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30 Hours to 90 Seconds: Blue Yonder’s Semantic Layer for Trusted Enterprise AI
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.
View all episodes
By AtScaleWhat 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.