Chapter 16A Regional and Data-Centered Approach to Workforce Development in an AI Economy
—Jeffrey Oakman (NJ AI Hub), Jennifer Rexford (Princeton University), Rachel C. Metzgar (Princeton University), and Federico d’Oleire Uquillas (Princeton University)
As artificial intelligence (AI) reshapes work and the economy faster than conventional training programs can adapt, we offer insights that can be replicated elsewhere for positive economic impact. This chapter offers a replicable, employer-informed framework for building AI-ready talent, being piloted by New Jersey's AI Hub—a partnership among the State of New Jersey, Princeton University, Microsoft, and CoreWeave. We argue that an AI-economy workforce strategy must be grounded in regional economic development practices, be coordinated regionally among key stakeholders, address the needs for broad AI literacy as well as sector-specific applications, and evolve through continuous feedback loops. Using an employer survey, we are mapping real-time skill demand, credential preferences, barriers to AI adoption, and training opportunities across priority sectors. Early input from pilot survey responses is discussed to demonstrate how complete survey data will allow the NJ AI Hub and its collaborators to translate findings into training and support for AI adoption, pilot workforce supports by sector, and institutionalize industry feedback loops, all to better align talent pipelines with the speed of AI's evolution.
Introduction: ...
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