Avi Freedman is CEO of network analytics company Kentik.

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The wave of retirement within network engineering should worry anyone who understands how modern systems operate. Network engineers are critical for maintaining the core infrastructure that keeps enterprise operations running, and yet many businesses face a significant talent shortage as seasoned professionals leave, retire or pivot to other roles.

One report highlights that about a quarter of U.S. network engineers are preparing to retire in the coming years, intensifying this talent crisis and threatening the stability of organizational networks. This talent vacuum presents a major challenge for organizations trying to keep their networks secure, reliable and efficient. The risks include increased vulnerabilities, longer outage durations and reduced performance, all of which have serious business implications.

This is where AI can play a major role. We can make every engineer a network expert if we democratize the understanding of networks and the ability to operate them. AI is allowing observability vendors to find issues proactively, diagnose and explain them proactively and interactively, and recommend fixes.

Network Intelligence And Super-Powered Engineers

Network intelligence is about transforming streams of telemetry about traffic, latency and network device metrics into real time, meaningful information that even non-experts can understand and act upon. Through AI that deeply understands the networks—its workflows, challenges, costs and limitations—engineers with even a minimal understanding of the network can detect anomalies, security threats and performance issues long before they escalate.

This goes beyond identifying traffic patterns and visualizing data. Network intelligence adds capacity to your team by acting as a trusted network guide: suggesting the right questions, surfacing answers and offering next steps. For example, it can proactively watch performance test results, identify cases where links are overloaded and provide recommendations for rebalancing traffic to restore performance.

The Talent Gap In Network Engineering

The reality is that network engineers possess specialized skills honed over years. As many experienced professionals retire or leave, organizations face a widening skills gap. I’ve seen firsthand how the implications extend beyond operational efficiency—security becomes more difficult to manage, and business agility is constrained.

It’s also worth noting that the network security implications of the Great Resignation could leave organizations more vulnerable to cyber threats. That makes it critical to rethink how we manage networks in this new era.

Traditional manual troubleshooting and configuration are no longer enough. Instead, we need a new approach that empowers more team members, regardless of their technical background, to maintain and troubleshoot network issues quickly and effectively. In multiple enterprises, we have seen help desks now enabled to do second-level network diagnosis, and expect to see this quickly progress to fixes and remediation.

What Network Intelligence Brings To The Table

The core of modern network intelligence is high-fidelity, contextualized telemetry, connected to a modern observability platform and driven by agentic AI and machine learning to drive high-quality automation, insights and workflows that improve operations.

When I communicate this to our customers, I emphasize that by collecting extensive telemetry from all network devices and the internet, applying AI that groks network processes and parameters and making those accessible to an agentic framework, companies can innovate faster, confidently scale applications and avoid operational risk even as teams (and budgets) shrink.

Natural language interfaces are also central to this transformation. They allow teams, whether they are network experts or not, to ask simple questions and receive meaningful, immediate answers. This democratization of data can significantly shorten resolution times and reduce reliance on scarce technical experts. It’s an essential step in building resilient, scalable networks with fewer dedicated network engineers.

The key benefits of network intelligence include:

• Faster Problem Resolution: Automated insights from AI and telemetry drive down the mean time to repair (MTTR), often catching issues before users even notice them.

• Bridging The Skills Gap: Natural language queries democratize network data, empowering broader teams to troubleshoot and resolve problems independently.

• Cost Savings: Automating monitoring and diagnostics cuts operational costs and accelerates deployment, freeing up experts for strategic tasks, like capacity planning, that improve ROI.

• Enhanced Security And Reliability: Continuous, high-quality telemetry enables early detection of threats and anomalies, helping prevent breaches and outages.

Looking Forward

I believe that the future of networks is in autonomous, nearly self-healing systems that can adapt and respond intelligently. Organizations that prioritize and invest in network intelligence could have a distinct advantage in the AI era. The ability to respond proactively to changes in the network—from customer experience and cost to security—is the hallmark of successful digital infrastructure, and the bar for success is rising more rapidly than ever.

Building this future requires us to rethink traditional network management. By building AI that truly understands network infrastructure, I believe we can develop networks that are not only resilient and secure, but also accessible to a broader range of team members.

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