In our previous post, we explored stage one of the Self-Driving Network™ journey: data—the critical foundation for intelligent networks. But data alone doesn’t solve problems. Stage two is where we apply math and data science to extract real value from the data—transforming raw information into precise, actionable insights that change how networks are managed.
The value of insights
Traditional troubleshooting means sifting through cryptic CLI commands, log files, and manually checking devices—a process that’s slow, reactive, and frustrating. IT teams play catch-up, resolving issues only after users report them. Searching for an issue feels like looking for a needle in a haystack… with countless potential causes.
AI-Native insights eliminate this guesswork. Instead of hunting through logs and command-line outputs, IT teams can detect and resolve problems before users even notice them. The frustration of “putting out fires” and constant troubleshooting can disappear when the right data insights surface problems proactively.
Real-world impact
To gain trust and confidence in AI and the insights it provides, IT teams may need to see it to believe it. One of my favorite examples of this was working with a skeptical IT manager during a proof of concept (PoC). The IT manager doubted Juniper Mist AI™ could help manage the network or find issues they weren’t already aware of. During the PoC, devices could connect to the competitor’s network but not the Juniper network. Fortunately, with data insights we were able to discover that a non-Mist router had a misconfigured Maximum Transmission Unit (MTU), preventing authentication packets from passing.
In another case, Mist AI detected a faulty Ethernet cable at an employee’s desk—an issue no one had reported. At first, the IT team dismissed the alert. But when they checked with the employee, they discovered she had quietly switched to Wi-Fi because the wired network was unreliable. Mist AI had surfaced a problem the IT team couldn’t see.
The IT manager, initially a skeptic, became a believer after seeing Mist AI accurately pinpoint this and other issues.
This level of granular visibility is transforming IT operations across industries. Retailers like Gap Inc. now have deep insight into network health, enabling them to reduce trouble tickets by up to 90%. “Now, we can slice and dice the data and see very clearly that we’re having a problem at a specific store,” says Snehal Patel, Global Network Architect at Gap. Having store-level insights allows IT teams to act quickly, ensuring seamless connectivity for employees and customers.
Higher education institutions are also seeing significant benefits. At Dartmouth College, Mist AI ensures that every user on campus has an exceptional experience. “Even if only 2% of users are having a problem, we can resolve it on the fly,” says Mitch Davis, Dartmouth’s CIO. AI-Native insights mean IT teams no longer need to wait for widespread complaints; they can identify and address issues proactively, ensuring a consistently high-quality experience for students and staff.
Beyond visibility, Mist AI insights also help bridge the IT skills gap—an increasing challenge for organizations. For example, at Rady Children’s Hospital, AI is making network management more accessible. “We no longer need seasoned engineers trying to interpret a collection of loosely related metrics. Now we have a direct line of sight into a problem, from point A to point B,” says Daniel Madain, Network Engineer Team Lead at Rady. This shift allows highly skilled engineers to focus on innovation and strategic projects while less experienced staff can confidently manage daily operations. In a time when IT teams are stretched thin, this capability is particularly invaluable.
The journey continues
Insights are a game changer, helping IT teams solve problems faster and more efficiently than ever before. But the next step—turning those insights into recommendations—unlocks even greater potential. In our next post, we’ll explore how AI-Native recommendations are enabling proactive network management, bringing us even closer to a true self-driving network.
Where does your organization sit on this journey to the Self-Driving Network? Wherever you are, Juniper can help you take the next step.
Read the next post in this blog series.
Additional blogs in this series
A journey to the Self-Driving Network™ is built on trust in AI
