Why AI Projects Fail
Artificial intelligence (AI) projects fail at a much higher rate than information technology (IT) projects that do not involve AI. What are the root causes that lead to these failures—and what can be done to minimize them?
Anu Narayanan is acting vice president and director of the RAND National Security Research Division (NSRD), which conducts research for the Office of the Secretary of War, other U.S. government organizations, and the governments of U.S. allies. She is a senior engineer at RAND, and professor of policy analysis at the RAND School of Public Policy. Her research focuses on the intersection of critical infrastructure and national security. She has led or conducted studies for the Department of the Air Force, the Office of the Secretary of War, the Department of Homeland Security, and the Department of Energy on a range of topics including installation and infrastructure investment decisionmaking, cybersecurity of the electric power grid, mission assurance, strategic basing, and data analytics for critical infrastructure risk management. Narayanan holds a Ph.D. in engineering and public policy from Carnegie Mellon University.
Ph.D. in engineering and public policy, Carnegie Mellon University; B.S. in applied mathematics, University of Texas at Austin
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