They need autonomous remediation — detecting underlying inefficiencies and solving them before an event occurs, without humans scrambling to fix it. This way, IT teams can avoid service degradation long before it manifests as customer complaints. And Site Reliability Engineers (SREs) can spend a lot less time in solving repetitive problems.
This is just one of the many reasons why enterprises need to automate remediation in their IT processes.
#1 Downtimes are costly
Let’s assume that 1000 employees, who are paid an average of $50 per hour, are affected by a service outage. If their productivity is reduced by 50% as a result of this outage, the loss stands at $25,000 per hour — the cost climbs exponentially as the severity of the outage/the number of affected employees increase. In reality, though, the cost of server outages is as much as $400,000 per hour, finds a recent study. With most monitoring tools, even organizations that have predictive capabilities depend on IT teams to manually resolve them, leading to longer downtimes, and higher employment costs.
With autonomous remediation, companies can transform their reactive incident response to a proactive one, saving resolution time and improving productivity, all while reducing service costs.
#2 Alert fatigue is common
Many monitoring tools serve merely as ‘alerting systems,’ sending an email or a Slack message when there is a potential issue. IT teams are then expected to manually review the alert, understand severity, and address the issue. Indiscriminate alert storms from multiple monitoring tools — including false alarms or minor concerns — result in alert fatigue, which in turn leads to teams missing important incidents.
Autonomous remediation systems can eliminate this challenge in two significant ways by:
- Detecting and resolving repetitive or minor issues automatically, saving FTE
- Differentiating and prioritizing problems, and providing contextual data to IT teams, whenever their intervention is needed
#3 Existing processes deliver poor customer experience
Traditional incident management processes are reactive in nature — their performance measured by mean time to restore (MTTR). In a world where customers demand 99.999% uptime, this can have a huge negative impact on customer experience.
The better metric is mean time between incidents (MTBI). Autonomous remediation enables this by proactively identifying issues and resolving them with minimal human supervision. It mitigates unexpected performance bottlenecks right off the bat to deliver an impeccable end user experience.
#4 Manual remediation is impossible with scale
Unlike a decade ago, organizational IT has grown vastly in scale. Considering bring your own device (BYOD) schemes, cloud services, self-service tools etc., the enterprise IT becomes unmanageable. Well, at least not manually.
Autonomous incident response is the only way organizations can monitor and maintain their scaling IT assets. A good monitoring solution that is powered by accurate AI and ML models can collect metric and event data from different silos and correlate that data with workload metrics, to come up with accurate remediation actions for anomalies.
#5 There is a lack of visibility across systems
The organic nature of IT adoption among large organizations have resulted in silos. A seamless integration of all systems is practically unachievable, neither is a mass migration to consolidated systems. While these silos are not the most productive, they are also inevitable.
AIOps and autonomous remediation has the power to soften the blow. By monitoring each of these tools independently, bringing the data to a common platform, and making sense of it in context, enterprises can enhance their visibility multi-fold.
#6 Remote work needs intelligent problem-solving
Autonomous remediation supports the shift to work-from-home through self-healing systems.
- Since problems are solved autonomously, there is no need for manual inspections, making IT systems self-reliant and saving FTE
- By analyzing data across disparate systems and making sense of them in context, it reduces misinterpretation or duplication of analysis
- Given that autonomous remediation works well on the cloud, enterprises can have more control over their environments and data
- Real-time responses could prevent data leaks or hacks, ensuring security
#7 SLAs can be difficult to adhere to
SLA adherence is one of the fundamental demands of IT operations. Often, IT teams miss SLAs because of some of the aforementioned reasons — alert fatigue, outdated metrics, lack of visibility etc.
Autonomous remediation can predict sites of SLA violations and help IT teams prioritize tasks to avert them. They can also detect patterns of violations so that service levels are not affected in the future.
#8 DevOps needs to continue in real time
Agile teams can deliver robust software only if DevOps continues to run in real time. Especially in a tech organization, any disruption to the DevOps pipeline can push software development initiatives back. Engineering teams cannot wait idly as resolution is going on.
Autonomous remediation helps prevent outages and disruptions, empowering engineering teams to continue in an agile and seamless manner.
Choose HEAL for your autonomous remediation needs
To efficiently manage the scale, dynamism and needs of your sprawling IT organization, you need more than just ITIL. You need a solution that can autonomously heal your assets — with early warning triggers to avoid outages, workload optimization to handle transaction surges, run remediation scripts independently.
If you are interested in learning more about HEAL’s powerful autonomous remediation capabilities, talk to us.