Network admins and engineers have enough work on their plates, especially considering increasing numbers of access points amid the hybrid workforce. They’re also grappling with ever-more sophisticated cybersecurity threats across a highly complex network that now includes data centers, clouds and edge computing.
Yet, there’s little forgiveness from end users when there is network disruption leading to down time. High availability and low latency are crucial.
Artificial intelligence (AI) technologies — such as machine learning (ML), natural language processing (NLP) and enhanced automation — can provide relief for overstretched IT teams, while ensuring highly performing networks.
Factors that optimize the network
An AI-driven network enables IT teams to gain visibility and insights across the network, including the data center, wireless and wired local-area networks (WANs) and clouds.
To deliver full assurance, Hajela said two factors are critical. The first is a cloud-native architecture. This capability enables the disaggregation of network functions to remedy issues faster and reduce the risk of potential downtime.
Consider, for example, the CRACK virus. It took some companies a week and others months to remediate it. “We have some customers that were trying to remediate CRACK for years,” Hajela said. “With Juniper Mist, CRACK was globally remediated in less than eight hours.”
Thanks to Juniper Mist’s cloud-native architecture, the AI-driven network can more quickly identify and fix affected functions rather than network teams having to manually scan all parts of the network to find the problem.
The second critical factor is a “well-stocked AI toolkit,” Hajela said. It should include:
- Conversational AI to help with end-user network troubleshooting
- ML for network team support with capabilities such as root-cause analysis, throughput prediction, anomaly detection, threat classification, and more
- Deep learning with features including analytics and reinforced learning
Juniper Mist combines a cloud-native architecture with AI capabilities, enabling organizations to accelerate toward an AI-driven network.
So, where to start? A logical place is around reduction of network teams’ support and services workload by using AI in IT operations (AIOps) to accelerate troubleshooting, deliver data-driven insights and provide reliable, secure user experiences.
Organizations can achieve a quick win by implementing digital network assistants. For example, Juniper’s Marvis Virtual Network Assistant solution sits on the front line of the customer support team and uses conversational AI to handle incoming troubleshooting requests from end users.
Marvis has been trained to think like a human and respond to issues as though it had just huddled with the network team for answers, Hajela said. It is based on a continuous learning model, so Juniper customer feedback goes back into the Marvis engine for constant improvements.
Next, look to other AIOps solutions that save time and money with capabilities such as:
- The ability to solve problems before users notice. Discover how a clothing retailer reduced on-site tech visits by 85%.
- The provision of precision troubleshooting. For example, the service desk at a higher education organization is now able to detect when it takes a user longer than two seconds to join its network.
- Rapid deployment of solutions for greater return on investment. An AI-driven network with an automated Wi-Fi network configuration helped a healthcare organization deliver apps and services faster, reducing their capital expenditures by 20% and leading to a 20% to 30% reduction in operational expenses.
Juniper Mist AI provides all these capabilities and more to become an extension of your network team.
The company’s solutions help reduce network incidents, helpdesk tickets and on-site visits without the need for human intervention — ultimately saving network teams time and money, but also providing excellent user experiences.
Explore what Mist AI can do – watch a demo, take a tour of the platform in action, or listen to a webinar.
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