The term “AIOps” stands for “artificial intelligence for IT operations.” Originally coined by Gartner in 2017, the term refers to the way data and information from an IT environment are managed by an IT team–in this case, using AI. This definition from Gartner provides more granular detail related to the concept and explicates the value of an AIOps platform:
“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”
But why should an enterprise IT team care about about AIOps?
To answer that question, let’s dig deeper to understand the story behind AIOps, explore the elements of AIOps platforms, and review three potential use cases.
The Core Elements of An AIOps Platform
Today’s application environments are exploding in complexity. According to the Wall Street Journal, midsize to large companies now use an average of eight different cloud providers for various enterprise applications and services. Compounding this complexity is the sheer volume of data produced by application infrastructure, and the high potential for performance problems each time an update or change is made to that existing infrastructure. While application performance monitoring (APM) solutions provide real-time alerts for performance problems, there’s evidence that IT teams need more support to effectively monitor the increasingly complex landscape.
And that’s where AIOps platforms enter the picture.
Rather than reacting to issues as they arise in the application environment, AIOps platforms allow IT teams to proactively manage performance challenges faster, and in real-time–before they become system-wide problems. That’s because AIOps platforms have the ability to ingest large volumes of data originating from all areas of the application environment, and analyze it using AI to identify areas of remediation and optimization.
AIOps platforms also play a critical role in eliminating the manual component of identifying issues within the IT landscape, a problem that’s compounded by the still siloed nature of the monitoring environment. In fact, recent research from AppDynamics revealed that 91% of global IT leaders said monitoring tools only provide data about how releases impact their own area of responsibility, and not the broader IT environment, or the business. With an AIOps platform, IT doesn’t have to work harder to get smarter about what’s happening within every facet of application infrastructure.
Make no mistake, AIOps platforms have compelling potential. But as of right now, the category itself is emergent and highly fluid. Case in point: Gartner defines AIOps platforms as having several key components, however, those components are broad enough that many tools could potentially fit into this category now or in the future. Here’s how a Gartner analyst, Pankaj Prasad, described AIOps platforms:
“AIOps platform technologies comprise of multiple layers that address data collection, storage, analytical engines and visualization. They enable integration with other applications via application programming interfaces (APIs) allowing for a vendor-agnostic data ingestion capability.”
While Gartner’s elements of an AIOps platform are somewhat broad–as are many others out there in the market–the category will continue to evolve in the years ahead as the technology becomes more rigorous, and its use cases more apparent. What’s more, many of these shifts will happen alongside changes in the broader APM space. A more pared down overview of core AIOps platform components would include:
- Machine learning
- Performance baselining
- Anomaly detection
- Automated root cause analysis
- Predictive insights
What Problems Does An AIOps Strategy Solve?
Growing complexity and the deluge of data within the application environment puts new demands on IT professionals to both synthesize meaning from this influx of information and connect it to broader business objectives. In this highly demanding environment, IT teams need all of the help they can get when it comes to performance optimization.
That’s where AIOps platforms can play a pivotal role in advancing IT organizations and reducing the complexity within the application environment. With AIOps, you can bring all data into a single place, and scale it to understand your environment from every possible angle. This provides teams with the flexibility needed to automate certain tasks when appropriate, and use AI to pinpoint problems faster.
From reducing the cognitive overhead of parsing through volumes of data within the application environment to the potential for self-healing capabilities that help solve major performance problems, AIOps is an exciting space that could help IT professionals in three major areas:
- Drive faster and better decision-making. Broadly speaking, AIOps platforms and related AI features have the potential to become smart enough about IT environments in order to surface insights and provide them to leaders for faster and better decision-making.
- Decrease MTTR. Outages and performance problems hurt the bottom line of every business, so IT organizations must actively seek out ways to reduce the mean time to resolution (MTTR). With AIOps, it’s possible that IT teams could decrease MTTR and prevent emerging issues, and in doing so, reduce the costs associated with performance problems.
- Build a more proactive approach to performance monitoring. According to research from AppDynamics, 74% of IT professionals would like to build a more proactive approach to performance monitoring. With AIOps technology, there’s potential to take it a step further, and respond to issues in real-time. What’s more, by taking in the totality of application environment data, AIOps platforms could connect performance insights to business outcomes. This would finally close the loop on the impact of performance on the business and customers, and it would help organizations take action before small issues become larger problems.
Looking Ahead to the Future of Performance Monitoring and AIOps
Right now, AIOps technology is still relatively new, the terms and concepts relatively fluid, and there’s a great deal of work to be done before anyone can deliver on the promise of AIOps. What is established, however, is that AIOps is already a mindset focused on prediction over reaction, answers over investigation, and actions over analysis. And that’s why IT leaders should keep an eye on the rise of AIOps as a whole, and start preparing for what’s next in monitoring and observability. If history is any indication, there’s enormous potential for transformation in the space in a short period of time.