Somewhere in your organization, an AI project is dying. Perhaps it’s the recommendation engine that was supposed to boost sales by 30%. Maybe it’s the predictive maintenance system that promised to slash downtime. Or the customer service chatbot that was going to revolutionize response times. The digital dust gathering on these ambitious initiatives represents not just wasted resources but shattered expectations that make future innovation harder to champion. Think of AI projects like icebergs. What executives see in vendor presentations and tech magazines is the gleaming tip above water – the finished, polished success stories. What remains hidden is the massive underlying structure of data preparation, infrastructure requirements, talent needs, and organizational change management that makes those successes possible. This expectation-reality gap is perhaps the most fundamental reason AI projects fail. There’s a persistent mythology that AI is a magical technology you simply “apply” to business problems like a high-tech bandage. The truth is messier and more demanding.

Full analysis : The seven mistakes that most enterprises make while adopting AI projects.