Key KPIs of AIOps Adoption

Organizations are on a path to increased and improved digitization, with hundreds of applications, services, microservices and servers running in a multi-cloud or hybrid environment. As IT looks to move away from operations, they are turning to AIOps to ensure compliance, cybersecurity and threat management.

AIOps leverages AI and ML capabilities to detect anomalies, predict outages, automate resolution and make ITOps more efficient so that teams spend less time putting out daily fires and more time innovating and transforming IT.

In qualitative terms, AIOps benefits organizations as it improves their resilience to threats, makes employees more productive, boosts workplace well-being and enhances customer experience.

However, specific KPIs can help track the quantitative effects of AIOps on an enterprise as leaders need to justify the cost of AIOps and prove its ROI. 

Let’s take a look at the metrics, organizations must keep a tab on to track the impact of AIOps and prove success.

Metrics to Measure AIOps Success 

What gets measured gets improved. Organizations measure AIOps effectiveness  using the following metrics in no particular order.

Mean Time to Detect

MTTD tracks the time you take to detect or identify an anomaly. AIOps determines patterns, establishes dynamic baselines to assets, sifts signals out of noise, correlates events, adds context to enrich MELTs and ultimately reduces mean time to detect, allowing an organization to quicken anomaly detection and correct it.

An organization can’t solve incidents in real time if it can’t detect them before they impact business operations. An issue can spring up in a specific part of an IT infrastructure that originated elsewhere. AIOps makes it possible and feasible to find the origin of an incident and its root cause.

Mean Time to Acknowledge

MTTA specifies the time taken to acknowledge an IT incident and route it to the concerned person or team for resolution. This can be highly complicated in a modern IT infrastructure with several asset dependencies.

AIOps reduces the time to acknowledge by identifying and automatically routing incident information to the right department. Intelligent automation eliminates the hassle of guesswork and back-and-forth between teams and positively impacts overall IT health.

Mean Time to Resolve/Repair

Time is money in the face of an IT incident that affects employee productivity or customer satisfaction. Getting a service up and running after an incident is critical to ensuring IT resilience. MTTR measures the time lapse between an incident’s occurrence and resolution.

Through effective root-cause analysis and incident escalation, AIOps reduces MTTR, leading directly or indirectly to cost savings. AIOps can also augment its intelligence with historical data so that incidents are automatically resolved if they are repetitive. In other cases, AIOps can make useful recommendations to repair a crashed service or application.

Ticket-to-Incident Ratio

In complex IT infrastructure, hundreds of tickets might be raised for the same incident, especially if the impact was cross-stack. In such cases, the ticket-to-incident ratio is extremely out of balance. IT teams may take a while to investigate and arrive at the knowledge that several tickets point to the same incident.

AIOps helps balance the ticket-to-incident ratio closer to 1:1 by correlating incidents and grouping the data from multiple IT environments to reduce the number and redundancy in tickets, logs and events. This helps diagnose issues efficiently and bolster employee productivity to act on incidents.

Mean Time Between Failures

MTBF denotes the average time lapse between failures and outages in an asset. For instance, if a service has operated for 100 hours and experienced downtime twice, its mean time between failures would be 50 hours, achieved by dividing the operational hours by the number of outages.

AIOps can detect and resolve incidents in real-time, preventing service outages. Moreover, it can learn from past failures and predict outages, positively impacting the MTBF KPI. A high MTBF proves IT resilience.

Service Availability

Service availability is the percentage of an asset’s uptime over a specific period or outage minutes per period of time. Machine learning and artificial intelligence can analyze past data, read patterns, form dynamic baselines and thus predict and prevent business-critical outages, allowing organizations to function more smoothly.

Software services in any enterprise today are responsible for performing business-critical functions. Any disturbances in these services can directly impact revenue and customer experience. AIOps can automate the resolution of simple and repetitive incidents, make recommendations to resolve new incidents and quicken root cause analysis, improving service availability.

% of Automated Vs. Manual resolution

AIOps eliminates the need for manual intervention every time an incident occurs. Teams can employ AI to automate incident resolution based on historical data, removing many monotonous tasks from their day. If the automated resolutions outnumber manual, it’s proof that your organization is saving time, resources and cost through AIOps.

Even in previously unseen incidents, AIOps can assist humans by making informed recommendations, routing incidents to the right people, correlating incidents for simplicity and enriching them contextually for quicker repair.

User-reported Vs. Automation-Detected Issues

A crucial part of IT’s responsibilities is to detect and fix a problem before it impacts the end user’s experience. This was traditionally done through user-reported issues when some damage had already happened. Increasingly, organizations are deploying AIOps to detect anomalies before they hamper the experience.

AIOps uses dynamic baselining, so any departure from an asset’s threshold is detected and reported before it becomes an issue. When an organization starts automatically detecting issues, it points to a healthy IT environment.

Time and cost saving

AIOps can directly impact an enterprise’s bottom line, whether it’s used to automate incident detection or speed it up. AIOps might just be the solution to IT teams struggling with burnout from handling too much on the day-to-day.

The time and cost savings from AIOps spans multiple areas. Learn more about it in our blog post A 7-Step Guide to IT Cost Reduction in 2024, and in a more detailed manner in the white paper AIOps Operating Model & Its Economic Benefits.

AIOps’ key performance indicators will let you know when you’ve started seeing the many benefits of automating IT operations. Learn more about CloudFabrix Robotic Data Automation that combines observability, AIOps, automation and security.

Tejo Prayaga
Tejo Prayaga
Tejo Prayaga is a high-growth Product Management & Marketing leader. Tejo has extensive experience helping enterprises build, scale, and market innovative products and solutions that use modern technologies like Data Automation, Artificial Intelligence, Machine Learning, Microservices, Cloud Services, and more. Startup geek, Ex-Cisco, MBA, Speaker, and Toastmaster!!