James Ryseff, Brandon De Bruhl, Sydne J. Newberry
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 identifiedFirst, 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 for AI to solve.
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