<html> <div class=„abstract product-page-abstract abstract-first-letter“><p>To investigate why artificial intelligence and machine learning (AI/ML) projects fail, the authors interviewed 65 data scientists and engineers with at least five years of experience in building AI/ML models in industry or academia. The authors identified five leading root causes for the failure of AI projects and synthesized the experts' experiences to develop recommendations to make AI projects more likely to succeed in industry settings and in academia.</p><p>By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI. Thus, understanding how to translate AI's enormous potential into concrete results remains an urgent challenge. The findings and recommendations of this report should be of interest to the U.S. Department of Defense, which has been actively looking for ways to use AI, along with other leaders in government and the private sector who are considering using AI/ML. The lessons from earlier efforts to build and apply AI/ML will help others avoid the same pitfalls.</p></div><div class=„full-bg-sand full-bg-bottom“><div class=„conducted“><ul class=„upc“><li class=„nsrd c6“><a href=„https://www.rand.org/nsrd.html“>RAND National Security Research Division</a></li></ul></div><div id=„indicia“><p class=„sans-small“><a href=„https://www.rand.org/giving/philanthropically-supported-research.html“>Funding for this research</a> was provided by RAND National Defense Research Institute (NDRI) exploratory research funding that was provided through the FFRDC contract and approved by NDRI's primary sponsor. The research was conducted within the <a href=„https://www.rand.org/nsrd/atp.html“>Acquisition and Technology Policy Program</a>m of the <a href=„https://www.rand.org/nsrd.html“>RAND National Security Research Division</a> (NSRD).</p><p class=„sans-small“>This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND research reports undergo rigorous peer review to ensure high standards for research quality and objectivity.</p><p class=„sans-small“>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 <a href=„https://www.rand.org/pubs/permissions.html“>www.rand.org/pubs/permissions</a>.</p><p class=„sans-small“>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.</p></div></div> </html>