled to integrate these technologies into their existing workflows. The challenge shifted from technological limitations to the preparedness of institutions and organizations to effectively absorb AI. Institutions, defined as the rules, incentives, standards, and accountability structures, play a crucial role in reducing uncertainty and fostering trust in new technologies. Organizations, operating within these institutional frameworks, must then adapt their workflows to accommodate AI.

The historical example of the chemical industry illustrated this point. While Germany pioneered the industry, the United States successfully diffused it by integrating chemistry into manufacturing and everyday commerce. This productivity boost only materialized after institutions evolved and organizations redesigned their workflows. The United States also established the discipline of business administration, which provided a framework for managing and scaling complex organizations.

The current situation with AI mirrors this historical pattern. The technology exists, but its widespread adoption requires a similar evolution of institutions and organizational structures. Without these changes, AI risks remaining a technology on the sidelines, failing to deliver its full potential economic impact.