About the Compendium

New Approaches to Characterize Industries: AI as a Framework and a Use Case brings together leading economists, data scientists, and policy experts to tackle one of the biggest challenges in the AI era: how to measure its real impact on industries, jobs, and skills. Traditional classification systems are failing to capture the rapid diffusion of generative AI, leaving policymakers and practitioners without the data needed to respond effectively. This volume highlights innovative strategies—from “industries of ideas” models that track talent flows to new workforce data linkages and skill-taxonomy frameworks—that can guide smarter education, training, and economic policy.

Introduction 

The newest artificial intelligence technologies, especially generative AI systems, could fundamentally transform how firms do business and how Americans work. Still, there is little data and evidence to understand how AI is reshaping the economy. 

This is coming just as the politics of work are changing. The automation and trade shocks of the early 2000s and the resulting “deaths of despair” have brought political attention to how technological and economic shifts can transform communities and livelihoods. Business leaders, workers, educational and training institutions, and governments need local, timely, and actionable data to help the workforce respond to shocks that are likely to be even greater than those of two decades ago. 

Unfortunately, the visibility of the workforce’s transformation has also made it clear that traditional data sources are inadequate to inform that response. Even current scientific and industrial classification systems are not fit for this purpose. AI, like many other new and emerging technologies, is neither a well-defined scientific field nor a distinct industry.¹ Moreover, national governments’ increasing focus on industrial policy, which is fundamentally reshaping the economy and society, suggests that entirely new data and frameworks may need to be developed to understand how workers and firms interact. 

On March 18, 2024, the American Enterprise Institute, Stanford University’s Digital Economy Lab, and New York University convened a daylong seminar titled “New Approaches to Characterize Industries: AI as a Framework and a Use Case.” Its goal was to begin exploring elements of a theoretical framework that could help meet this data challenge, and seminar participants were tasked with identifying what data, definitions, models, and tools exist or could be developed. 

Participants were invited to the workshop for their expertise in data, measurement, and analysis. But more importantly, each presenter had a history of designing, deploying, and using new data systems— such as the Longitudinal Employer-Household Dynamics program at the US Census Bureau, the Institute for Research on Innovation & Science (IRIS) at the University of Michigan, the Texas Workforce Commission, and the New Jersey Department of Labor & Workforce Development—and private job market data. As intended, workshop participants drew on their experiences to identify empirically implementable, dynamic, and flexible approaches for understanding this critical and emerging space. 

The following three key takeaways emerged from the formal and informal discussions: 

1. An innovative institution should be established to implement a new vision and framework. This independent, nonpartisan institution could be dedicated to producing bottom-up, demand-driven tools and insights for businesses, workers, and governments by connecting advances in AI and other critical technologies to changes in the nature of new and existing jobs, skills, and economic opportunities.
2. Policymakers and practitioners should support the institution in establishing partnerships that directly serve the needs of businesses, workers, and researchers. These partnerships would build an understanding of how AI and other emerging technologies affect local and regional economies and labor markets. An initial focus might include
   
   1. Providing data and insights to firms as businesses’ AI capabilities move from experimental use to enterprise use at scale,
   2. Prototyping and producing customizable tools so current and future workers can acquire skills in response to changing demands, and
   3. Producing tools and analyses that federal, state, and local government agencies would use to allocate programmatic education and training resources.
3. Finally, the new framework and resulting classifications should be designed to inform and be complemented by the federal statistical system’s operations; federal, state, and local program and service providers; and scientific research. This can be achieved by bringing together the best minds from each key sector through focused fellowship, training, and competitions.

In this introduction, we analyze and integrate the papers produced for the workshop and the day-of exchanges among the invited experts. The chapter is divided into six key sections that move from the central challenges and opportunities in AI measurement through perspectives informed by research and federal and state agencies, concluding with an analysis of how to better represent AI data through a “follow-the-people” approach to talent flows—rather than using firm surveys to measure AI’s implementation. Workshop participants outlined a number of opportunities to strengthen data collection.