Understanding why Artificial Intelligence Projects Fail and Strategies for Success
Authors Ryseff, De Bruhl and Newberry (2024) explore why AI and machine learning (AI/ML) projects often fail. The study consisted of interviews by 65 experienced data scientists and engineers. The study identifies five key root causes for these failures:
Misunderstanding or miscommunicating the problem that AI is meant to solve.
Insufficient data to train effective AI models.
Overemphasis on using the latest technology rather than focusing on real problems.
Inadequate infrastructure for data management and AI model deployment.
Applying AI to problems that are too complex for the technology to address.
They offer several recommendations for success. The recommendations include; industry leaders should ensure clear understanding of project goals, more…
