The Reality of Enterprise AI Readiness and the Integration Deficit - Onix
Many business leaders define artificial intelligence readiness through isolated metrics like graphics processing unit capacity, clean data pipelines, or machine learning operations. While these components are important, they describe the basic requirements rather than the root causes of execution failure. Because organizations overlook the need for a unified context infrastructure, approximately 85% of enterprise artificial intelligence initiatives fail to meet operational expectations. Without a persistent, machine-queryable representation of data lineage, business logic, and key performance indicators, advanced applications remain expensive tools running on data they cannot interpret. To overcome this, modern systems must transition away from isolated tools to a unified environment characterized by: A persistent, queryable representation of business rules and organizational logic. Comprehensive data lineage mapping that tracks how information moves across the corporate network....