What Is AIOps?
AIOps is artificial intelligence for IT operations. It refers to the strategic use of AI, machine learning (ML), and machine reasoning (MR) technologies throughout IT operations to simplify and streamline processes and optimize the use of IT resources.
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Where did the term AIOps originate?
Gartner, a global research and advisory firm, was the first to coin the term AIOps. Gartner's definition of AIOps is as follows: "AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination."
Is AIOps a platform?
AIOps can be considered a platform, in that organizations need to align various hardware and software components—including AI and ML engines and specialized servers—as well as human expertise to implement and operate AIOps.
Many service providers offer AIOps solutions for combining big data and AI, ML, and MR capabilities. These solutions improve and automate event monitoring, service management, and more. Most providers typically refer to these solutions as AIOps platforms.
However, more than anything, AIOps is an approach to modernizing IT operations in all areas—including security operations (SecOps), network operations (NetOps), and development operations (DevOps)—by using advanced technology like AI to integrate systems and data and intelligently automate IT.
What else is required to implement AIOps?
A business can't set up AIOps without the ability to integrate its IT systems so those systems can share information and learn from each other. Systems integration requires an application programming interface (API) that is open; in other words, the product manufacturer makes the API publicly available to software developers.
Software development kits (SDKs) are also essential for setting up AIOps. Developers use these toolkits to build custom applications that can be added onto or connected with other programs.
Open platform solution overview
What does AIOps look like in practice?
An example of AIOps is as follows:
- A network management system (NMS) employing AI/ML technology detects a problem with a networking device, such as a switch, router, or access point on the network. The AI/ML diagnoses the problem and provides remediation steps for solving the issue.
- The NMS sends an alert to the IT Service Management (ITSM) server that a problem has been identified on the network and a service ticket should be opened to authorize the repair. A time-to-repair estimate is entered based on the remediation from the NMS.
- The ITSM automatically creates an open ticket, which authorizes the upgrade/repair of the device, specifies the time required to repair, and sets a window for completion.
- Once the issue is fixed by technical support staff—either directly or through automation—the NMS updates the ITSM that the issue is resolved and automatically closes the ticket.
The NMS, powered by AI/ML, saved time in troubleshooting and remediating a solution. Then the ticketing process was handled automatically and seamlessly between the integrated systems, so there was no need for an IT team member to manually create, open, or close a support ticket. This is a very simple example of how AI/ML and connected systems save time and create efficiency. This is the power of AIOps.
Integration makes IT more efficient
What is the state of AIOps, generally?
Most systems that take advantage of AIOps today are integrations resulting from direct collaboration between the manufacturers of those systems. The manufacturers work together to ensure their product integrations are as tight and functional as possible.
However, as more forward-looking companies introduce open APIs and SDKs with their products, manufacturers won't need to be involved as much—or at all—in AIOps integrations. Customers will be able to do their own integrations and customize them to meet their specific needs.
Benefits of AIOps
Time and cost savings
IT teams spend a great deal of time managing tasks that could be automated. With AIOps, IT staff could, for example, stop spending hours fixing faults in the network and instead resolve them with a single click.
Every bit of time saved on a daily basis through automation—10 minutes on one task, 15 minutes on another—can add up to significant annual savings in IT costs for an organization.
Businesses that use an AIOps approach can also detect and resolve IT problems faster, prioritize issues more effectively, and increase the overall performance of their IT organization and the various teams within it, including SecOps, NetOps, and DevOps.
All of the above can enhance an organization's efficiency and productivity, and its bottom line.
Force multiplying of IT capabilities
AIOps allows experienced engineers to devote their time and expertise to more value-added work—including innovation for the business—instead of tedious, manual work.
AIOps can also help bridge skills gaps in an IT organization. Less-experienced team members can rely on the AI, ML, or MR capabilities integrated into IT operations to help them troubleshoot issues quickly, and without the need to escalate matters to more experienced personnel.
And AIOps can help provide insights that allow IT professionals to make decisions faster and more accurately. By sitting between various systems for SecOps, NetOps, DevOps, and other areas of IT, AIOps can collectively alert those teams to problems or opportunities that they can act on together.
Accelerating digital transformation
As a business integrates AI, ML, and MR into its systems and brings those systems together using APIs and SDKs, it increases information sharing across those systems. That helps AI, ML, and MR tools and solutions become more intelligent over time, and work even smarter. These technologies need to ingest mass quantities of data to learn.
Another benefit of implementing and expanding the use of AIOps is it helps to accelerate digital transformation. AIOps allows the business to make better use of data, analytics, and automation across all areas of IT so it can, among other things:
- Innovate new products and services faster
- Better understand customers and deliver new experiences to them
- Reduce risk, and increase agility and resiliency
As more areas of the business become digitized and integrated, it becomes easier to digitally transform the entire organization.
3 initial steps to implement AIOps
AIOps is a gradual process. The three foundational steps outlined here can help an organization get started with implementing AIOps.
Look for quick wins
AIOps is ultimately about helping IT teams to work better together and optimize IT operations. Look for obvious areas in IT where AI, ML, and MR could make a positive impact by helping IT staff to save time and make faster decisions. For example, IT technical support is often a starting point for AIOps because so many tasks are routine and can be easily automated.
Adopt open technology
Legacy hardware and software that can't talk to each other is a common obstacle to implementing AIOps. They can't share data because they weren't designed for it and can't be programmed to do it. Organizations that want to integrate AIOps into their IT operations need to focus on systems that use open standards. They will want to work with vendors that provide open APIs and SDKs for integrating systems and customizing those integrations.
Organizations will want to make sure data telemetry is open standard as well. Some vendors consider the telemetry from their products to be proprietary, and they charge customers a fee to access it. That can make bringing some systems and data into AIOps impossible, or at least costly.
Start linking systems together
Once the organization has an initial AIOps strategy and has integrated AI, ML, and MR into systems in a few areas of its IT operations, the next step is for the business to integrate and customize those systems using APIs and SDKs. Linking these select systems together so they can begin sharing data and learning from each other marks the start of AIOps.