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Why AI Projects Fail and How They Can Succeed
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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: RAND Corporation
A RAND Corporation report examining practical barriers to AI deployment success, relevant to policymakers and organizations seeking to understand why AI systems underperform or fail when moved from development to real-world use.
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Importance: 45/100organizational reportanalysis
Summary
This RAND Corporation research report analyzes the common reasons AI projects fail in practice, examining organizational, technical, and governance challenges. It provides evidence-based recommendations for improving AI project outcomes across government and industry contexts. The report is particularly relevant for understanding the gap between AI capabilities and successful real-world deployment.
Key Points
- •AI project failures often stem from organizational and cultural barriers rather than purely technical limitations.
- •Misaligned expectations between technical teams and leadership/stakeholders frequently undermine AI initiatives.
- •Data quality, availability, and governance issues are among the most common causes of AI project failure.
- •Successful AI deployment requires sustained institutional commitment, clear success metrics, and iterative development processes.
- •The report offers structured recommendations for government and organizational decision-makers to improve AI adoption outcomes.
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The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI | RAND
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RAND researchers interviewed data scientists and engineers with experience in building artificial intelligence and machine learning (AI/ML) models in industry or academia to investigate why AI projects fail. They synthesized the experts' experiences to develop recommendations for smart implementation of AI. The lessons from earlier efforts to build and apply AI/ML will be helpful for others to avoid the same pitfalls.
The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed
Avoiding the Anti-Patterns of AI
James Ryseff , Brandon F. De Bruhl , Sydne J. Newberry
Research Published Aug 13, 2024
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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.
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.
Key Findings
Five leading root causes of the failure of AI projects were identified
First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.
Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult
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