AIOps, which first entered the conversation when Gartner coined the term in 2016, is now a well-established niche within the IT industry. Yet it is also continuing to evolve and change as organizations learn new strategies for getting more out of AIOps platforms. There are still a number of opportunities for moving AIOps forward and increasing the value that it can provide.
Here’s a look at five ways in which AIOps solutions can move the needle forward by going beyond conventional AIOps.
More Automated Remediation
To date, many AIOps use cases have been reactive or prescriptive. In the former instance, AIOps platforms help troubleshoot problems after they occur by, for example, identifying the root cause of an application failure. In the latter, they provide recommendations about how to avoid problems or optimize operations. For instance, a prescriptive AIOps platform could suggest the ideal resource allocation for a cloud-based virtual server that balances performance with cost.
AIOps is valuable in both of these contexts. However, next-generation AIOps platforms go further by providing automated remediation functionality as well. Automated remediation means that AIOps can proactively solve problems or optimize operations on its own, rather than merely play an assistive role in helping human engineers to perform these tasks.
Although some AIOps platforms already provide automated remediation, this functionality is likely to become more central as AIOps evolves. For next-generation AIOps platforms, automated remediation will be a core use case rather than the convenient functionality that it currently represents.
Expanded Focus on Performing Complex Tasks
Beyond taking greater advantage of AIOps’s ability to remediate problems automatically, next-generation AIOps platforms are also poised to handle a greater degree of complexity in the workflows that they automate.
Today, when AIOps is used to automate tasks that would otherwise be performed by humans, the tasks are usually relatively simple and routine. An AIOps security platform could change firewall rules to block a malicious host, for example, or it could allocate more compute resources to an underperforming virtual machine. These are straightforward procedures that are simple to automate.
AIOps will need to go further in performing automated tasks if it is to create greater value for IT operations teams. The ability to, say, write a YAML file to automate a new Kubernetes deployment or reconfigure a set of IAM rules in response to changes in access requirements are the types of more complex tasks that AIOps could automate in the future.
The building-blocks for automation of this sort are already in place; these tasks can be powered by the same data analytics procedures and algorithms that drive AIOps today. What remains is simply to add the level of sophistication required for AIOps platforms to perform more complex tasks.
Enhanced Ability to Leverage Disparate Data Sets
AIOps platforms today tend to be designed to interpret one category of data, and interpret it well. An AIOps platform that optimizes cloud computing spend will analyze cost and performance data from cloud environments to make its recommendations. An AIOps-driven security platform will gather information from server and network logs to identify threats.
This means that AIOps platforms (and the data that powers them) remain siloed to some extent. This is not surprising—designing the algorithms that power AIOps is easier when the types and structure of the data that the platform has to interpret are limited in scope.
Going forward, however, this challenge will be overcome as AIOps platforms gain greater ability to leverage all types of data as a unified whole. By extension, they will be able to perform multiple types of roles. For example, instead of only handling cloud cost optimization or only dealing with security, a single AIOps platform that can ingest any data that the IT operations team throws at it will be able to meet all of the team’s needs.
Applying AIOps to New Use Cases
Most AIOps use cases today center around performance monitoring and optimization as well as security operations. These are useful areas for applying the insights and automation that AIOps has to offer, but they represent only a small set of the total responsibilities of IT operations teams.
In order to realize its full potential, then, AIOps will need to address additional use cases. Infrastructure provisioning is one facet of IT operations in which it is easy to imagine AIOps playing a greater role. An AIOps solution could take infrastructure-as-code to the next level by generating infrastructure configurations automatically and then applying them autonomously, instead of requiring human engineers to write the provisioning templates.
As another example, AIOps could help manage IT governance. Today, IT teams depend on manual effort to enforce IT governance policies. Teams might have some basic auditing platforms available to help identify non-compliance automatically, but finding and addressing these issues is nonetheless a largely manual effort. AIOps could streamline this process by making it easier not just to identify instances of non-compliance, but also to remediate them automatically. The result would be more effective IT governance, while requiring less manual work on the part of IT engineers.
Self-Managed AIOps Platforms
AIOps platforms are usually designed to help manage other applications or infrastructure. But somewhat ironically, managing AIOps platforms themselves remains the responsibility of IT operations. Human engineers have to deploy, configure, and monitor the platforms manually.
For that reason, there is an opportunity to develop AIOps platforms that work more autonomously. Imagine an AIOps platform that you could enable in your environment by simply clicking a button. This platform would then automatically deploy itself (along with any local agents that it required) and configure its own management rules, all in a way that’s tailored to your environment.
That type of solution seems perhaps somewhat further off than the other types of AIOps innovations discussed above, but it’s feasible to create by building on the intelligent, data-driven functionality that is already at the core of AIOps.
AIOps has already reached maturity in the sense that AIOps platforms are delivering real value in production environments today. However, there are a variety of ways in which AIOps can expand to deliver even more value. The next generation of AIOps platforms will deliver more automation, support more complex tasks and use cases, and be powered by an even greater range of data.
These are the types of changes that will extend AIOps from being a complement to IT operations that assists human engineers from the periphery to the centerpiece of IT operations.