Our analytics data is trusted, governed, and boardroom-ready. So why is it letting our AI initiatives down?

This deep dive unpacks one of the most common — and costly — misconceptions SAP organisations are running into right now: that data good enough for a dashboard is automatically good enough for an AI model or agent. It isn't, and the gap between the two is not a data quality problem. It is a fundamental difference in what each consumer needs from our data.

Drawing on hands-on experience working across SAP data landscapes, Ingo walks us through real scenarios in BW, SAC, and Datasphere — examining what analytics-ready and AI-ready data each require, where they conflict, and how over-optimising for one actively sabotages the other. He then explores how Datasphere's semantic layer and data products can help us serve both consumers from a shared, trusted foundation without forcing everything down the same pipeline.

We leave with a practical framework for assessing our own data estate's AI readiness, a sharper understanding of the "dashboard trap" that catches high-analytics-maturity organisations off guard, and concrete next steps we can take back to our teams.

What You’ll Learn

• A clear distinction between analytics-ready and AI-ready data — and why the difference matters right now
• A diagnostic lens for identifying where our existing data estate is AI-ready and where it quietly isn't
• A practical approach to serving both consumers from Datasphere without rebuilding from scratch