CIOs are constantly under pressure to do less with more. However, when IT is already overwhelmed with trying to roll out new digital services and keeping existing business-critical apps and infrastructure up and performing optimally, CIOs need to look at new solutions for streamlining operations to free up both their people’s time and capital for innovation.
That’s why more IT leaders are evaluating AIOps to solve their biggest challenges. In fact, IDC predicted that 70% of CIOs will aggressively apply data and AI to IT operations, tools, and processes by 2021 (Source: IDC). After all, a few quick and dirty algorithms is all it takes to analyze your worldwide IT operations and guarantee to save you tens of millions of dollars, right?
Unfortunately, hype like that leads to disillusionment. When shiny new objects don’t live up to overblown promises, they end up abandoned. In the case of AIOps, that would be too bad, because the reality of AIOps can be better than the hype.
But if AIOps doesn’t work miracles, then why should CIOs care?
AIOps puts machine learning and data analytics to work in several different contexts to enable simpler, faster, and more efficient IT operations management including:
- Application performance monitoring: Taking a service-aware, user-centric approach to application performance.
- Behavioral learning (dynamic baselining) and predictive event management: Understanding which issues are likely to become actual problems so IT knows what to fix before they impact users.
- Probable cause analysis: Correlating related events and anomalies so IT can troubleshoot more accurately and address root causes more quickly.
- Log analytics: Using day-to-day log files to make the system even smarter about baselines and anomalies.
We all know that digital business requirements and infrastructures are becoming more complex, so IT needs to respond more quickly, efficiently, and accurately. However, the ability to do so far exceeds human scale.
AIOps enables IT to move from rule-based, manual management of analysis to machine-assisted analysis and machine learning systems. This is required not only because of limits to the amount and complexity of analysis human agents can achieve, but also to enable a level of change that hasn’t been possible without the help of artificial intelligence.
Take Park Place Technologies for example — they are the world’s largest post-warranty data center maintenance organization, helping thousands of customers manage their data centers globally. When customers have performance issues, costs escalate for Park Place if manual triage is required. To improve customer satisfaction and drive uptime, Park Place decided that an AIOps platform was the right solution for automating the support process, and began their implementation with 500 customers, with plans to support more.
“Using AIOps helps us move from a reactive service model to proactive, and ultimately to predictive. We’re able to see signs that there’s an impending failure and remediate it before it happens, really saving our customers a lot of downtime,” said Paul Mercina, Director of Product Management, Park Place Technologies.
Since applying its AIOps solution, Park Place has reduced the number of times a ticket is touched by a human by 80 percent. Not only that, but the AIOps solution also allowed them to automate the triage process and optimize the customer experience — in fact they’ve experienced a 10 percent faster time to resolve incidents by leveraging advanced anomaly detection.
IT service providers may also use AIOps to create a holistic monitoring strategy that brings an extra level of predictability to the IT environment by combining data, events, logs, and metrics from many sources. By applying machine learning and advanced analytics, the behavioral learning capabilities gained from AIOps can help identify patterns and suppress any events that fall within bands of normalcy. This can result in a reduction in event noise and in turn reduce the number of tickets per month, saving IT service providers significant time and resources.
In addition to these use cases, AIOps can centralize the way organizations handle and respond to IT incidents at multiple locations around the world. This is especially important in industries like manufacturing where a latency problem affecting communications to its distribution centers can lead to delays in production. In this scenario, AIOps can generate insights and identify problems affecting operations between facilities and send automated notifications when a problem has been identified. This helps to not only increase incident response times, but better resolve issues when they arise.
These are just some of the many ways enterprises are using AIOps today to avert problems, cut costs, improve customer experience, and free IT staff to work on the innovations their organizations desperately need. It might not work miracles, but it does something even better: AIOps elevates the strategic importance and visibility of IT to the business by delivering the performance and availability needed no matter how complex environments become. Although its current use cases might not be garnering headlines worldwide, AIOps’ proven results make its reality better than the hype.