The practice of applying artificial intelligence and machine learning (AI/ML) algorithms to IT management has gained mainstream acceptance over the last few years. Many vendors and IT organizations embraced the acronym AIOps (artificial intelligence for IT operations) to denote this phenomenon. Whether called AI/ML or AIOps, these technologies are gaining traction and delivering value.
Many IT organizations apply AI/ML and AIOps technology across domains, correlating insights from the various layers of IT infrastructure and operations. However, Enterprise Management Associates (EMA™) has observed significant interest in applying these AI technologies narrowly to network management, according to a new research report, titled AI-Driven Networks: Leveling Up Network Management with AI/ML and AIOps. Networks are notoriously complex, and network management is rife with manual processes and tool sprawl, making networks a fertile domain for AI-driven transformation.
Previous research from EMA confirmed that IT organizations recognize the potential value of AI-driven network management. Two years ago, 90% of IT professionals told EMA that the application of AIOps to network management could lead to better business outcomes for their companies. EMA's new research found this optimism ticked slightly to 92% today.
AI/ML technology can streamline, enhance, and automate many network management processes. IT organizations are recognizing the potential benefits of improving overall network performance, agility, and security, especially with solutions from network management and network infrastructure vendors. Even emerging, general-purpose tools like ChatGPT can offer value to network teams.
The key hurdle is to translate into reality this belief that AI-driven network management can improve business outcomes. IT organizations must learn how to evaluate and implement this technology. Then, they must convince potentially skeptical technical personnel to use it. Two years ago, only 41% of IT organizations believed they were fully effective at evaluating the AI/ML and AIOps technology they were considering for network management use cases, and only 30% considered themselves completely successful in their overall engagement with the technology at that point.
Two years later, with this new report, EMA is revisiting the topic of AI-driven network management to explore whether the technology has matured and whether IT organizations have found success in using it. This report examines the experiences of individuals who are currently engaged with AI/ML and AIOps solutions aimed at network management.
Some of the key findings include:
■ Only 36% of organizations have been completely successful with managing their networks with AI/ML and AIOps technology, up from 30% in 2021.
■ Network optimization, automated troubleshooting, intelligent alerting and escalations, and vendor management are top-priority use cases for AI/ML.
■ Nearly 69% of organizations saw at least some improvement in overall end-user experience since applying AI/ML technology to network management.
