Why AI Projects Fail and What to Do About It
Most AI projects fail. That’s a headline we often see. They fail not because the idea or design was faulty. Not because the technology wasn’t ready. No, they fail for the same reasons any ambitious project fails: weak alignment, unclear scope, lacking data quality, and poor governance. In this post, I explain why AI projects go off the rails, the signals to watch for, and the practices that high-functioning teams use to turn AI from hype into operational leverage.