The newest ally in the hunt to improve network performance management is a familiar one: AI. It’s needed now in network observability because traditional approaches aren’t working. Network Operations (NetOps) teams are using outdated observability tools that lack customization, scalability, and data integrity leaving practitioners overwhelmed by alert fatigue and fragmented insights.
It’s no surprise then that only 46 percent of IT leaders consider themselves fully successful with network observability tools, according to recent research from Enterprise Management Associates (EMA) and BlueCat. Enterprise networks now span hybrid, multi-cloud, edge, and remote environments. As a result, increased network complexity is a given, however, expectations of network performance from customers, partners, and employees show no signs of abating.
Today network performance is no longer just a problem for engineers; it impacts every area of the business. As organizations embrace distributed applications, increased AI workloads, and edge deployments, they must understand how to optimize and modernize their observability toolsets to increase performance and avoid vulnerabilities.
This is where AI fundamentally changes the equation. AI is no longer an incremental enhancement to observability; it’s the catalyst that allows CIOs to move beyond fragmented, reactive tools and build a unified, proactive system of intelligence that anticipates issues, improves performance, and strengthens business resilience.
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Successful enterprises have shrunk their observability toolsets, improved data quality and collection, and embraced AI-driven automation. These outcomes are possible only if CIOs equip their teams with the right tools and a proactive – not reactive – mindset that embraces AI innovations in network observability.
Troubleshooting automation
Immature and legacy tools clutter network observability, throwing up alerts without prioritization and event-based runbooks. To practitioners, it’s all noise – too many symptoms without definitive causes. Not all alerts are created equal. Unable to discern which to address first, teams find themselves stuck in triage. Issues linger, blind spots multiply, and network performance degrades.
Sometimes that degradation turns into an IT outage – costing businesses up to $2 million per hour – or leads to an equally costly security incident. These are not isolated pain points either; they’re systemic consequences of tool sprawl and complexity and without the intelligence to filter the noise, correlate the data, and highlight what truly matters, teams are going to be left reacting rather than preventing.
The research from EMA and BlueCat underscores this point. Eighty-seven per cent of NetOps teams say they use multiple observability tools, creating inefficiencies and fragmented insights, while only 29% of alerts are actionable, slowing incident response and adding to the stress of the teams.
These findings demonstrate why traditional approaches can no longer keep pace with the complexity of modern networks. Organizations need AI to make observability faster, smarter, and more effective; not simply automating existing workflows, but transforming how issues are detected, understood, and acted upon.
Automated remediation triggered by incidents
AI-driven automation makes network observability, and NetOps, more intelligent by distinguishing between the symptom and the cause faster than manual or basic automation. It also helps teams flag issues earlier, pinpoint root causes, and has the ability to trigger automated workflows. It can shrink the gap between symptom and cause, drawing from vendors, experts, and community knowledge to deliver actionable insights to NetOps teams. This accelerates incident response or, in some instances, provides teams with the tools to avoid an incident altogether. While automation strengthens incident response, predictive analytics has the capability to take observability to a new level.
Prediction, optimization, and capacity management
Ultimately, AI-driven predictive analytics are changing the way network performance is managed. By analyzing patterns and learning from historical and real-time data, AI can anticipate and identify potential network disruptions before they occur. This enables teams to take preemptive measures that reduce downtime, optimize capacity, and maintain business continuity.
Network resilience is no longer defined by how quickly teams respond, but by how effectively they anticipate. With AI, NetOps teams can shift from firefighting to forward planning – transforming network observability from a reactive exercise into a proactive strategy that strengthens business operations.
Anomaly detection further enhances this approach. AI and machine learning continuously monitor network traffic, learning what ‘normal’ looks like across environments and instantly flagging deviations that may indicate congestion, bottlenecks, or potential failures. Unlike traditional systems that react only after thresholds are breached, AI recognizes subtle changes in behavior as they happen, giving teams valuable time to intervene.
By combining predictive analytics and anomaly detection, organizations can maintain optimal performance and ensure that resources are used effectively.
Building a framework around intelligent observability
To get real value from AI-driven observability, however, CIOs need to strengthen the foundations that AI depends on. For example, AI can only surface accurate insights if the underlying data is clean, unified, and collected in real-time. Without these basics in place, even the best AI models will end up working with incomplete or inconsistent information, and consequently, yield poor results.
Strengthening these fundamentals ensures that AI isn’t working in isolation, but operating on a stable, unified, real-time view of the network. To do this, organizations should focus on two priority areas:
1. Consolidate and integrate
Right now, most NetOps teams use multiple point tools that don’t talk to each other. This fragmentation limits what AI can learn or automate. By consolidating or deeply integrating existing tools, organizations are able to provide AI with a unified dataset, which can significantly improve correlation, root-cause analysis, and automated remediation.
2. Elevate data quality and extend visibility
AI’s accuracy is only as good as the data feeding it. Extending visibility across cloud, edge, and on-prem environments, and shifting from periodic polling to streaming telemetry, can provide AI with richer, cleaner signals to detect anomalies earlier as well as predict failures more reliably.
Strategic guidance for CIOs
CIOs play a crucial role in building a framework around intelligent observability. They should prioritize empowering teams to find answers quickly rather than manually piecing together data from multiple systems. They should align network insights with business outcomes, user experience, and anticipate issues through proactive detection, rather than reacting after the fact.
When network health directly impacts revenue, productivity, and brand reputation, observability becomes a competitive differentiator that can no longer be treated as an afterthought.
Towards a more intelligent network
Many NetOps teams are already on the path to network observability maturity. Best-in-class enterprises conduct real-time performance monitoring and correlation across metrics and flows. Teams correlate raw-packet data with performance metrics and perform deep monitoring of software environments from SD-WAN to the cloud.
The ideal state is optimized and AI-driven – self-healing network that detects issues and resolves them without human intervention. We’re not there yet, but the trajectory is clear. Enterprises that embrace AI-driven observability will not only optimize network performance, they’ll future-proof their operations for the AI-driven enterprise era.
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