How AI is Reshaping Digital Infrastructure

Modern networks have outgrown traditional network monitoring. The rapid expansion of cloud-based applications, remote work, and decentralized operations has made managing networks more complex than ever. In response, businesses are turning to Network Intelligence, a new approach that leverages artificial intelligence (AI) to not only observe networks but actively optimize them. 

For industries that rely on real-time connectivity—whether in logistics, streaming services, or online marketplaces—network reliability is critical. A slowdown or outage can disrupt operations, causing revenue loss and frustrated customers. Traditional monitoring tools were built for a different era; one where networks were simpler, and troubleshooting was a manual process. As digital ecosystems expand, companies need a more adaptive, proactive approach to network management. 

From Network Monitoring to Network Intelligence 

Network monitoring has historically been reactive: engineers sift through logs, interpret cryptic alerts, and manually trace the source of problems. This approach often leads to delayed resolutions and inefficient troubleshooting. 

“Networks have become this complex organism of clouds, data centers, and SaaS applications, all connected by the internet as the new backbone,” says Christoph Pfister, Chief Product Officer at Kentik. “Managing that complexity requires more than just dashboards and alerts – it requires intelligence.” 

This shift is where Network Intelligence comes in. Rather than waiting for an issue to escalate, AI-powered systems continuously analyze network behavior, identify anomalies, and even predict potential failures before they happen. 

AI’s Role in Network Optimization 

The integration of AI into network management has transformed how businesses operate. AI-driven systems can: 

• Detect anomalies in real-time: AI recognizes unusual patterns in traffic flow, helping teams address potential outages before they escalate. 

• Predict network issues: Machine learning models analyze past incidents and forecast possible future disruptions. 

• Optimize performance automatically: AI can recommend (and, in some cases, execute) changes to improve network efficiency. 

Take, for example, cloud-based applications that require large amounts of data to be transferred across different regions. An AI-powered network intelligence system can detect inefficiencies—such as unnecessary routing delays or misconfigured traffic paths—and suggest ways to optimize data flow. These adjustments reduce costs, improve performance, and free up engineers for higher-level problem-solving. 

A New Era: AI as a Digital Network Engineer 

As AI capabilities evolve, businesses are moving beyond simple automation toward what some call “agentic AI”—AI that can act as a digital network engineer. Instead of just identifying problems, these AI systems are learning to make decisions and take corrective actions within predefined guardrails. 

“We’re imagining a world where network insights are no longer limited to network experts,” says Pfister. “Where AI augments engineers, increasing their productivity and reducing troubleshooting time.” 

A major step in this direction is natural language interaction with network data. Traditionally, engineers needed deep technical expertise to query and interpret network logs. Now, generative AI tools allow teams to simply ask the system a question—such as “Why is traffic latency high in this region?”—and receive a precise, actionable response. 

For global companies, this also means overcoming language barriers. Some teams are now using AI-driven network intelligence in multiple languages, allowing engineers to diagnose and resolve issues without needing to be fluent in the system’s primary programming language. 

Beyond Automation: The Future of Network Intelligence 

While AI-driven networking is still developing, businesses are already seeing significant efficiency gains. Companies that previously relied on manual investigations are now resolving network issues in a fraction of the time. 

One major shift is the ability to delegate network troubleshooting across teams. Instead of overwhelming IT departments with routine network inquiries, companies are integrating AI-powered chat systems that allow non-technical employees to ask simple network-related questions and receive instant answers—without needing direct intervention from engineers. 

Still, full automation remains a gradual process. While AI can suggest fixes and predict future issues, many organizations prefer to keep a human in the loop, ensuring that AI-driven changes align with broader IT strategies. 

The Competitive Advantage of Network Intelligence 

The evolution from network monitoring to network intelligence is more than just an upgrade—it’s becoming a necessity for businesses that rely on always-on connectivity. Whether managing e-commerce platforms, cloud-based collaboration tools, or global logistics networks, companies are realizing that a proactive, AI-driven approach to network management is no longer optional. 

As the industry moves forward, the focus will be on how AI can work alongside engineers, rather than replacing them. The ability to process vast amounts of data, provide real-time insights, and automate routine tasks will redefine what it means to manage a modern network. 

“Network Intelligence isn’t just about solving problems faster,” Pfister adds. “It’s about making network management more accessible, more efficient, and ultimately, more intelligent.”