Organizations understand AI’s potential, but many initiatives stall before delivering meaningful operational impact.

The problem is rarely the model alone. More often, AI struggles to scale because data is fragmented across legacy systems, governance is added too late, and decision processes lack the explainability and human oversight required in regulated environments.

This session explores a practical framework for deploying human-governed agentic AI workflows on top of existing systems. Rather than waiting for large-scale modernization, the approach focuses on making data AI-ready, orchestrating governed agentic workflows, and embedding human oversight directly into decision loops.

Attendees will learn how organizations can move beyond pilots by designing for trust from the start; combining explainability, auditability, and human-in-the-loop control to enable safe, scalable AI adoption in high-stakes environments.

* Target Audience *

• Executive sponsors and senior leaders responsible for AI investment, risk, and operational outcomes
• Implementation teams across data, analytics, AI/ML, engineering, architecture, governance, risk, and compliance
• Business and operational leaders in operations and customer-facing functions who are using, or exploring how to use, AI in day-to-day processes.

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* Session Format & Duration *

30-60 minute talk with Q&A, structured as a practical presentation that combines a clear problem framing, a deployment model, and actionable patterns for moving AI from pilot to production.

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* Key Takeaways *

• Why AI initiatives stall between pilot and production in complex organizations
• A practical framework for deploying human-governed agentic AI workflows
• How to design AI systems for explainability, auditability, and human oversight
• Reusable deployment patterns for trusted AI in regulated environments

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* Technical Requirements *

• Projector or large display
• HDMI or USB-C connection
• Internet access optional but preferred

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