5 raisons pour lesquelles les projets d’IA générative échouent
📈 Over 80% of generative AI projects fail, doubling the average failure rate for tech projects, according to a Rand Corporation study interviewing 65 data scientists. The study identifies five main failure causes and offers best practices. Key failures include misunderstanding the problem, lack of data for training models, and inadequate infrastructure. Success requires clear problem definition, proper data and tools, and long-term commitment. Understanding AI's limitations is crucial for project leaders.
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