AIOps: What Is It and Why Should Your Enterprise Care?

Let’s start with the most obvious first question, because while AIOps as a concept has been around since Gartner coined the term in 2016, it’s only now gaining widespread attention. Indeed, if you have your eye on the industry, you’ll know that it has become something of a buzzword in IT circles, yet only around 5 percent of large enterprises have made inroads into AIOps adoption.

Buzzword or not, AIOps is no idle focus for a new wave of IT hyperboles. It’s a major concept in AI consulting offering significant practical benefits for enterprises managing fast-paced deployments of ever-more-complex and powerful software solutions. You can think of AIOps as being the IT equivalent of fighting fire with fire, or machines to manage machines.

Modern businesses manage operations with the use of increasingly smart and rapid algorithmic solutions, which are already becoming too fast and complex for human engineers to oversee. When issues arise with these solutions, people need the help of technology to identify and resolve them, and that’s the premise behind the ascendency of AIOps.

As a term, AIOps is an amalgamation of AI (artificial intelligence) and Ops (as in operations) and therefore shortens “AI Ops” to a single word. Essentially then, you can define it as the use of artificial intelligence to manage IT operations.

The primary components of an AIOps platform are machine learning and big data. These AI elements are applied in combination with human intelligence to provide full visibility into the performance of modern business management systems.

AIOps powers the process in which data from IT ticketing and event reporting is aggregated with observational data from job logs and monitoring solutions inside a big-data application. The data is then scrutinized by advanced analytics and machine learning algorithms, to automate issue resolution and create a real-time stream of continuous insights into business solution performance.

It’s essential to note that AIOps does not remove the need for human intervention into the maintenance and improvement of business information systems. Instead, it simply reduces such a necessity. AIOps introduces capabilities to automate routine preventative and remedial activities and to display data visualizations for IT technicians to interpret and use for more comprehensive and timely interventions when necessary.

Of course, the act of naming a concept does not create a demand for it. The growth in AIOps interest is fuelled not by its acquisition of a name, but by the need for enterprises to upgrade IT Ops efficiencies. The simple reality is that human IT technicians and engineers can no longer keep up with the maintenance of technology stacks in large enterprises. Manual management is no longer tenable, and it takes a machine to manage another machine.

To illustrate the issue in a little more detail, let’s look briefly at the enterprise IT challenges that increasingly drive the need for AI and automation in operations management.

The enterprise IT landscape has become elastic, dynamic, and complex. On-premises infrastructure is disappearing, usurped by mobile technology, managed and unmanaged cloud solutions, SaaS, and third-party service providers. In such an environment, there is no place for human monitoring of day-to-day operations. Instead of keeping the lights on, IT personnel would be better deployed in the resolution of complex and exceptional issues only.

Enterprise IT is no longer about computers. More and more formerly dumb items are connected via the internet of things, APIs are growing in abundance, and more users are joining the digital collective. In short, we’re reaching a point where IT is the business. No business can afford IT problems that linger long enough to impact user experience, but again, the human capacity to respond is insufficient, and automation becomes a necessity.

If bringing all the enterprise data together for correlation is the first step in AIOps implementation, the second step is necessary to address the consequence of step one. No doubt, the number of alerts demanding attention from your IT specialists is already substantial and burdensome. In a recent survey on the state of AIOps, the AIOps Exchange reported that 40% of IT organizations receive over a million alerts daily. Perhaps you can relate!

Alerts will only become more prolific when you begin to achieve end-to-end visibility. Your business will therefore need a solution to help you reduce that burden on your human resources.

An AIOps platform might be the only realistic answer to managing alerts that can number well into the tens of thousands. The use of AI for business automation is becoming essential to reduce the number of monitoring tools, along with the downtime and human effort involved in identifying and responding to system alerts.

As with any maturing technology or concept, AIOps adoption is likely to present obstacles and challenges. However, as mentioned so often in our articles on this blog, forewarned is forearmed, and when the most predominant challenges enter into your awareness, your ability to counter them increases.

In the case of AIOps, the following elements of adoption are likely to present pain-points:

As advanced as digital technologies have become, the adage of “garbage in, garbage out” still holds. For many companies planning to implement AIOps, data quality will present a barrier to successful deployment. As AIOps solutions must aggregate data before analyzing it, data deficiencies found anywhere in the IT environment will hinder the platform effectiveness as these deficiencies get exposed during the aggregation.

Several AIOps vendors base their pricing on the data volume the tool will handle, and pricing schemes are not necessarily simple to interpret. Then there are all the other costs to consider, such as licensing, training, implementation, and integration.

You might even find it impossible to implement your solution without AI consultants’ help. Furthermore, not all AIOps platforms are cloud-based, so you might need to factor the cost of extra hardware into your budget.

Time too is a critical element of implementation costs, and AIOps tools, in the main, are not simple to get up-and-running. In addition to a possibly lengthy time-to-production, your IT department will need to decide whether the right expertise exists within the organization. If not, it will need new hires or the involvement of third-party service providers. Don’t forget to account for the time your teams will spend on end-user training, too.

While there is already a reasonable choice of available AIOps tools, the technology is not yet at a level where the wrong choice might merely introduce some inconveniences. If you don’t select well, your enterprise could experience significant problems. The last thing you need is a solution that can’t:

As enterprise IT continues to become bigger, faster, smarter, and more decentralized, the only way to keep the machines running is with other machines. AIOps is all about taking the pressure off the IT people, who will otherwise drown in the rising tide of business digitization.

It might be early days for AIOps, but only cynics can fail to recognize that machine learning and automation will be a necessity for tomorrow’s digitally transformed enterprise. For some large organizations, it’s already the only solution to keep systems running reliably.

Your enterprise should not only know about AIOps but care about it, because it’s coming. By making ready for it today, you’ll have the edge over the cynics, and be the first on the high ground as the floodwaters rise.