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Rapidly Growing Data Pipelines Are Hurting Fortune 500 AI Accuracy

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Fortune 500 companies are seeing the accuracy of their deployed large language models degrade as they connect them to ever-expanding internal data sources. These sources include outdated documents, conflicting policies, and messy communication logs from platforms like Jira, Confluence, and Slack. This uncurated data flood creates semantic confusion for the models, leading to contradictory answers and operational failures. The core technical issues involve context saturation within the models and high semantic collision between similar but conflicting data fragments. The result is decision paralysis, compliance risks, and soaring computational costs for these enterprise AI systems.

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