Availability and AIOps: Crucial in the Modern Enterprise

In today’s digital economy, tech availability can make or break a business. After all, sophisticated apps and services are no longer just “nice to haves.” They are essentials to every modern business. And, because consumers have come to rely on digital apps and services, they have to work — 24 hours a day, seven days a week. AIOps can play a critical role.

Why? Society’s dependence on digital technologies was already increasing before the pandemic accelerated tech adoption by an estimated three to four years. And there’s no going back. We now live and breathe online, using apps for many of our daily professional and personal activities, from connecting with colleagues to connecting with friends, tracking grocery orders to tracking budgets, and the list goes on.

With the globe relying on digital apps and services every second of every day, businesses face harsh consequences for tech downtime. For starters, downtime is disastrous for a company’s reputation. Poor performance or an outage can cause consumer perception to tumble, decreasing sales and hindering long-term growth. This is in addition to significant upfront monetary losses. The average cost of tech downtime is $5,600 per minute, according to Gartner. And, of course, the longer the incident goes untreated, the greater the damage.

In other words, enterprises need to get serious about their technology’s availability. Most modern companies have DevOps and SRE teams that work to keep systems running at peak performance. These jobs are critical considering incidents are inevitable, especially in today’s ever more complex and sometimes more fragile IT environments.

But, while traditional teams worked to fix incidents and outages after they occurred, modern teams must anticipate service-disrupting issues. How? With advanced artificial intelligence for IT operations (AIOps).

See also: AIOps: What It is, When to Use it, and How to Get Started

Not all tools will cut it

Since Gartner coined the term “AIOps” in 2016, the market has grown significantly, stoked by an era of digital transformation. Like most digital tools in an expanding market, some solutions are more effective than others. And some have evolved to meet modern demands, while others have remained stagnant.

With traditional monitoring tools, when the tool found a data anomaly, it notified engineers that a problem had occurred, prompting the team to investigate and fix the issues. But this availability model has a significant problem: it’s too slow. To avoid false positives, thresholds are typically set very high, which means  DevOps and SRE teams don’t know about service-affecting problems until systems are experiencing major performance issues. And the bigger picture: customers are already impacted by the outage.

Initially, when the concept of applying AI to operations became a thing, a lot of the early hype was around prediction, and back then, prediction meant linear regression, that is, looking back at past behavior and using that to extrapolate future state. While that has some utility (determining trends for things like capacity planning), it failed to fulfill the promise of predicting inherently unpredictable events, such as accidents and vandalism.

To address this, a new approach of applying AI in real time to telemetry and events emerged. This shifted the focus from prediction to early detection. In most cases, AI has shaved hours off the time it took to respond to issues, and in some cases, even days. This allows operations to start fixing issues before they become customer-impacting.

There’s yet another disadvantage to a legacy approach to monitoring. Legacy technology’s slower mean time to recovery (MTTR) bogs down engineers. Not only does this hinder future-driven initiatives like paying down tech debt and advancing innovation, but it also causes these teams’ morale, job satisfaction, and confidence to sink.

See also: AIOps 2.0: Making Actionable Intelligence Actually Actionable

Modern AIOps to the rescue

As businesses manage an increasingly complex IT environment, they need more information than just event data can provide. That’s why modern AIOps solutions scan the entirety of the IT ecosystem, adding metrics, traces, and logs to the traditional event data. It’s the only way to understand the entire system’s performance and diagnose issues early.

Modern AIOps tools take a forward-thinking approach to the entire incident management lifecycle, further accelerating incident resolution. After detecting incidents early in the lifecycle, modern AIOps solutions automate the workflow. The platform notifies engineers responsible for the incident and gives them important context to help expedite the fix — and prevent frustrated end users and significant financial losses.

Today’s most effective AIOps platforms also include robust machine learning capabilities that remember destructive patterns and prevent the issue from ever happening again. This reduces incidents, delighting both the consumers relying on this technology to work and the DevOps and SREs responsible for system performance. Modern AIOps keeps users online. And by reducing noise, toil, and unplanned work, engineers can pursue the activities that excite them: innovation and experimentation.

As businesses navigate a looming recession, budget cuts, and the Great Resignation, they must prioritize superior customer experiences and employee satisfaction. Investing in AIOps could be the key. Advanced AIOps ensures more uptime, building confidence in corporate brands while saving company resources.