The job of an Ops team has never been more important - or more challenging - than it is today.

Firstly, operational settings are becoming more complex, as businesses seek to adopt new capabilities, and in many cases, work towards a digital transformation strategy. Intricate workflows for a growing number of applications are increasingly sprawled across disparate cloud and on-premises environments.

A new consumer application, for example, may reside in the public cloud, but the data it needs to function could be stored in multiple places across a mixed and hybrid cloud environment. These increasingly expansive settings also create unprecedented levels of operational data, often leading to opaque alert storms that can make it harder still to pinpoint the source of a problem if and when it arises.

Secondly, pressures to resolve operational issues are greater than ever before. Gone are the days of overnight upgrades and downtime as standard. People expect round-the-clock access to digital services, both in their working lives and their consumer lives. Businesses and their customers are now highly dependent on digital services and applications; any interruption can be catastrophic. To take just one example, during Black Friday in 2018, Amazon lost $99 million in sales from an outage which lasted just one hour. So for operations, the stakes have never been higher.

And thirdly, despite the importance of their work, Ops teams may often find themselves squeezed for resources. Operational budgets, resources and team sizes are all either shrinking or not growing quickly enough to match the increasing complexity of the systems and data volumes involved. For example, as user counts have been climbing for most companies, the average operating department now faces less budget per user than in 2018. In other words, these teams must manage more complex environments, higher risks, larger volumes of data, and with fewer resources.

AI Ops has the answer

Fortunately for Ops teams, there are a variety of tools available that leverage artificial intelligence (AI) to help make sense of this predicament - tools often grouped under the banner of ‘AI Operations,’ or AI Ops for short. While it’s easy to be skeptical of any new IT jargon, the term has been around for a while – and it’s far more than just a buzzword.

AI Ops tools use AI to help users consume and analyse infrastructure and application data at scale. This enables Ops teams to benefit from clear, actionable insights into how and where changes should be made when (or before) performance issues arise.

There is a wide variety of AI Ops tools in the market, each of which help teams manage their operational environments in a different way. For most businesses, the challenge will be in ensuring that they have the right tools to do the right job - and using them in conjunction to achieve the best outcome.

Working out workflows

First, it’s perhaps best to look at how these different tools approach an operational problem. Application Performance Monitoring (APM) is one such tool that focuses on the complex workflows on which applications are dependent - the network of storage and interdependent apps that must be running smoothly to keep the app running. For example, disruption to a customer-facing banking app might arise because a database with crucial information has ceased functioning correctly.

Without a way to map out the data flows of applications, and which elements within this flow are performing correctly, teams are often forced to take a trial-and-error approach to pinpointing the problem. But APM tools offer a clear graphical interface for Ops teams to see exactly how everything is working, and what’s causing the holdup.

Investigating the infrastructure

Traditional APM tools give little oversight of the problems in the underlying resources powering these workflows. That troublesome database may fail repeatedly because the virtual machine it’s based on doesn’t have enough compute power or sufficient storage to keep up with peak demand. A traditional APM view of the problem wouldn’t have revealed these resource constraints and contention issues.

However, more advanced APM systems work in conjunction with Application Resource Management (ARM) tools. ARM focuses on the infrastructure beneath application workflows, examining the entire application stack to spot bottlenecks in the infrastructure resources for specific assets. Some tools will even leverage AI and machine learning to make automatic adjustments to head off problems of this sort.

For example, AppDynamics, part of Cisco, works alongside Cisco Intersight Workload Optimizer to exchange and correlate data giving application and infrastructure teams a shared view of infrastructure dependencies that impact application performance, user experience, and business impact.

Boosting benefits

By using advanced AI Ops tools that combine APM and ARM capabilities, Ops teams can gain a full view of the complex web of interdependent applications and resources and automatically catch and fix problems before they lead to disruption.

This superior visibility and control offer myriad benefits for Ops teams. By cutting through those reams of operational data and getting straight to the workflow and resourcing problems that could be slowing down an application, these tools can rapidly identify and adjust any faulty areas, enabling teams to ensure smoother operations.

In other words, by taking the guesswork out of application management, AI Ops cuts down the need to manually analyze and monitor operational environments, easing the pressure on shrinking Ops teams.

There are also wider business benefits. The time saved on manual tasks frees up Ops teams to focus on more value-add work with the help of insights generated by these tools. By correctly utilizing AI Ops, teams can pinpoint recurrent weak points in infrastructure and use this insight to advise the business on where to invest maintenance budgets for maximum effect.

With the number of AI neologisms going around these days, it’s easy to dismiss AI Ops as just another buzzword - a new fad that doesn’t necessarily add value. But by using the right AI Ops tools in combination, businesses can tackle real problems for the Ops team, freeing them up from increasingly time-consuming tasks to extract as much value out of operational data as possible.

In this way, AI Ops can help to solve many of the Ops team’s headaches, while offering strategic value to the business.