From RAND Policy Currents <[email protected]>
Subject Most AI Projects Fail. Here’s Why
Date August 15, 2024 6:31 PM
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Policy Currents | The newsletter for policy people
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** Aug. 15, 2024
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Why AI Projects Fail and How They Can Succeed

AI is already being used to optimize supply chains, advance the development of pharmaceutical drugs, and pilot fighter jets. But despite the promise and the hype, many organizations are struggling to deliver working AI applications. In fact, by some estimates, more than 80 percent of AI projects fail. That's twice the rate of failure for IT projects that do not involve AI.

To understand why so many AI projects fall short, RAND researchers interviewed 65 data scientists and engineers with at least five years of experience in building AI or machine learning models. This revealed five root causes of AI failures:
- Industry stakeholders often misunderstand--or miscommunicate--what problem needs to be solved using AI.
- Many organizations lack the data to adequately train an effective AI model.
- There's too much focus on using new technology--and too little focus on using AI to solve real problems.
- Some organizations might lack adequate infrastructure to manage their data and deploy AI models.
- AI technology is sometimes applied to problems that are simply too difficult for it to solve.

To minimize these issues, organizations can ensure that technical staff understand the purpose of AI projects; invest in robust infrastructure; and work to understand AI's limitations. It's also essential to focus on the problem--not the technology. Chasing the latest and greatest AI models for their own sake, RAND researchers write, is one of the most frequent pathways to failure.

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