Generative AI promises to democratize analytics by allowing anyone in the organization to ask business questions in natural language. Yet many enterprises struggle to move beyond pilots and proofs of concept. The challenge isn’t the AI model itself. It’s the foundation underneath it.
In this session, AtScale’s Vice President of Solutions Engineering Daniel Gray demonstrates why enterprise-scale AI requires more than direct access to data. Through live demonstrations using Gemini, Claude, Cursor, BigQuery, and AtScale’s semantic layer, Daniel shows how organizations can overcome the four biggest barriers to GenAI adoption: data ambiguity, inconsistent business semantics, governance challenges, and uncontrolled costs.
You’ll see why large language models struggle when asked to interpret complex enterprise data models directly, and how semantic layers provide the business context required for accurate answers. Deterministic analytics are becoming essential as organizations move from human-guided BI to agentic AI workflows. The session explores how semantic layers reduce AI infrastructure costs through intelligent query optimization and automated aggregations while maintaining governance and security across every tool employees choose to use.
Key Takeaway
Enterprise AI success depends on more than model selection. Organizations must provide AI systems with governed business context, deterministic metrics, and scalable performance. A semantic layer transforms AI from a tool that guesses into a system that understands how the business measures success, enabling accurate, explainable, and cost-effective analytics at enterprise scale.
See how enterprises make GenAI accurate and governed in this demo.
What You’ll Learn
- Why direct LLM-to-database approaches struggle with accuracy and determinism
- How semantic layers eliminate data and column ambiguity
- The difference between prompt engineering and context engineering
- How governed business semantics improve AI accuracy
- Why performance and cost optimization are critical for enterprise-wide AI adoption
- How automated aggregations reduce AI infrastructure costs
- Best practices for security, governance, and role-based access controls
- Why explainability and determinism are foundational requirements for agentic AI
- How to support AI, BI, and analytics tools without vendor lock-in
- What it takes to move from AI pilots to measurable enterprise ROI
