AIOps is a powerful tool, and although it will require enterprises to acquire new skill sets and remodel their IT teams, it won’t replace human engineers.
As enterprises grow their operations and gather increasing amounts of data, IT teams can struggle to keep up. Enterprises are increasingly reliant on network automation to handle a wide variety of IT operations. And Algorithmic IT Operations, or “AIOps,” represents the latest iteration of smart tools for managing the world’s increasing influx of data.
Over just the past few years, artificial intelligence (AI) has become integral to many IT operations. IT job postings are increasingly calling for data scientists with AI skills, and researchers predict that in 2019, a full quarter of global enterprises will rely on AIOps. However, IT teams may be hesitant about how best to adapt to a tool that at first glance might seem to put their jobs at risk.
The term “AIOps” was coined by Gartner Research in 2016, and refers to the use of “smart,” automated algorithms that perform work typically done by DevOps engineers. These tools use machine learning (ML) to continually adjust and optimize IT operations by capturing and analyzing huge amounts of data.
AIOps is being billed as a major time-saver for IT departments, enabling DevOps to scale their workload by allowing ML to monitor system health and determine which anomalies require a response and which are just ‘noise.’
AIOps doesn’t just automate tasks — by combining ML with Big Data, it can actually make decisions and continually optimize IT processes to improve system health. Using predictive analytics, AIOps can proactively address and prevent system issues before they happen, learning to expect and adjust for changes in availability and performance. Whereas a human engineer might overlook difficult-to-detect cybersecurity threats, AIOps can decipher nearly invisible anomalies in user behavior and go beyond simply alerting the appropriate engineers by taking steps to address the problem directly. ML algorithms can also be used to diagnose issues, generate insight into root causes and keep track of tickets for future reference.
Because the system possesses organic learning abilities, AIOps algorithms can improve over time, reducing cost and time requirements by adjusting to the specific needs of each individual organization. Ideally AIOps’ platforms sidestep the need for human intervention, while still providing enough transparency so that an engineer can step in should the need arise.
As network complexity increases, algorithmic learning will be increasingly useful in managing IT operations that would otherwise mean information overload for a human engineer. Platforms like HPE’s Operation Bridge can learn to manage IT relationships across hybrid systems, analyzing data from traditional, virtual, and cloud computing platforms. Crucially, AIOps can (with some effort) be integrated with third-party tools, providing comprehensive and accurate pictures of these complex networks — even while maintaining speed and security.
More and more data is being generated daily — by computers, smartphones, and the many devices that comprise the increasingly ubiquitous Internet of Things (IoT). Businesses may want to use data to improve operations, however analyzing patterns in that data may exceed the capabilities of their IT team. AIOps can ideally aggregate that information and provide diagnostic or predictive insights much more quickly. The result is simple: faster, more resilient networks and ultimately better outcomes (like, for example, more responsive customer service).
So will AIOps replace IT teams? No — those jobs are safe. However IT teams will need to augment their skill sets and in many cases adjust the team makeup. In practice, AIOps may significantly reduce the amount of ‘noise’ an IT team has to deal with on a daily basis. Even a 25% reduction in alerts — a realistic goal for this new technology — could represent major savings. That said, there is no situation in which AIOps represents a 100% fix, as the system requires both intelligent data input and human supervision.
IT teams will need to understand how exactly their AIOps tools are making decisions, which in turn requires a firm grasp of the tool’s algorithms. Many AI-generated insights and alerts will need to be approved or adjusted, often based on information that is simply outside of its scope — think of how many anomalous or one-off events require such major adjustments. In addition, AIOps, while intelligent, is far from error-free. One account from UbiSoft suggested that AIOps can in some cases generate as many as 30% false positives. Clearly DevOps will still be needed to provide management and accountability.
Implementing AIOps solutions can represent a major investment, and some businesses may be skeptical of the hype, aware that the reality of the technology may not deliver on its promise. Organizational hesitancy to this new technology may arise from embedded company cultures, budget restraints, and an unwillingness to recalibrate IT departments.
Despite these hurdles, AIOps remains the way of the future, and ultimately a necessary investment for businesses looking to remain competitive. Gartner Research suggests that by 2020, up to half of global enterprises will use AIOps, and it’s easy to see why: even a partial increase in automated analysis can incur substantial cost savings not to mention crucial data-driven insights. Stakeholders will increasingly need to be able to make near-immediate decisions in order to maintain their competitive edge.
And yet fewer than 10% of organizations have implemented AIOps solutions, as businesses struggle to understand how the switch will impact their operations. With the significant retraining or hiring that AIOps adoption can require, it may make sense to work with a managed services provider that can give your IT department access to the latest technologies at a fraction of the cost.
With nearly three decades of experience designing, deploying, and managing increasingly complex enterprise networks, Turn-key Technologies (TTI) has the expertise necessary to help companies determine if, when, and how to implement AIOps solutions. As enterprises begin to explore how AIOps can help cut costs and free up their IT teams to work on higher-level issues, those that partner with networking experts like TTI can rest assured that they’re in capable hands.
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