Not Another DevOps Clone – The Growing Business Case for AIOps

Not Another DevOps Clone – The Growing Business Case for AIOps

Here’s a bold prediction – if the last decade was about DevOps, the current decade may end up being all about AIOps.

The increasing number of infrastructure incidents has made it hard for the IT support teams to respond, close, and manage the tickets on time. This couldn’t have come at a worse time too when you consider how central the IT infrastructure has now become to enterprise performance. Any gaps here now materially impact the operations, and inevitably, business outcomes too.

To address this issue, IT Ops has identified the potential of combining AI with operations. It has given rise to a new strategy of IT management called AIOps.

Gartner was the first to define it as – combining big data and machine learning to automate IT operations and for “event correlation, anomaly detection, and causality determination.”

Although an emerging trend, AIOps seems to have rapidly gained attention among technologists. According to Allied Market Research, AIOps is poised to reach a market value of $644.96 billion by 2030.

AIOps – A Primer

AIOps is sometimes misunderstood as a DevOps clone as both alter the functional dynamics of the operations team. However, there’s a difference. DevOps is a combination of people, processes, and products. It enables collaboration between the development and operations teams in the interest of allowing faster and more iterative development and releases.

In AIOps, AI is combined with IT operations to automate and enhance some crucial operational tasks, largely related to the IT infrastructure. DevOps focuses on accelerating the development speed, while AIOps focuses more on reducing the turnaround time for operational requests and eliminating human errors.

Let’s learn more about the benefits, potential use cases, and challenges of using AIOps.The Benefits, Use Cases, and Challenges of AIOps


Observability is the process of collecting data, including that from APM systems, endpoint performance tracking tools, and other such digital assets in an enterprise, to deduce the state of an entire system. Observability aims to get a holistic view of the whole system and its functioning across the entire enterprise. This view enables teams to monitor solutions in their entirety and make decisions that will enhance customer experience.

Reduces downtime: According to Gartner, a company could lose $5,600 per minute to $30k an hour due to unexpected downtime. AIOps can help companies to reduce downtime and save revenue and reputation. It can help the Site Reliability Engineers (SRE) team detect and resolve issues that could lead to unexpected downtime or damages.

Helps with proactive planning: AIOps helps the IT Ops team, infrastructure teams, and operation leaders with resource planning, suggesting workload migration strategies, and planning asset management.

Speeds up time to resolution: AIOps cuts down the noise and distractions, finds the exact root cause of an issue and offers solutions that could help resolve the problem faster. This helps the engineers to reduce the mean time to resolution.

Decrease operational costs: Manual incident management is cumbersome and time-consuming. Not to forget, it’s expensive too. As the volume of incidents and complexities increase, the operations team will have to add more headcount to address the problems. AIOps automates the workflows and provides actionable insights to the operations team. This frees the staff’s time, reduces the need to hire additional resources, and streamlines the operations, which helps in reducing operational costs.

Use Cases

Anomaly detection and remediation: AIOps helps in sifting through a large volume of data to identify events and data breaches that could potentially impact the business or attract fines and negative PR and result in deteriorating brand value. It can also automate the remediation process or offer the best possible suggestions to remediate based on historical data.

Cloud migration: Many interdependencies, such as multiple cloud environments and vendors, are involved when companies migrate their workloads to the cloud. This could make cloud migration complicated and result in operational risks. AIOps provides clarity on the interdependencies and helps in reducing operational risks.

Asset monitoring and performance: With AIOps, the team will get an accurate picture of the lifecycle of several IT assets. It helps them identify aging assets and reduce risks that could emerge from using them.

DevOps management: AIOps can help reduce the gaps and friction between the development and operations teams and improve the alignment between them. It allows the development team to get better visibility of the environment and states and provides the Ops team the visibility on how the developers are making changes and deployments in the production environment. It also provides automation support to help the IT teams make DevOps implementation successful.


While the benefits and uses of AIOps are quite visible, there are certain challenges as well that the IT Ops team faces during implementation.

Interoperability issues: Interoperability is an issue as it can’t be integrated with legacy systems due to limitations in integrations. This makes the data required to enable AIOps inaccessible. It could also leave out some tickets or incidents that have originated from the service desk, making it hard for the IT teams to close all the tickets on time.

Data inconsistency: Every type of data produced by the IT Ops team is different. This could overwhelm the AIOps platforms and make it hard to use them for data and prediction modeling. To address this problem, the team will have to take stock of the different types of data and treat them differently to extract events from the AIOps platform.

Poor data: AIOps relies on data to make accurate observations and decisions. However, many companies do not have good-quality data. For example, there was a company where 70% of the service tickets did not have a proper categorization.A study on other companies revealed that the overall data structure was not standardized. In some cases, tools could help bring some order to the chaos but often these are process or practice issues that are beyond the ambit of tools. Some companies, for instance, had a textbox where the IT team could add their notes or descriptions under each ticket. In many cases, the data is either inconsistent, incomplete, not useful or full of noise. This makes it hard for the AIOps platform to operate at its optimum level. For analytics and AIOps to function well, the data quality must be good and consolidated in a unified platform. Applying analytics on unstructured or non-standardized data could make the outcome unreliable. Fixing this demands applying a smart combination of tools, technology understanding, domain knowledge, and enterprise experience. As is apparent, that combination isn’t readily available to most organizations.

Making a Business Case for AIOps

As digital transformation takes place, companies must prepare for complexities, large-scale outages, and operational challenges in IT. Most companies do not have people with the right skill sets. Even if they do, they cannot always take preventive measures or remediate issues on time.

AIOps can address that problem by reducing alarm fatigue, predicting outages and preventing them on time, and protecting the assets from risks. However, for AIOps to work well, companies must design a well-planned strategy and choose the right platform before implementing it. They must also define the KPIs and measure the performance regularly to ensure they meet the expectations. This will enable the companies to make the most out of AIOps and manage IT Ops efficiently.

You can browse