How AI + Automation Are Paving the Way for Autonomous Networks
Overview
Network management teams are drowning in alerts, tickets, and manual escalations—slowing MTTR, driving up costs, and jeopardizing service quality. But the rise of AI-driven automation is changing the game.
In this webinar replay, experts from Grokstream and Resolve discuss how AIOps and automation are shaping the future of autonomous networks. Join Josh Kindiger and Ari Stowe to see firsthand how AI can reduce noise, detect root causes, and trigger end-to-end remediation without manual effort.
We discuss:
- The current landscape: What's driving the push toward autonomous networks
- Why autonomy matters: Core concepts and the transformative benefits
- Maturity assessment: How to assess where your network stands on the autonomous maturity scale
- Live demonstration: Watch a real-world use case where AI and automation come together
- Building your roadmap: Practical strategies for developing network autonomy
As AI and automation become the driving forces behind next-generation networks, the industry is heading towards a future of full autonomy.
Don't miss this opportunity to learn from the experts about shaping the future of network operations.
Key Takeaways
- Network operations automation is the foundation of autonomous networks. Reducing alert noise is helpful, but true progress toward an autonomous NOC requires moving from correlation to action by pairing AIOps for network operations with real remediation workflows.
- AIOps without automation stalls at “insight.” Correlating alarms and grouping events may reduce volume, but it doesn’t change MTTR or operational cost unless it triggers AI-driven network remediation that resolves issues end-to-end.
- Repeat incidents are the fastest path to autonomy. A large percentage of network incidents are recurring patterns. By identifying those patterns and attaching automation to them, teams can eliminate thousands of repetitive tickets.
- Predictive operations require visibility into suppressed signals. Many teams hide low-severity events to cope with noise, but those signals often precede outages. Reintroducing that context enables earlier detection and preventative automation.
- Trust in AI builds over time. Autonomous network operations don’t start with full automation. They begin with recommendations, move to human-in-the-loop execution, and mature into automated remediation as confidence and explainability increase.
FAQs
Q: What advice would you give for adopting AIOps and automation in hybrid or legacy environments?
A: Hybrid and legacy environments are the norm, not the exception. The key is starting with a low-risk pilot that integrates into existing observability and incident tools without disrupting operations. Automation doesn’t require perfect APIs; many legacy systems can still be automated through command-line actions, file exchanges, or existing operational processes.
Timestamp: 40:41–42:52
Q: How should teams assess where they stand on the network automation maturity model?
A: Start by mapping the repeatable operational processes you already run today. Many organizations have isolated automations but lack end-to-end orchestration. Focus first on high-volume, recurring network issues, automate those patterns, and then expand toward broader, cross-functional outcomes as maturity increases. The result is meaningfully improved operations.
Timestamp: 43:54–45:28
Q: What’s the biggest misconception about AIOps today?
A: That you need perfect data or advanced operational maturity before getting started. The speakers emphasize that waiting for pristine monitoring, ticket hygiene, or fully normalized data slows progress. Most organizations can begin immediately, learn from real patterns, and mature faster through iteration.
Timestamp: 45:39–46:35
