Скачать или смотреть AI Project Failure: The Data Consolidation Gap

The ease of using consumer tools like ChatGPT often fuels a utopian vision where AI magically solves all business problems, leading to a world where "nobody will have to work again." However, the reality is that most companies are fundamentally unprepared to take this leap. The chasm between a simple prompt and a functional enterprise AI is vast, primarily because businesses lack the necessary foundation—their "books are not in order."

Critical business knowledge is often fragmented: scattered across outdated PDFs, siloed in old legacy support systems, or existing as "tribal knowledge" locked inside an employee's head. For instance, a support solution might only reside in "Jimmy's head." Attempting to train AI on this chaotic, decentralized data results in immediate failure.

To cross this chasm and unlock true automation, the first necessary step is data consolidation. Businesses must first extract and centralize all this disorganized intelligence to create a clean, proprietary AI model. This rigorous preparation, though arduous, is what moves the company out of the valley of despair and enables the automation and optimization that leadership expects.

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