What is AIOps?

The increasing complexities and pressures of digital business transformation is forcing businesses to reconsider how they guarantee infrastructure and application performance. Demands for faster, always-on business services, and an exponential growth in data and incidents, means IT operations are noisier than ever. There are more and more considerations and issues to which a human must respond, react and address.

Gartner has defined a response to ease and overcome these pressures. AIOps, which stands for ‘Artificial Intelligence for IT Operations’, describes the paradigm shift that will enable businesses to cope with accelerating digital business transformation. AIOps employs automation, big data analytics and machine learning to streamline and enhance IT operations with proactive, personal and dynamic insight. In doing so, AIOps will restructure how businesses manage IT. Because AIOps depends upon large datasets, IT operations should be reorganized according to the data sources used, rather than by existing technology types or infrastructure layers.

AIOps enables businesses to rapidly innovate at the core of their operations. This means they can meet the increased pressures of digitalization today and focus on strategies for future success. It enhances existing IT operations, application and network performance monitoring tools used by IT teams to keep the business running. AIOps helps IT teams leverage the insight and revenue potential of big data, making them essential to business success.

By 2022, 40% of global enterprises will have adopted an AIOps platform to support two or more major IT operations functions, up from fewer than 5% today.

Gartner, ‘Market Guide for AIOps Platforms’ (2017)

Putting the ‘AI’ in AIOps

Machine learning provides the artificial intelligence element of AIOps. Machine learning is a current application of AI based on the idea of giving machines access to data, and letting them learn for themselves without being explicitly programmed or configured. The theory and algorithms upon which machine learning relies are not new. However, it is only with the development of big data analytics that machine learning can be used to improve performance across the whole IT management suite. Specifically, it can be used to discover patterns in data to describe historical conditions, and can make predictions for future planning.

Presently, AIOps functionality is primarily used for monitoring event management and application performance. It has achieved notable success in reducing mean time to resolution (MTTR) by alerting IT teams to issues before they impact business performance. The machine learning that powers AIOps can detect abnormal activity on the network and automatically trigger troubleshooting processes without needing human attention or intervention.

Machine learning algorithms and analytics draw upon data from across IT. This is delivered by the underlying network. Because network conditions change so quickly, machine learning and analytics tool need to collect data from a unified source or risk that data being inconsistent, incomplete or not up to date. The quality of the insights and predictions produced by AIOps is dependent upon the quality of the underlying data fed into it.

Augmenting the human

AIOps enables IT teams to proactively contribute to business success both by preventing costly downtime and providing the insight needed to improve business performance. The automation enabled by machine learning is part of the general move in analytics towards augmentation – machines helping humans. Machine learning carries the burden of big data analytics, fixes faults where necessary, and forecasts future trends.

Just 6% of businesses employing AIOps currently apply it to IT operations beyond monitoring.

Gartner, ‘Market Guide for AIOps Platforms’ (2017)

These processes improve employee productivity, increases agility and frees IT teams to consider the bigger picture – applying these insights for the benefit of the business and customers. This augmentation of IT’s capabilities is crucial because, unlike in the previous era, IT operations are no longer restricted just to serving the employees of the business. Digitalization means they have a direct impact upon the customer experience. As customers and markets demand business services become faster and more reliable, AIOps helps IT teams shift the network’s focus to provisioning for the devices and apps dependent on their network. Machine learning increases the speed and accessibility of services to meet this demand, meaning IT teams can create greater value for end customers.

The development of AIOps is reflective of the trend in digital transformation that has seen applications and services brought together. Every business application, service and exchange depends on the digital infrastructure, making IT a valuable business asset. AIOps gives IT teams more insight into the infrastructure to help the business stay competitive and meet customer demands. Its success has been such that Gartner research suggests AIOps functionality is now increasingly being applied to use cases beyond solely monitoring. Businesses are looking to improve ticketing processes, CMDB capabilities and automation at the interface between development and production, demonstrating the effectiveness and potential of these processes.

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