Every CIO I know is balancing two humongous-sized beach balls. One ball represents driving innovation, improving experiences, and delivering data, analytics, and AI capabilities. The CIO wants to fill this ball with helium and make it easy for agile, self-organizing teams to collaborate, experiment, and release changes quickly.

The other ball represents the reliability, stability, performance, and security of mission-critical business services – from the ones that run core operations down to the pilots and POCs the dev and data science teams are innovating.

DevOps + SRE Improve Reliability but Can’t Eliminate P1s

Wait! Wasn’t DevOps supposed to fix the paradox of deploying fast while assuring high service levels? Shouldn’t all the investment in CI/CD automations, continuous testing, shifting-left security practices, migrating to the cloud, configuring infrastructure as code … among other practices … eliminate the need for IT Ops, IT service management, having major P1s, and putting practices in place to reduce the mean time to recover from them?

Of course, DevOps and SRE practices have made huge impacts on dev and ops functions, but what CIOs know is that there will always be unknowns, mistakes, and issues outside of IT’s control that creates instability. IT will always need people responding to these issues and under business pressure to resolve them faster, address root causes, and handle growing complexities.

I know this because of the AIOps benchmark report StarCIO recently completed, where respondents told us that MTTR/uptime are top KPIs and that 70 percent require 3 hours or longer to resolve major P1s. I recently shared five reasons major P1 Incidents have terribly long resolution times about these findings and also wrote about three meaningful KPIs to focus agile development, DevOps, and IT Ops to deliver business outcomes.

Why CIOs Must Add AIOps to their DevOps Programs

DevOps always included monitoring as a primary practice, and many have followed through by implementing monitoring tools, increasing their applications’ observability, developing service level objectives, and measuring error budgets.

But the challenge is that there’s too much operational data for NOCs, IT Ops, and SREs to parse through when incidents span apps, microservices, planetary databases, and SaaS integrations across data centers, multiclouds, and edge networks. That’s the challenge AIOps aims to address by centralizing operational data and applying open box machine learning algorithms to correlate incidents.

So it’s no surprise to see BigPanda’s news of raising $190 million and with 2021 sales growing by 155 percent on a year-over-year basis. “The need among IT Operations teams for AI-powered insights and automation has exploded in recent years,” said Assaf Resnick, co-founder and CEO of BigPanda.

And StarCIO’s research showed that 93 percent of organizations are investing in AIOps or plan to soon, and the early adopters (16 percent) report that AIOps is already making a significant operational impact.

So for CIOs trying to juggle innovation and reliability, AIOps provides the intelligence and automation to help IT keep both balls in the air.