Enterprise AI projects often fail not due to technology but due to weak data foundations, unrealistic expectations, and organizational will, according to Raghvendra Kushwah, Co-founder of Eucloid Data Solutions. AI quality depends on the data layer beneath it; fragmented, inconsistent data causes even sophisticated models to fail. Unified platforms like Databricks, with capabilities like Genie, enable business users to query governed data in plain language, but only work when data is unified; on fragmented data, they produce confident, wrong answers faster. A large US healthcare organization with more than 65 source systems, over 5,000 data objects, and roughly 800 pipelines spent nearly a year consolidating onto a single governed platform before pursuing AI. The consolidation delivered around 30% better performance and cost efficiency over the legacy environment, and the first AI step—Genie-powered bots for business users—followed at a fraction of the cost of building full dashboards. Governance is a second readiness dimension; pilots often die at the production gate when security and compliance teams question handling of sensitive data under regimes like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or India's Digital Personal Data Protection (DPDP) Act. Without built-in lineage, access controls, and auditability, AI cannot scale past the demo.