About This Webinar

Enterprise AI initiatives are stalling, not because of models, but because business logic, rules, and governance are not AI-ready.

While organizations are deploying AI agents, copilots, and automation across the enterprise, their decision logic is fragmented across customizing, rules, approvals, and hierarchies that AI cannot interpret on its own. AI can process data, but it cannot understand how your business makes decisions.

This webinar introduces the Business Context Layer for Enterprise AI, a governed platform that transforms enterprise systems into structured, reusable business reasoning. By harvesting business context, rules, and authority from enterprise systems, and packaging them into governed Business Data Products, organizations can move decision logic, not just raw data, into AI-ready form.

The impact is immediate: reduced effort to deliver AI use cases, consistent decision logic across systems, and AI that operates in line with business rules and governance. The same semantic assets power agents, copilots, analytics, and automation, without duplication or drift.

Cirql One shows how to bridge the gap between your enterprise landscape today and a scalable AI governance foundation your organization needs, delivering control for executives, leverage from your SAP investment, and scale for your architecture.

Key Takeaways

  • Why AI agents fail without structured business rules & decision logic
  • Where your SAP governance logic resides, and how to unlock it for AI
  • How to build a Business Semantic Layer that governs AI at enterprise scale
  • How to create a unified foundation for consistent decisions across use cases

Meet Your Speaker

Carl Donsbach
Carl Donsbach
Customer & Partner Success Manager

Webinar Details

Date:

May 21, 2026

Time:

4:00 pm
CET

SECURE YOUR SPOT

Limited seats available for live Q&A participation

Join leading enterprises already planning their BW migration strategy

Questions? Contact us at info@cirqlone.com or +49 6221 4053 650

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About This Webinar