What is AIOps?

Figure 1: AIOps Platform enabling continuous insights across IT Operations Management (ITOM). Source: Gartner (November 2018)

Why invest in AIOps?

Using AIOps, customers can accelerate root cause analysis, forecast potential issues, and automate investigation and remediation to ultimately drive better business outcomes.

Investments in AIOps tools and training are driven by two primary forces—the importance of digital transformation and the growing complexity of the IT environment. IT is faced with more apps, systems, and platforms than ever to keep running in peak condition. Containers, microservices, and other highly dynamic environments (e.g., Cloud, SaaS) generate large volumes of data that exceed the capacity of manual processing, making AIOps necessary for modern cloud-native applications and greater IT automation.

Additionally, most enterprises realize that to build customer loyalty, streamline operations, and increase workforce productivity, they must develop and deliver exceptional digital experiences and do so faster and more effectively than their competition. AIOps provides IT and business leaders with data-driven insights and analytics of IT network and application performance along with automation workflows to maintain high levels of performance and service availability.

What are the benefits of AIOps?

AIOps drives four key benefits as outlined below:

1. 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 teams can decrease MTTR and prevent emerging issues, and in doing so, significantly reduce the costs associated with performance problems.
2. Build a more predictive approach.AIOps provides the ability to recognize patterns and detect potential issues. This can help organizations act before small issues become larger problems.
3. Automate remediation for common issues. IT organizations are increasingly leveraging automation to accelerate IT operations. Automated remediations have become a key element of IT Ops strategy for improved performance monitoring and management. With embedded expert knowledge, AIOps can not only prescribe corrective action but also automate remediation for detected issues.
4. Drive faster and better decision-making.AIOps platforms that leverage advanced AI features can automatically investigate and take action to address issues before anyone is even aware of them. This extends beyond IT to the business, as AIOps tools can be a rich source of data for business intelligence (BI) platforms.

What data is needed for AIOps?

AI relies on high quality data to derive reliable and accurate outcomes. When applied to IT Ops, relevant data includes metrics, logs, traces, and alerts, along with data for network flows, device health, application performance, user experience info, even packets and transactional metadata are critical.

Most enterprises generate vast amounts of this structured and unstructured data daily. This volume of data demands scalability to collect, store, pre-process and analyze the billions of transactions, metadata, and metrics that are generated each day. The quality and completeness of the data drives artificial intelligence and machine learning insights, making scalability a critical component of effective AIOps. Equipped with the necessary data, AIOps tools can automatically map dependencies and build contextual models so that troubleshooters can quickly determine the root cause of an issue.

Further, using AI techniques such as Generative AI, this telemetry data can also be combined with business performance data to drive better business decisions for the enterprise, resulting in AIOps data and insights becoming critical to executive teams and leaders as they develop and execute enterprise strategy.

What are some typical AIOps use cases?

Many organizations are embracing AIOps for:

• Troubleshooting:Accelerate MTTR by applying machine learning, advanced analytics, and visualizations to IT telemetry. Use application dependency mapping to visualize the complex, often short-lived, relationships between applications and end-user experience, and the infrastructure that underpins them.
• Proactive issue identification:Automate anomaly detection to alert based on unusual performance behavior before end-user SLAs are breached. Surface unsuspected issues using pattern recognition and anomaly detection to quickly find the needle in the haystack and take proactive, preventative action.
• Strategic planning:Prioritize efforts that will have the most overall business impact by identifying the enterprise IT components implicated in the most important transactions. Provide business insight by analyzing all the transactions and associated metadata for these components to understand past and present performance, and future trends.
• Event management:Reduce alert noise generated by false alarms or by multiple downstream events triggered by a common root cause. IT uses AIOPs to build more actionable context into alerts (events) and optimize the incident management workflow by incorporating automated remediation and cross-domain root cause analysis.
• Automated remediation:AIOps systems can be enabled to support automated remediation based on IT operations runbooks. When applied correctly, automation can accelerate fixes, improve user satisfaction, and free IT to focus on more strategic initiatives.
• Infrastructure automation:Re-route network traffic to reduce congestion and free up bandwidth, spin up additional cloud instances and concurrently expand the SD-WAN fabric, and re-distribute containerized workload, for optimum resource utilization and cost-savings.

What is the future direction for AIOps?

AIOps began with the use of machine learning and data analytics to obtain actionable insights and enable proactive responses that reduce toil and improve performance and availability. With the recent advances in AI, the field of AIOps is also evolving to include capabilities such as Generative AI and Agentic AI to further reduce manual effort and improve service experience. Within AIOps, Generative AI is used primarily for its natural language interface and reasoning capabilities to synthesize analytics and recommend corrective action. Agentic AI is the next evolution of applied artificial intelligence in IT, where AI moves beyond passive analysis and recommendations to autonomously act as an intelligent agent on behalf of IT teams. Agentic AI workflows are intended to incorporate all other types of AI techniques to deliver the desired objective with minimal to no human interaction required.