The future of AIOps: Not if, but when?

Traditional, siloed IT operations lack the ability to provide a correlated enterprise-wide view across domains, resulting in greater mean time to resolution (MTTR) for incidents. In fact, a recent study found that the average MTTR in such cases is 2.2 hours. Enterprises incur an average loss of $72,000 for every minute of outage in IT services. In other words, mistakes are expensive.

Businesses typically need about six employees to address each incident. This heavy reliance on people and tacit knowledge increases the risk of human error. As massive amounts of data flow into IT operations systems, businesses are struggling to detect, diagnose and resolve critical issues and are unable meet their IT goals.

SEE ALSO: Facebook AI open sources a chatbot with empathy and personality

The COVID-19 pandemic has exacerbated this problem as more people are working from home; in fact, Time Magazine recently deemed this “the world’s largest work from home experiment.” For the IT professionals and teams now working remotely, this has especially led to a sea change in how things are done. The way IT staff collaborates to triage an incident is being redefined, with technologies like AIOps being used to help with proactive management rather than reactive. The silver lining in all of this is that by being forced to adjust to the “new normal” of remote work, this situation will also accelerate the adoption of new technologies that can increase resiliency. Too often there has been a singular focus on efficiency to the detriment of resiliency, but the reality is there must be a balance between both.

The rise of AIOps

It’s clear that IT teams need help. An article from analyst firm Gartner, Inc. noted that the rapid growth of data is challenging for IT Ops to capitalize on. There’s simply more data being produced than humans alone can possibly grapple with. Businesses want to meet customer demands and are implementing changes rapidly, but they are in serious need of agility.

This scenario is just right for assistance from AI to turn IT Ops into AIOps. With its perfect blend of AI and automation, AIOps acts as an enabler for IT agility, thus allowing ease of change in the enterprise IT landscape. Through proactive and autonomous IT operations, AIOps allows IT operations teams to focus on other strategically important activities – which, in turn, translates to business agility.

Gartner defines AIOps as a combination of Big Data and machine learning functionalities used to automate IT Ops processes. More businesses are turning to AIOps to prevent, identify and resolve high-severity outages and other IT operations problems. By ensuring zero downtime of critical applications through autonomous incident resolution, AIOps helps minimize revenue risk for enterprises through improved business availability and efficiency. Organizations reduce IT costs and maximize profits.

AIOps will continue to rapidly grow in adoption in 2020

Data continues to proliferate in unprecedented ways, with no sign of slowing down. In fact, IDC predicts the global data sphere will reach 175 zettabytes by 2025. There’s just too much data for humans to functionally or logistically manage, which is where AI and ML come in. Ninety-one percent of respondents to a recent survey from the AIOps Exchange said they were looking at machine learning-powered tools to help IT operations teams be more productive.

Growth is coming slowly but steadily

While it’s only a matter of time before AIOps gains mainstream adoption, scepticism prevails among customers when it comes to embracing this new approach. Part of the reason why you don’t see an immediate explosion yet (in terms of adoption of AIOps) is because most of the enterprises are taking time to evaluate the readiness of their organization and technology to ensure they can adopt these new technologies seamlessly.

Gartner predicts that large-enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. That’s a significant leap in the next three years. But to transition effectively to AIOps, organizations need four things:

1. The right tools in place
2. Deep knowledge of their data sources
3. A technology stack that is fairly current
4. The ability to observe/monitor

SEE ALSO: What does Serverless have in common with Nutella?

Making AIOps work

The flow of data these days seems to exceed the speed of light and is definitely exceeding human capacity to manage, monitor and analyze. This threatens the proper functioning of IT Ops, which then threatens the business that relies on it. Outages are expensive, and the risk of human error runs high, necessitating a different solution.

AIOps provides the intelligence coupled with automation that enterprises need to effectively manage and glean from the velocity, variety and volume of data coming into their IT systems. But organizations need to prepare wisely before transitioning to AIOps, making sure the correct knowledge set and the correct tools are in place. As scale, complexity and change continue to increase, AIOps is no longer an option but a necessity.

The post The future of AIOps: Not if, but when? appeared first on JAXenter.