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AI Summary
- Enterprise AI reality: 95% of AI projects fail because companies lack proprietary data, agentic workflows, and proper integration across RBC, Merck, and 7-Eleven case studies
- LLMs are rapidly commoditizing; competitive advantage shifts to proprietary data moats, agentic systems, and workflow integration rather than model capabilities
- Databricks and Glean executives reveal failed AI bets and operational lessons from real-world enterprise deployments across finance, healthcare, and retail