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What happens when enterprise AI meets inconsistent metrics, fragmented dashboards, and conflicting business logic?

In this episode of the Data-Driven Podcast, AtScale CTO and co-founder Dave Mariani sits down with Brad Lindsey and Jeremy Arendt from Blue Yonder to discuss how Blue Yonder transformed its analytics strategy from disconnected dashboards into a governed semantic layer foundation for AI and enterprise analytics.

The conversation explores why semantic layers have become critical infrastructure for AI, how governed metrics enable trusted self-service analytics, and why enterprises must standardize business definitions before deploying AI agents at scale.

Key topics include:

• Why Blue Yonder shifted from dashboard development to data infrastructure
• Building a universal semantic layer for AI, BI, Excel, and LLMs
• How semantic models eliminate inconsistent metrics across the business
• Why semantic governance matters for agentic AI
• The role of Model Context Protocol (MCP) and semantic context in enterprise AI
• Creating reusable governed business logic for analytics and AI
• How Blue Yonder reduced analysis work from 30 hours to 90 seconds using semantic models and AI
• Scaling trusted self-service analytics without losing governance
• The future of semantic layers as operational infrastructure for AI

The discussion also highlights a major shift happening across enterprise data architecture: semantic layers are no longer just BI tooling. They are becoming the governed operational foundation for AI-powered decision making.

Learn how Blue Yonder is preparing for a future where AI agents, dashboards, copilots, and analytics workflows all operate from the same trusted semantic foundation.