Despite concerns, AI is heavily embedded in network operations. However, this doesn't mean network professionals are losing control over their networks.
Autonomous networking doesn't mean relinquishing full control of the network. Instead, it refers to the automated interpretation of network state without human intervention. Network engineers aren't being replaced -- they're taking on a new role.
Modern AIOps systems are intelligent and continuously process telemetry data, logs, flows, metrics and events to detect patterns and surface actionable insights. This article discusses which processes AIOps systems can effectively automate in networking and how that affects network management.
The evolution of AI in network management
The evolution of network management can be outlined in the following three stages:
1. Manual operations. CLI-based troubleshooting, reactive responses and error-prone processes.
2. Automation. Error-prone CLI troubleshooting became possible through automation. Network teams went from implementing manual processes to running scripted workflows and policy-based configuration.
3. AIOps. Machine learning (ML) with telemetry and decision support. In multivendor environments, vendors continuously analyze network behavior in real time.
In the modern AIOps era, vendors use the following features for network management:
- ML for anomaly detection.
- Correlation engine for incident grouping.
- Stream telemetry processing.
- Predictive modeling for performance trends.
10 network tasks AIOps can automate
AIOps environments already have several functions operating. These tasks are autonomous, meaning they execute continuously without human intervention once configured.
- Anomaly detection. AI is an intelligent layer. It continuously learns normal network behavior and flags deviations, such as latency spikes, device instability and traffic surges. It relies on ML models rather than fixed rules.
- Event correlation and noise reduction. Many events happen on a network. AI groups thousands of raw alerts into a single meaningful incident by identifying relationships between events across devices, services and applications.
- Fault detection. The system can automatically detect failures in links, routers, switches or services by using telemetry signals such as packet loss and interface status.
- Telemetry processing and analysis. AI handles and processes massive streams of logs, metrics and network flows in real time. It can identify patterns and operational signals that humans can't analyze manually at scale.
- Network inventory discovery. As an intelligent layer, AI can automatically detect devices connected to the network, map topology changes and maintain a current inventory without manual intervention.
- Application performance monitoring. AI can learn normal network behavior and can predict potential issues before users report them. It tracks application behavior by analyzing latency, jitter and throughput.
- Security anomaly detection. AI can identify unusual network traffic behavior, such as unauthorized access, abnormal traffic flows and excessive server requests.
- Incident ticket automation. AI is an assistant to network teams, not a competitor. When issues are detected, it can automatically create and update tickets in IT service management systems with relevant context, logs and severity classification without manual input from security teams.
- Dynamic baseline generation. Instead of fixed limits, AI continuously updates what it considers normal for each device, app or network based on how traffic changes over time.
- Alert prioritization. AI ranks incidents by severity and potential business consequences, surfacing critical issues first without manual triage.
AIOps implications for network teams
AI is shifting the networking operating model. With network functions being increasingly automated, engineers spend less time on manual troubleshooting. This means network teams can focus on other areas of network management, such as system design and governance.
Networking professionals have shifted focus to the following new responsibilities:
- Automation policy definitions.
- Observability architecture design.
- AI-generated insight validation.
- AI system data quality management.
This shift is real, but gradual. In some companies, legacy operations remain in place and take time to phase out. However, regardless of the type of intelligence AI brings into networking, a trust-but-verify mindset remains crucial, as AI can hallucinate and lead to erroneous decisions.
Best practices for autonomous network operations
Organizations can adopt the following best practices to implement AIOps successfully within network operations:
- Build telemetry-first architectures. AI success depends on data quality and coverage.
- Automate low-risk domains first. Start with detection and correlation before moving to the control layers.
- Maintain human governance over changes. Autonomy without control can increase risks, even as it reduces toil.
- Upskill engineering teams. To adapt to industry disruption, network engineers should understand automation pipelines alongside more fundamental networking skills, such as routing protocols.
Autonomous networking doesn't replace network engineers -- it removes the need to manually analyze network data at scale, giving engineers more time to focus on critical tasks where human judgment remains essential.
Today, AI can detect issues, group alerts, prioritize issues and analyze telemetry. It can't, however, make high-risk changes to the network. Understanding this boundary is crucial for enterprises that want to use AI without creating organizational chaos.
Verlaine Muhungu is a self-taught tech enthusiast, DevNet advocate and aspiring Cisco Press author, focused on network automation, penetration testing and secure coding practices. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.
