Large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.
~ Gartner
When the COVID-19 pandemic necessitated their workforce to function remotely, Siemens USA, a manufacturer of industrial and healthcare equipment, adopted AIOps to monitor and protect 95% of its 400,000 PCs, laptops, mobiles, and applications. With sophisticated endpoint detection and response technology, the system used AI and machine learning (ML) to analyze IT data, identify threats, and provide actionable insights.
As Adeeb Mahmood, senior director of cybersecurity operations for Siemens USA in Washington, DC, said, “The faster we are able to detect and prevent threats to our devices and critical data, the better protected our company is. The technology provides our security analysts with actionable outputs and enables us to remain current with threats and indicators of compromise.”
Like Siemens, every major organization is on the path of rapid digitization, with hundreds of apps, services, micro-services, and servers running in a hybrid IT environment. Hence, cybersecurity becomes the foremost priority because even a single network outage can bring business operations to an unexpected halt.
AIOps is a group of tools and components that leverage AI and ML to detect anomalies and threats, predict future outages, resolve issues, and automate standard service desk operations. Consequently, AIOps maximizes network uptime, accelerates incident response and remediation, enhances employee productivity, and decreases costs.
However, before investing in new technology, it is prudent to weigh the benefits against the cost and ensure it will deliver value to the employees, customers, and the business at large. So, let’s take a look at the nine KPIs that measure the impact of AIOps on IT processes.
1. Mean time to detect (MTTD)
MTTD measures the time taken to identify the issue. AIOps detects patterns and groups events, sifts signals out of noise, and reduces event streams up to 95% to identify the critical alerts related to IT infrastructure performance. Hence, AIOps leads to faster anomaly detection, reduced downtime, and enhanced productivity.
2. Mean time to acknowledge (MTTA)
When an issue is detected, IT teams must acknowledge the problem and identify who will resolve it. AIOps uses machine learning algorithms to automatically decide who will address the issue and ensure the right people are up and working on it.
3. Mean time to resolve/repair (MTTR)
Time is money, and when an essential process or app is down, getting it up and running on time is crucial. MTTR measures the average time required to repair faulty equipment. Simply put, it is the time that lapses between the start of the incident and when the system returns to full functionality.
By diagnosing the root cause of the issue and escalating the problem to the right team of IT professionals, AIOps reduces the MTTR. Using machine learning, the systems can quickly identify whether an issue has occurred in the past and recommend/automate actions to resolve it.
4. Ticket-to-incident ratio
Often, tens or hundreds of tickets are raised for the same issue, especially if the anomalous event has a cross-stack impact. In such a situation, tickets seldom map to incidents in a 1:1 ratio. While different teams are investigating the incident from varied perspectives, organizations must be mindful of the time it takes to realize it is the same problem.
AIOps correlates and groups the data generated from multiple IT environments to reduce the number of tickets, logs, and events and diagnose problems swiftly, thus improving the service desk’s efficiency and freeing up staff to focus on other value-adding tasks.
5. Service availability
Service availability refers to the percentage of uptime over a specific period of time. Simply stated, it is the outage minutes per period of time.
Machine learning algorithms analyze past data to predict and resolve potential network downtime and prevent business-critical outages. Moreover, AIOps can address the less-urgent alerts pertaining to more urgent issues before they cause severe harm.
6. Mean time between failures (MTBF)
The mean time between failures or MTBF means the average time between system breakdowns. MTBF is calculated by dividing the number of operational hours by the number of failures. For example, an asset operates 1,000 hours a year, and in the previous year, it broke down eight times. Therefore, MTBF for that asset is 125 hours.
Needless to say, AIOps helps improve MTBF by rectifying current issues and predicting potential future outages.
7. Automated vs. manual resolution
Machine learning algorithms can identify patterns, learn from past remediation measures taken, e.g., previous scripts executed, and automatically remedy the problem, thus reducing the need for manual intervention.
8. User reporting vs. automatic detection
IT teams must detect and resolve issues before the end-user becomes aware of them and reports them to the company.
AIOps leverages dynamic thresholds for automated alert generation and escalation to remedy problems before the end-user is affected.
9. Common business KPIs
AIOps is inevitably a vital business asset that helps improve typical business KPIs. By ensuring network stability and minimizing downtime, AIOps streamlines revenue cycle and operations. Additionally, it enhances the service quality of business apps that customers use, improving customer experience and building customer trust and loyalty in the process. Moreover, AIOps diagnoses anomalies, sends specific alerts to the IT team, and assists them in predicting future outages. This helps your IT staff be more productive and focus on tasks that fuel business growth.
Lastly, automation and quick remediation of IT issues result in improved time to value and save costs, thus favorably impacting the business’s bottom line.
How can Acuvate help?
Indeed, the ability of AIOps to predict and prevent incidents acts as a strategic enabler of growth and improves employee and customer experience. As seen above, the impact of AIOps on business can be measured using specific KPIs.
At Acuvate, we help clients automate operations and manage an increasingly complex IT infrastructure through AI-driven analytics and machine learning.
We have a wide range of AI use cases in IT operations that empower businesses to optimize their IT operations.
Our AIOps solutions enhance infrastructure and application performance, reduce costs, infuse agility, and improve support desk efficiency and network uptime. These include –
- Service desk automation
- Intelligent alerts and incident management
- Reduction in event noise
- Predictive analytics and insights
- Anomaly detection
- Root cause analysis automation
- Performance base-lining
To know more about our services, please feel free to schedule a personalized consultation with our AIOps experts.
You can also explore our AIOps solution capabilities for more information.
