What does enterprise AI actually need to succeed: more data access or better context?

In this episode of the Data-Driven Podcast, AtScale CTO Dave Mariani sits down with Coginiti CTO Matthew Mullins to unpack one of the most important debates in modern data architecture: access vs. understanding. The conversation explores the rise of the semantic layer, the role of open semantics, and why initiatives like the Open Semantic Interchange (OSI) matter for the future of AI and analytics.

Matthew shares his journey from cognitive science and formal semantics into enterprise data, and how those foundations are becoming critical again in the age of AI. Together, Dave and Matthew break down:

• Why inconsistent metrics (like “29 definitions of customer”) still plague enterprises
• The real purpose of OSI and whether it should be an interchange format or a modeling standard
• Why semantic layers are becoming core infrastructure for agentic AI and LLMs
• How the Model Context Protocol (MCP) fits into the stack, and what it doesn’t solve
• Why AI cannot replace human-defined business semantics
• The shift from dashboards to AI-driven, ad hoc business intelligence

The key takeaway: LLMs don’t understand your data; they understand language about your data. Without a semantic layer to provide governed definitions, relationships, and context, AI systems will produce inconsistent or unreliable results.

If you're building AI-powered analytics, designing data platforms, or evaluating open standards like OSI, this conversation outlines where the industry is heading.

Learn more about AtScale’s universal semantic layer: https://www.atscale.com