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Artificial Intelligence (AI) has become a buzzword across nearly every sector of enterprise IT—and networking is no exception. But beyond the hype, what does AI really mean when applied to networks and client devices? And how can IT teams begin to unlock its potential?
This blog breaks it down.
What Is AI in Networking?
AI in networking refers to the use of machine learning (ML), pattern recognition, and data analytics to automate, optimize, and improve how networks perform and how issues are resolved. Rather than simply collecting telemetry and waiting for humans to act, AI enables systems to analyze trends, detect anomalies, predict future problems, and even recommend or take corrective action.
Key benefits include:
- Faster troubleshooting and root cause analysis
- Predictive maintenance and issue prevention
- More efficient use of IT resources
- Improved user and device experiences
AI for Network Infrastructure
Most major vendors—like Cisco, Juniper Mist, and Aruba—have added AI capabilities to their infrastructure management platforms. These often include features like anomaly detection, automated policy tuning, and smart alerts based on controller or access point telemetry.
But there’s a catch: these insights are often limited to what the infrastructure can see. This “inside-out” perspective leaves gaps, particularly at the client edge where the user experience actually happens.
AI for Client and Endpoint Visibility
That’s where AI-powered platforms like 7SIGNAL come in—offering an “outside-in” approach. By placing lightweight software agents on devices and using vendor-agnostic sensors, independent platforms can collect high-fidelity data from the user’s point of view. Then, AI engines analyze that data to uncover:
- Suboptimal roaming decisions
- Driver and OS issues
- Poor RF conditions not visible to infrastructure
- Application-level impact and user experience degradation
This device-centric view is crucial in today’s hybrid, distributed environments where users are connecting from anywhere, on any device, over networks the IT team doesn’t always control.
Feeding the AIOps Engine
AI becomes even more valuable when paired with AIOps platforms. Independent data from client and RF monitoring can be piped into tools like Splunk, Moogsoft, or ServiceNow to drive smarter workflows and self-healing automation. For example, if a user’s device consistently connects to a weak AP despite stronger alternatives nearby, AI can suggest a driver update, AP tuning, or policy change—before the helpdesk ticket is ever created.
Start with the Right Data
AI is only as good as the data it’s fed. That’s why the first step to successful AI-powered networking isn’t more dashboards or promises—it’s visibility. Getting objective, real-world telemetry from the client’s perspective enables AI to deliver actionable insights instead of guesswork.
In the end, AI for networking and clients isn’t about replacing humans—it’s about augmenting them. It gives IT teams the context, clarity, and confidence to optimize networks proactively, not reactively.