A new MIT study reveals that 95% of generative AI projects fail to deliver measurable results. Despite billions invested and immense hype, most pilots collapse due to flawed integration, misaligned priorities, and lack of organizational readiness. Only 5% succeed, often by focusing on targeted use cases, leveraging specialized vendor partnerships, and aligning AI with practical workflows. The findings have rattled markets, raising fears of an AI bubble similar to the dot-com crash. Yet, lessons emerge for businesses worldwide: start with purpose-driven pilots, invest in ROI-rich areas like back-office automation, and scale responsibly. This article explores the MIT study, examines investor jitters, uncovers broader risks like algorithmic bias and job displacement, and offers a roadmap for sustainable AI adoption. For global businesses, the message is clear: avoid the hype cycle and focus on strategy, not speculation.