AI-Driven Network Operations: What’s Real and What’s Marketing for ISPs
This is the second installment in my series about the modern Internet Service Provider.
The term AIOps gets thrown around liberally, and it’s worth being precise about what machine learning actually does well in network operations versus where it’s still a pipe dream.
Where ML Delivers Measurable Value Today
Anomaly detection is the most mature application. Network telemetry generates enormous volumes of time-series data—interface counters, latency measurements, BGP update rates, optical power levels. Training models to recognize normal baseline behavior and flag deviations works. It works because the data is structured, voluminous, and the cost of false negatives (missed anomalies) is high enough to justify the investment in model development and tuning. ISPs have large amounts of data. This only grows as networks get bigger. The challenge here is integrating that into existing systems such as LibreNMS, Zabbix, or whatever the ISP is using. You can collect all the data you want, but doing something with it is where the magic happens.
Predictive capacity planning is the second area where AI has proven its worth. Rather than relying on static thresholds (“alert when utilization exceeds 80%”), models that account for time-of-day patterns, seasonal trends, and growth trajectories can predict when a link or node will hit capacity constraints weeks or months in advance. This shifts capacity augmentation from reactive to planned, which has direct cost implications.
The ISPs getting real value from AI in network operations aren’t chasing autonomous networks. They’re using AI to reduce mean time to detect (MTTD) and to make capacity decisions with better data. That’s not sexy, but it’s measurable.
Where the Hype Outpaces Reality
Fully automated remediation—the idea that an ML system detects a problem and fixes it without human intervention—remains limited to narrow, well-defined scenarios. Automated BGP blackhole activation in response to a detected DDoS attack is achievable. Automated rerouting around a fiber cut with complex traffic engineering implications is a different matter entirely. The blast radius of an incorrect automated action on a production ISP network is too large for most operators to accept without human-in-the-loop validation. These solutions tend to cause more harm and effort than they solve, at least at this time.
The path forward is progressive automation: start with detection, move to recommendation, and automate remediation only for actions with well-understood, bounded failure modes.
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