Generative AI is transforming how organizations interact with data, but without consistent definitions and governance, AI outputs can be misleading or inaccurate.
This executive guide explores how semantic layers serve as a critical foundation for AI-driven analytics—enabling trustworthy insights, natural language queries, and secure, scalable data access. Learn how leading enterprises are using semantic models to power the next wave of intelligent analytics.
Key Takeaways:
✅ Why generative AI needs semantic layers to produce accurate, business-aligned results.
✅ How semantic models reduce AI “hallucinations” and enforce governance across analytics workflows.
✅ Real-world examples of AI assistants and dashboards powered by semantic definitions.
✅ When to use semantic layers vs. raw data access for enterprise AI initiatives.
✅ Practical strategies for embedding consistency, context, and access controls into GenAI applications.
