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In this episode of the AtScale Data-Driven Podcast, Dave Mariani sits down with Juan Sequeda, Principal Researcher at ServiceNow and former Head of the AI Lab at data.world, to explore what’s next for analytics, AI, and trust in 2026.

As AI becomes embedded across the enterprise, one issue is emerging as a hard blocker: lack of context. Large Language Models are powerful, but without semantic layers, governed metadata, and shared business definitions, they struggle to produce reliable, explainable answers.

Juan and Dave discuss why metadata is no longer “documentation,” but operational infrastructure, and how semantic layers are becoming the foundation for trusted AI systems. They break down why dashboards alone can’t deliver prescriptive analytics, how AI agents are shifting analytics from insights to action, and why enterprises are consolidating around platforms that can govern context at scale.

The conversation also covers:

• Why AI and LLMs fail without semantic context
• The rise of semantic layers as enterprise AI infrastructure
• How metadata, knowledge graphs, and governance converge
• What AI means for dashboards, agents, and analytics workflows
• The future of tech jobs, systems thinking, and human skills

If you’re a data leader, analytics architect, or AI practitioner trying to understand how to make AI trustworthy in production, this episode explains why semantics, not models, are the real bottleneck.

🔗 Learn more about AtScale’s semantic layer and Model Context Protocol (MCP):
https://www.atscale.com

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