6 Proven Lessons from AI Projects That Broke Before They Scaled
Analysis of failed AI projects reveals six common pitfalls: vague goals, poor data quality, overcomplicated models, deployment oversights, maintenance neglect, and insufficient stakeholder buy-in.
