Towards the Dark NOC: AI-native operations at scale

Case study: How the NOC becomes AI-native
Traditional network operations centers are evolving into AI-native operational environments powered by predictive assurance, digital twins, and autonomous workflows. This session explores how CSPs are redesigning the NOC for AI-native operations — using digital twins, predictive assurance, and autonomous workflows to move from reactive fault management to real-time decision-making. 

• Scaling autonomous assurance across complex, hybrid networks 
• Using digital twins to predict and simulate network behavior 
• Moving from fault detection to root cause decisioning 
• Closing the loop with autonomous remediation 

Panel: When AI takes the ops seat
As telecom operators introduce AI-assisted and autonomous operational workflows, the role of network operations teams is rapidly evolving. This panel explores how AI is already making operational decisions inside live network environments - and what it takes to scale this safely across the NOC, from incident management to real-time optimization. 

• Where AI is already making real operational decisions today 
• What breaks when you try to scale autonomy 
• Where operators still don’t trust AI - and why 
• How far AI should go in live operations 
• What the next-generation NOC team looks like 

Additional topics covered in this session block may include:

• What does a dark NOC actually look like? 
• Making AI decisions visible: Observability in the AI-native NOC 
• The last mile in autonomy: Human decision boundaries 
• Making autonomy repeatable across the network