AIOps is saving enterprises operational costs and labor, but most IT teams are not ready to deploy and manage it.
Artificial Intelligence Operations (AIOps) promises to revolutionize IT services by using the power of AI to create self-learning programs that optimize the work of IT teams. These tools can save costs and labor for big businesses and they already have an early proven track record. OpsRamp’s report, The State of AIOps, released earlier this year found that 87% of AIOps deployments “are delivering value.”
Because of this early success, AIOps platforms are attracting more and more attention from enterprise IT teams. But as businesses begin adopting AIOps, they must be cautious to not bite off more than they can chew. There are legitimate concerns about the validity of AIOps when it comes to certain use cases, and businesses may not have access to the requisite IT skills to successfully implement them. CIOs should think carefully as they consider adding AIOps to their IT toolbelts.
AIOps support IT teams in two overarching ways — they automate mundane tasks that would traditionally busy up IT resources and they do work that is beyond the capabilities of human engineers. Within these two categories, there are several specific use cases that illustrate just how significantly AIOps tools can help enterprises.
One of the top use cases for AIOps tools is cybersecurity, where intelligent alert systems can streamline the process of detecting and addressing anomalous activity. Traditional alert management practices can be broken down into three steps: IT teams locate an issue, they prioritize a resolution, and they integrate the proper tools to fix said issue. While these are often simple steps in and of themselves, they can be extremely time-consuming for workers, and thus suck up valuable IT resources that come at a premium. AIOps delivers intelligent alerting by automating these tasks, which means humans no longer have to deal with the bulk of the alerts. And since the nature of alert management is time sensitive, automation can deliver faster threat detection and analysis.
AIOps is not just limited to security resources — it can also optimize storage and network management in both fundamental and advanced operations. AI’s application in enterprise storage ops has improved simple tasks like automating disk calibrations. Additionally, AI’s predictive analytics can preemptively adjust or add new storage volume capacities as necessary, so IT teams don’t need to spend time monitoring alerts when their disks near maximum capacity.
Outages and disruptions in enterprise network infrastructures are incredibly cumbersome to find and repair — AIOps can locate outliers in data infrastructure logs by pinpointing their source via advanced machine learning algorithms which helps IT teams perform a faster and more efficient root cause analysis.
The AIOps platform market currently sits at $2.55 billion and is projected to reach $11.02 billion by 2023 with a Compound Annual Growth Rate (CAGR) of 34.0%. Gartner predicts that by 2022, over 75% of organizations will use deep neural networks — which are driven by AI — in their business solutions.
These predictions are no surprise because there are already many tangible benefits to using AIOps — the three most substantive of which are: productivity gains from automating repetitive tasks, increased remediation process in root cause analysis, and overall stronger infrastructure performance. IT leaders are reporting that AIOps tools are improving mean-time-to-resolution of incidents by as much as 50% thanks to these services.
Although there is much to gain, there is also legitimate concern about the validity and technical ability to successfully bring AIOps into a dynamic IT environment. OpsRamp’s report found that that 67% of IT leaders are worried about data accuracy and 64% are concerned with the talent gap. In short, enterprises aren’t ready to reap all of the benefits just yet.
As of now, the decisions and recommendations produced by AIOps tools cannot be completely relied upon, and even if they could be, enterprises need to consider the strength and capabilities of their IT teams to manage them properly. Engineers need to gain expertise in machine learning — particularly in matters of incident analysis — to fully support functional AIOps deployment. Although many workers are trained to deploy AI in traditional use cases like customer support, infrastructure management requires more complex configuration — IT professionals should be cross-domain experts who are capable of explaining both the technical and business impacts of an issue.
Therefore, AIOps requires that IT teams invest in themselves as much as the tools. Unfortunately, 53% of enterprises take anywhere between six to twelve months to hire data science and analytics professionals. If you don’t have a big enough team to successfully adopt AIOps, it’s wise to hire a top-tier managed service provider like Turn-key Technologies (TTI).
TTI has three decades of experience helping businesses deploy enterprise-grade technologies and solutions. With a wide range of managed services offerings, we can help enterprises plug the skills gap needed for AIOps deployment and management. Enterprises may not be fully ready for AIOps, but if they want to test the waters, they’ll need IT help now more than ever, and there’s no better place to start than with a quality partner like TTI.
Please, rotate your device