Most enterprise AI projects don't fail because of the model. They fail because of data, specifically, the gap between having data and having data an AI can actually understand and act on. This is the last mile problem, and it's quietly killing AI ROI across the enterprise.

That's the real story behind the stat: 87% of AI projects never reach production.

This is Episode 20 of AI, Actually, the podcast that cuts through the hype to help business leaders get real value from AI. Jim Johnson is joined by Andy Sweet, Nicole Kosky, and Ben Titmus, who leads the data and infrastructure practice at AnswerRocket, to break down what it actually takes to make enterprise AI work at the data layer.

You'll learn:

• What "data readiness" actually means for AI, and why it's different from what your BI dashboards needed
• How to define and build a semantic layer iteratively, without a massive multi-year initiative
• Why throwing everything at an LLM produces confident-sounding wrong answers, and how guardrails prevent it
• How Microsoft Fabric, Snowflake, Databricks, and BigQuery are each approaching semantic layer infrastructure
• Why model-swappability, not model loyalty, is the right architecture posture right now
• What Chief Data Officers should do in the next 90 days to lead AI transformation from the front

This episode is designed for: Chief Data Officers, VP Analytics, data engineering leaders, and any executive responsible for making AI work inside a real business.

Learn more about AnswerRocket's enterprise AI solutions: https://answerrocket.com/ 

Speakers:

• Andy Sweet, VP Enterprise AI Solutions, AnswerRocket - https://www.linkedin.com/in/andrewdsweet/ 
• Jim Johnson, President, AnswerRocket - https://www.linkedin.com/in/jim-johnson-bb82451/
• Nicole Kosky, Senior Director of Services, AnswerRocket - https://www.linkedin.com/in/nicole-kosky-5b9a3b6/
• Ben Titmus, Senior Director, Practice Leader - AI Data, Platforms and Infrastructure - https://www.linkedin.com/in/benjamin-titmus-a2817626/ 

Chapters:

00:00     Introduction and Milestones

01:40     The Data Dilemma in AI

02:59     Understanding Data Readiness

05:50     Defining the Semantic Layer

08:46     The Importance of Context in AI

13:08     Navigating AI's Limitations

16:44     Building Guardrails for AI

21:12     Achieving ROI in AI Projects

26:45     Recommendations for Chief Data Officers

#EnterpriseAI #AIStrategy #DataReadiness #SemanticLayer #AIActually #ChiefDataOfficer #AIAgents