RAND Corporation released a new report, stating that behind the AI industry's success, 80% AI ProjectsWill fail, resulting in billions of dollars being wasted.
Report Introduction
RAND Corporation is a nonprofit global policy think tank, research institute, and public sector consulting firm based in the United States. The report was co-authored by several scientists and engineers in the field of AI.
Five reasons why AI projects fail
The agency interviewed 65 data scientists and engineers with at least five years of experience building AI/ML models in industry or academia, and found the top five root causes of AI project failure. The top five reasons are as follows:
- Investment Misalignment:Industry stakeholders often have misunderstandings about AI or poor communication, and are not clear about what problems AI can solve.
- Lack of sufficient data:Enterprises lack the necessary data to adequately train effective AI models.
- Blindly chasing new things:AI projects fail because organizations focus more on using the latest and greatest technology rather than solving real problems for their intended users.
- Lack of computing power:Enterprises may not have adequate infrastructure to manage data and deploy completed AI models, increasing the likelihood of project failure.
- AI bottlenecks:In other words, AI product positioning is to solve problems that are difficult to solve with current AI technology, and the goal is too far away to be achieved.
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