A typical business network is a mind-boggling collection of complex technologies, comprising hundreds of servers, routers, switches, firewalls, load balancers, endpoint devices and other boxes, thousands of applications, miles of cabling and multiple layers of networking protocols. Managing it all through manual processes and human intervention is no longer practical.
AIOps platforms use AI disciplines such as machine learning, natural language processing and advanced analytics to evaluate and act upon vast amounts of telemetry data generated by networked devices. After correlating and finding patterns within data streams, an AIOps platform can often trigger automatic responses that address problems in real time.
According to a recent study from ZK Research and Masergy, 65 percent of companies are already using AIOps, and 94 percent say that AIOps is “important or very important” for managing network and cloud application performance. In addition, 84 percent said AIOps creates a path to a fully automated network environment, and 86 percent expect to have a fully automated network within the next five years.
While AIOps has the potential to be a game-changer, it can be tough to get right. In a recent Enterprise Management Associates survey of companies using AIOps, more than half said it was “challenging” or “very difficult” to implement. Some of the chief challenges include:
- Fuzzy strategy. Some companies implement the solution before adequately identifying the problem they wish to solve. You don’t want to adopt AIOps and try to figure out all the ways you can use it later. You should have a clear idea of what you want to accomplish and why that will bring business value. It’s best to start with a small project with clear-cut objectives. Gartner says companies should "prioritize practical outcomes over aspirational goals by adopting an incremental approach.”
- Integration complexities. Most organizations operate with a mixture of legacy on-premises systems and newer cloud and virtualized resources. Interoperability issues can make it difficult to synchronize data across infrastructures. Moving data between cloud and on-premises systems can be a time-consuming and error-prone process. It often requires opening ports on the corporate firewall or setting up complex tunneling protocols.
- Poor data quality. As the old saying goes: “garbage in, garbage out.” An AIOps system can’t accurately identify or address problems if it’s working with poor or incomplete data. Many organizations have too many different data sources with inconsistent data formatting. According to an IDC report, more than half of companies with stalled AI projects cite data quality issues as the main problem.
- Cultural barriers. Like it or not, many people remain distrustful of AI-powered technologies — in the EMA survey, “fear or distrust of AI” ranked among the top five barriers to adoption. Even those comfortable with the technology may resist changing processes and adopting new tools.
Despite the challenges, AIOps offers too many important benefits to be ignored. As network infrastructures become increasingly complex, AIOps can deliver substantial competitive advantages through increased automation and improved efficiency. Contact us to learn more about developing an AIOps strategy for your organization.