Details
Large Language Models promise to transform analytics, yet enterprises consistently encounter hallucinations, inconsistent responses, and outputs they can’t trust for strategic decisions. The fundamental issue isn’t the AI – it’s the missing context layer that provides governed metrics and business logic.
A semantic layer solves this by codifying domain knowledge and enabling full traceability. When AI systems access the same governed logic that powers trusted BI, they become consistent, explainable, and enterprise-ready. In this webinar you will learn:
- Why AI systems fail without proper context and governance
- How semantic layers create trustworthy, traceable AI outputs
- Integrating semantic layers with data governance frameworks
- Steps to operationalize AI that’s reliable and scalable across enterprise use cases
Speakers
Post-event summary
The webinar titled “From Experimental to Enterprise: How Semantic Layers Make AI Trustworthy,” was hosted by EDM Association and AtScale and focused on how semantic layers are becoming a foundational requirement for trustworthy enterprise AI. Speakers included:
- David Mariani, CTO, Co-Founder, AtScale
- Kyle Morton, COO, EDM Association
Dave opened by explaining the widening gap between the promise of large language models and their real-world accuracy when applied to complex organizational data. He emphasized that LLMs struggle because they must interpret custom schemas, inconsistent metrics, and sparse metadata—leading to error rates as high as 80% when querying raw enterprise data. Semantic layers, he explained, solve this by translating physical data into governed, business-ready concepts that LLMs can reliably understand. This framing set the stage for a deeper comparison between semantic layers and knowledge graphs, highlighting that semantic layers define what businesses measure, while knowledge graphs define how data entities relate, and that both can be used together to strengthen context.
A central portion of the webinar explored the Model Context Protocol (MCP), which Dave described as “like JDBC for LLMs”—a standardized way for models to connect to external systems and understand both available data and permissible workflows. MCP tools allow LLMs to log into the semantic layer, list models, retrieve metadata, and run governed queries automatically. Through a live demonstration using Claude, Dave showed how an LLM can navigate AtScale’s semantic models, interpret metadata, generate accurate SQL behind the scenes, and deliver insights typically requiring BI expertise. The model not only answered direct analytic questions but also generated deeper, unsolicited findings by autonomously running additional queries. In one example, Claude identified high-value customers, product mix patterns, and regional trends, producing visualizations and recommendations without manual setup. This capability illustrated how LLMs, when grounded in a semantic layer, shift analytics from manual exploration to automated insight generation. As Dave noted, “It can’t find that needle if it doesn’t have context, and if you can’t trust it.”
The discussion also addressed how organizations can adopt semantic layers even outside traditional structured domains, acknowledging that while current tools excel with structured data, semi-structured formats like JSON can bridge gaps. Dave and moderator Kyle Morton highlighted that organizations already using semantic layers for BI have a significant head start in operationalizing AI because they’ve already solved for governance, consistency, and metric definition—issues that otherwise stall AI adoption in the C-suite. The speakers emphasized that semantic layers are no longer just BI accelerators but strategic infrastructure enabling safe, reliable, and scalable AI across the enterprise.
The webinar concluded with guidance for practitioners: business stakeholders should initiate conversations about semantic layers, backed by IT and data teams who operationalize them. The overarching message was clear—trusted data, strong governance, and semantic context form the essential backbone for enterprise AI.
