Why AI Projects Fail

A transcript for this video is available via YouTube. The transcript and captions are auto-generated and have not been edited.

James Ryseff, Anu Narayanan

VideoPublished Apr 10, 2025

By some estimates, more than 80 percent of artificial intelligence (AI) projects fail. That is twice the rate of failure for information technology (IT) projects that do not involve AI. RAND’s James Ryseff talked to experienced data scientists and machine learning engineers to uncover five root causes that lead to AI failures—and what can be done to minimize these issues.

Topics

Document Details

Citation

RAND Style Manual

Ryseff, James and Anu Narayanan, Why AI Projects Fail, RAND Corporation, PT-A2680-1, 2025. As of April 10, 2025: https://www.rand.org/pubs/presentations/PTA2680-1.html

Chicago Manual of Style

Ryseff, James and Anu Narayanan, Why AI Projects Fail. Santa Monica, CA: RAND Corporation, 2025. https://www.rand.org/pubs/presentations/PTA2680-1.html.
BibTeX RIS

Research conducted by

This publication is part of the RAND presentation series. RAND presentations may include recorded briefings related to a body of RAND research, videos of congressional testimonies, and multimedia presentations on topics or RAND capabilities.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.