"Adoption is high, hype is higher, but meaningful business impact remains elusive. AI is everywhere except on the bottom line. Why this disconnect? It's not that AI technology suddenly hit a wall. The models are more powerful than ever. The problem is how companies are using AI, not what AI can or cannot do. Organizations have treated AI like just another software deployment, expecting a plug-and-play solution."
"The GenAI divide is between companies that install AI tools and those that build the capability to use them. Many enterprises are on the wrong side of this divide, convinced that buying an AI tool is equivalent to having an AI solution. At the same time, employees often get more value from shadow AI than officially sanctioned AI projects. Businesses are deploying plenty of AI, but only a handful have figured out how to extract real value from it."
Enterprises are investing heavily in generative AI, yet most projects deliver no measurable returns and only about 5 percent reach widespread production. High adoption and experimentation coexist with limited business impact because organizations treat AI as a software plug-in rather than a new form of labor that needs training, contextual data, and workflow changes. Many firms buy tools instead of building capability to integrate AI. Official projects often underperform while shadow AI yields more immediate employee value. Pilots proliferate but fail when legacy processes and inadequate integration prevent AI from scaling into operational use.
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