Artificial intelligence (AI) is reshaping the way we work. The technology’s rapid development and widespread adoption mean we still lack clear answers to fundamental questions: What kinds of human work can AI perform? Which employers are using it? What skills might it replace or create? As a result, policymakers do not have the data they need to fully understand AI’s impact on the U.S. economy, labor force, and industries. To address this knowledge gap, sociologist Jason Owen-Smith, executive director of the University of Michigan’s Institute for Research on Innovation and Science (IRIS) and courtesy professor at the Ford School, joined other experts from across the country at an American Enterprise Institute (AEI) seminar in 2024.
The outcome was a new policy report, “New Approaches to Characterize Industries: AI as a Framework and a Use Case,” which Owen-Smith co-authored with colleagues from the Wharton School at the University of Pennsylvania, New York University, Stanford University, and various U.S. government agencies.
Owen-Smith examined the measurement challenges of AI posed by recent policy directives like the 2022 CHIPS and Science Act (CSA). The CSA invests heavily in American science and technology programs and mandates measuring such investments’ effects on job creation. But it does not specify any specific measuring or reporting mechanisms for reviewers and the existing data are difficult to aggregate.
Simply put, we lack methodologies, measures, and systems to achieve this goal,” said the compendium authors.
Owen-Smith calls for policymakers to rethink their approach to technology classification and data gathering. Rather than relying solely on document-centric systems, which categorize AI innovations by research topics or patents, he recommends a people-focused strategy. By tracing the career paths and activities of practitioners, academics, and students, researchers can better see how and where AI is being applied in different industries. This approach, developed at IRIS and with partners across academia, government, and the private sector, is called the “Industries of Ideas Model.”
Owen-Smith also advocates for “data federalism,” regional networks of public-private partnerships anchored by universities and state agencies. Under this approach, data could be used to track industry movements and form a more comprehensive understanding of AI’s impact on local and regional economies.
>>Read the full compendium here.
>>Read more on this topic in Owen-Smith’s white paper “AI and Work: How to Build New Data Foundations,” published by AEI, October 2024.
About IRIS
The Institute for Research on Innovation and Science (IRIS) is a member consortium of universities anchored by an IRB-approved data repository hosted at the University of Michigan’s Institute for Social Research. IRIS was founded in 2015 with support from the Alfred P. Sloan and Ewing Marion Kauffman foundations.
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