It's been said that the human brain is the most advanced supercomputer in the world. That may be, but scientists have yet to figure out how to tap into all that processing power. For now, AI algorithms can do vital tasks faster and often more effectively than any human analyst.

So what are these tasks? Here are six that all start with C, making them easy to remember.

Curation – As geographic boundaries break down and more organizations adopt a work-from-anywhere workforce strategy, IT is experiencing what some have called "IT sprawl." The number of tools used to manage these systems is also expanding. For example, in Gartner's 2020 CISO Effectiveness Survey, 78 percent of CISOs told the research firm they have 16 or more tools in their cybersecurity vendor portfolio; 12 percent have 46 or more.

Keep in mind, that's just cybersecurity. IT teams often use multiple tools to manage the other performance aspects of enterprise systems, including application performance, networking and infrastructure. An analyst trying to manually sift through all the data served up by dozens of operational systems can easily overlook the details that matter. 

AIOps serves as a central repository for performance data, making it accessible to advanced algorithms that can identify patterns and anomalies that may indicate a problem. Just as importantly, AIOps uses machine learning to cut through all the noise. By discarding immaterial incidents, your IT operations team doesn't need to spend time chasing down irrelevant anomalies.

Correlation – One of the advantages of curating data from across the enterprise is the ability to correlate seemingly unrelated events. It's like the butterfly effect. Every time someone does something over here, it creates a problem over there. It may not seem like the two events are related, but they are. With the right AIOps solution, artificial intelligence and machine learning are used to identify dependencies and impacts a human IT analyst might miss.

Causality – Root-cause analysis is one of the most-talked about benefits of applying AI and machine learning to IT operations. Before AI, when a problem occurred, IT would develop a hypothesis then test that hypothesis. For example, if performance degrades immediately following an upgrade, the upgrade might be deemed the most likely the cause. So development rolls back the upgrade to try to resolve the problem. If that doesn't work, they move onto the next most likely scenario. 

The problem is that the modern enterprise IT environment consists of millions of moving parts and even more lines of code. This hypothesize and then test approach takes time – a luxury the organization may not have when an event affects critical systems. Plus, a sudden performance issue might not be caused by just one change. AIOps machine learning algorithms learn from the way your systems interact to determine the most likely cause. 

Communication – Like any modern ITOps tool, AIOps serves up need-to-know information to those who need to know it. What makes AIOps different is the tool's ability to correlate events from across the enterprise and notify the people of an event, even if they're on different teams. We won't spend much time on this C, because the real impact of the advanced communications capabilities offered by AIOps is our next C: collaboration.

Collaboration – A siloed IT organization can lead to unnecessarily long remediation times. Even in a mid-sized enterprise, IT operations encompasses many different domains: application development, systems administration, infrastructure, etc. Resolving an incident may require contributions from individuals across these domains. Many IT departments have a preferred tool they use for managing these events. However, these tools don't often provide visibility into the incident management tools used by other teams.

Not all AIOps tools are the same, but WWT's approach to AIOps is to incorporate existing service management solutions into an comprehensive AIOps platform. This approach provides enhanced visibility into performance and troubleshooting across teams but removes the learning curve (and loss of morale) that comes from insisting everyone use the same systems. Enhanced visibility and coordination also increase accountability, improving important metrics like MTTD and MTTR. 

Calibration– Whether they call it IT automation, auto-remediation, or the self-healing enterprise, this is the ultimate achievement for IT leaders everywhere. Automation speeds up identification and remediation. IT automation also allows your staff to focus on higher value add activities that can't yet be automated. (And there are still plenty of those!)

AIOps uses machine learning to help you identify and automate the low-hanging fruit – those repetitive tasks that don't require much thought. These algorithms also learn from the data they collect across the enterprise and the manual action taken in response to incidents to suggest additional areas where automation can be applied.

AIOps Helps IT Professionals Work Smarter, Not Harder

Lately, there's been a lot of buzz around whether IT automation is going to replace people or not. Maybe someday. But not yet. If you look at the 6 Cs of AIOps, every one of them is about enabling IT professionals to do their jobs faster and more effectively than ever before. AIOps isn't about cutting staff; it's about doing more with the talent you have and helping them reach their true potential.

To Learn More, Request a Demo of AIOps