AIOps Market Outlook – Trends and Applications on the Horizon

Artificial intelligence for IT operations (AIOps) continues to transform IT operations management (ITOM). AIOps provide the capabilities necessary to manage the increased complexity and massive scale of the infrastructure that delivers the services your business depends on.

If you are reading this blog, there is a fair chance you already know something about the AIOps market or the Optanix Platform. If not, here’s a short primer that answers the question, “What is AIOps?” to get you started.

AIOps Market Trend #1: Superior Service Delivery

AIOps is a means to the end. The end in this case is the outcome resulting from the effective use of AIOps in ITOM, which is the superior delivery of applications and services to customers, employees and other end users. Any discussion on the trends and applications around AIOps needs to be viewed from the perspective of how it eventually delivers on this promise.

A good place to start is to look at the strategic trends driven by emerging technologies and the manner in which they are being used.

End users these days expect an immersive interaction with the applications they use. This interaction is multi experiential and drives the need to provide multiple pathways that deliver a cohesive experience. They expect all this to happen efficiently without unproductive, unpleasant and distracting delays.

A review of any of the usual analyst reports will list a slew of technologies that enable these outcomes. Many of them are particularly relevant to this discussion. Before exploring how they affect future AIOps market trends and applications of AIOps, let’s take a look at some key ones:

• Edge Computing: Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. From an infrastructure perspective, this means that computing resources need to be located closer to the user, leading to the distribution of intelligence and data storage/processing to the edge of the network. The result is reduced latency and processing times, which leads to a much faster response time for the user.
• Distributed Cloud: Cloud computing provides the core around which the edge coalesces. The latest trend is the move away from centralized data centers and cloud providers to a distributed environment. Gartner describes the distributed cloud as the distribution of public cloud services to locations outside the cloud provider’s physical data centers, but which are still controlled by the provider. The benefits once again are reduced latency and superior responsiveness when users interact with applications and services.

At the end of the day, both edge computing and the cloud directly enable the kinds of behavior end users expect from today’s applications and services. They do this in a manner that is best characterized as distributed – essentially moving necessary resources and capabilities closer to the user. Computing, storage, analytical and automated behaviors are no longer just centralized – there is a distributed element that is also in play.

This trend to move intelligence away from the core into the boundary of contact with the users is one of the key trends driving the future direction of the AIOps market.

AIOps Market Trend #2: Actionable Insights

The primary AIOps benefits lie in the generation of actionable insights into the state of the infrastructure and the ability to act upon them. When looked at with the perspective driven by an understanding of edge and cloud computing, there are some definite outcomes that can be determined:

• Automated Decision Making: AIOps platforms excel at gleaning insights. Based on these, they provide the ability to automate forms of decision making such as remediation. Today, this functionality needs to be decentralized and deployed where it is needed along the edge, to react faster to the needs of the network.
• Service Infrastructure Management: AIOps platforms now need to provide a cloud-based management layer that allows infrastructure and operations (I&O) teams to address problems across both the centralized and localized resources in an efficient manner. The fact that resources today are not necessarily centralized means that increased security is needed along the entire footprint of the distributed estate under management.
• Data Collection, Aggregation, and Enrichment: The AI benefits of AIOps depend on the ability to apply machine learning and analytics to large data sets. Typically, this kind of data would be gathered from across the managed infrastructure, then aggregated and enriched to provide a basis for analysis.

AIOps platforms today must move to a model where analysis is conducted at both a centralized location as well as at nodes along the edge. This necessitates the need to have a sound data management strategy that determines what kinds of analyses are performed closer to the edge, and what needs to be done at the center and then disseminated.

The future trends and applications of the AIOps market all lead towards a hybrid approach that combines both centralized and distributed processing of the monitoring, intelligence, automation and storage capabilities needed. The result will be an overall improvement in the latency and performance of the infrastructure that delivers business services.