As networks grow increasingly complex and distributed, the benefits of deploying artificial intelligence (AI) technology are becoming increasingly evident. In short, AI is poised to fundamentally change the way networks are monitored and managed.
A key AI benefit is rescuing skilled network teams from routine and mundane tasks. "AI can help monitor the health and configuration of the network, identifying anomalies and potentially taking corrective actions automatically," says Marc Herren, network advisory director with the technology research and advisory firm ISG, in an email interview.
More importantly, the emergence of software-defined WANs (SD-WANs) is opening the way for network managers to integrate AI technology into network operations and management. "For the industry to deliver on the promise of a self-healing or self-correcting WAN, AI tools can help automate routine network operation tasks, set policies, measure network performance against set targets, and respond to and rectify the networks as needed," Herren explains.
AI can also make snap decisions to remediate a variety of serious network issues. "Although human operators can more effectively triage complex and multi-step problems, AI is a powerful tool that can supplement the work of network engineers to add robust controls and automation to mature networks," says David Brauchler, a principal security consultant with cybersecurity and software assurance services firm NCC Group via email. "AI should be considered an addition to a company’s network team rather than a replacement, accelerating the work of engineers and creating new efficiency improvements to developed workflows."
Getting started with AI-based network management
When laying the groundwork for AI network management, it's necessary to understand the network's infrastructure, devices, and connections and to evaluate data sources and data flows, says Portia Crowe, chief data strategist with Accenture Federal Services' defense and applied intelligence unit in an email interview. It's also important to understand your team's talent level and AI expertise. "Exploration and implementation on a small scale will get you started and provide an ability to iterate and learn, as well as to capture metrics to help with scalability," she advises.
