Telecom firms and enterprise IT teams across global 5G and cloud networks are building systems that can “heal” themselves, using AIOps platforms to spot failures, fix faults, and keep digital services running without waiting for human intervention.
Networks’ complex is hitting new grounds, and mobile network operators (MNOSs) can no longer depend on humans’ oversight alone to spot problems in time. That gap is pushing companies to adopt AI-based operations that connect data, automation, and decision, turning it into a single control layer.
AIOps platforms use AI to manage information technology (IT) operations by automatically monitoring networks, analyzing huge amounts of data, and teaching systems to fix themselves. Self-healing networks rely on top AIOps platforms for reducing system downtime.
Self-Healing Networks
A modern self healing network collects constant data from routers, servers, radios, and security tools. The information is then is fed into AIOps platforms, where machine learning looks for patterns that signal trouble before it spreads.
These tools also support AI for network security and monitoring.
In many networks, this process is combined with IT operations automation so systems can reroute traffic, adjust capacity, or isolate risky devices. Several telecom groups now rely on top AIOps platforms for reducing system downtime.
With AI network monitoring, the system learns what normal activity looks like, simplifying the process of spotting problems that might have gone unnoticed in the past.
“Knowing what AI technology you are using, writing the right program to do what you want, and protecting the network from harm will remain an ongoing battle,” according to Telecom analyst, Jeff Kagan.
The approach is often used through AIOPs for incident management, which helps teams find the root cause of failures and apply fixes in minutes instead of hours.
Challenges like slow manual processes, fragmented infrastructure, and maintenance bottlenecks are the specialty for AIOps platforms that are designed to automate and simplify, as highlighted in Telecom26’s analysis.
AI Automation for IT Operations Difficulties
Even with advanced AIOps platforms, true zero-touch automation remains hard to achieve. Older hardware was not designed to support AI-driven control.
“The biggest hurdle is older infrastructure, since a lot of networks just weren’t built with automation or AI in mind. So, you’re trying to layer new tools on top of outdated systems that don’t give you the data or the control that you need,” said David Idle, CPO at Bigleaf Networks.
Idle considers that zero-touch functions best when everything is built from the ground up to support it. That is why many firms focus on AI and IT operations automation rather than full autonomy.
“AI has the ability to detect anomalies well; however, building networks with confidence in the causal relationship between anomaly detection, safe rollback, and clear accountability for all actions taken during the healing process presents a significant obstacle,” Principal Engineer at Cisco, Nik Kale, highlighted the security problem.
To reduce risk, operators limit how far AI network automation can go without approval.
Many choose a top AIOps platform for cybersecurity that includes safeguards, rollbacks, and human checks.
At the same time, combining A for network security and monitoring with a self-healing network approach increases the force of systems against threats. By integrating AI automation, networks can continuously improve performance, showing how AIOps platforms are the backbone for smarter, more resilient operations.
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