There is no charge to attend but you must be registered as a full delegate to sign up. Seating is limited. 

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Autonomous networks promise faster resolution, better reliability, and lower operating costs, yet most AI initiatives stall before execution. This learning lab examines why. Attendees will work through the architectural, data, and governance foundations required to move from AI pilots to trusted, closed-loop network operations at scale.  Attendees will address why AI and automation initiatives fail to scale in network operations. The session helps participants identify gaps in network data authority, connectivity modeling, and governance, and shows how to establish a trusted foundation that enables AI systems to move safely from insight to execution.

You’ll learn:

• Why AI and automation efforts in network operations stall due to fragmented network representations and weak governance.
• Defining characteristics of an authoritative, machine-readable system of network truth.
• A sequencing roadmap and practical readiness to prepare networks for closed-loop automation and autonomous operations.