The article argues that Enterprise AI initiatives fail to scale not because of AI models, but because enterprises lack a shared business understanding.
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While AI adoption is widespread, most organizations struggle to achieve measurable business value because business context remains fragmented across enterprise systems. It identifies four key challenges: inconsistent business definitions across systems, AI hallucinations caused by missing enterprise context, fragmented data spread across multiple applications, and repeated effort to rebuild integrations and business logic for every AI initiative.
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The article proposes Enterprise Intelligence as the missing layer between enterprise data and AI. By creating a shared representation of business entities, relationships, rules, and metrics, it enables AI, analytics, and workflows to operate from trusted business context, helping organizations scale AI initiatives more effectively.
