Why most AI projects fail: It’s infrastructure and people 

Most AI projects fail due to inadequate infrastructure and people issues, rather than the technology itself. This is often overlooked by critics who blame AI for its inability to produce business results. To succeed, organizations need to focus on building robust infrastructure and addressing people-related challenges. This includes investing in data quality, model training, and collaboration between teams. By addressing these underlying issues, organizations can improve the chances of their AI projects succeeding.