According to Revolutionizing Network Management with AIOps, a research report conducted by Enterprise Management Associates (EMA), 91% of experts believe that AIOps-driven network management can lead to better business outcomes for their enterprises.
Additionally, nine out of ten experts believe that AIOps can address many of the shortcomings of their existing network management solutions. They are also enthusiastic about their ability to automate much of their networks and to streamline operations with this technology.
EMA explains that AIOps is an abbreviation of the phrase "artificial intelligence for IT operations." AIOps combines machine learning and artificial intelligence algorithms with big data and other technologies to enhance IT management. This technology can find patterns in IT data, infer insights and draw conclusions from those patterns, and communicate this knowledge to IT management.
EMA has observed robust AIOps development within the networking industry over the last few years. Network infrastructure vendors and network management vendors have developed homegrown AIOps technologies to enrich their solutions by training them specifically for network management use cases. Moreover, EMA research has detected strong interest among enterprise IT organizations in using this technology.
"IT organizations expect significant returns on their investments in this technology. Enterprises that apply AIOps to networking are able to optimize their infrastructure, reduce operational overhead, and improve security," said Shamus McGillicuddy, VP of Research, covering network management at EMA.
Enterprises need to be aware, however, that AIOps-driven network management has plenty of room for improvement. Only 30% of enterprises have been fully successful with this technology so far, also found in the survey. They want to see vendors advance and mature their capabilities, particularly around predictive analysis, root-cause analysis, network baselining, and anomaly detection. Ultimately, network management organizations have a lot of work ahead of them if they want to realize the full potential of AIOps.
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