Cloud AI Projects Are Failing—Here's What No One's Telling You

Ninety-five percent of enterprise generative AI projects fail, a staggering figure revealed by an MIT study. This failure isn't rooted in insufficient infrastructure, as some vendors claim, but rather in human expertise and preparation. Many enterprises lack the talent required to build, train, and refine foundational AI models. Instead of trying to reinvent the wheel, companies would achieve greater success by leveraging mature, licensed AI models developed at scale by industry-leading providers. The issue of preparation is also critical. Running large-scale AI successfully today would have required enterprises to start planning up to seven years ago, with investments in power, cooling, networking, and infrastructure. Most organizations didn't take those steps, leaving them unprepared for AI at scale. The solution, however, lies in the cloud. Cloud platforms allow enterprises to bypass infrastructure latency and start AI projects immediately using the data they already have. Public cloud providers like AWS, Azure, and GCP enable companies to connect, unify, and reason across on-premises and cloud-based data without years of preparation or costly upgrades. The future of AI belongs to organizations that act quickly, leveraging available tools and their existing data to drive competitive advantage. Success isn't a distant goal; it's achievable now with cloud-enabled innovation.

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