How AIOps Will Reshape Enterprise IT

Credit: ID 125661612 © Allies Interactive Services Private Limited | Dreamstime.com

If you don’t know much about Artificial Intelligence (AI) or how it will reshape enterprise IT, it’s time to get up to speed because the market is rapidly maturing. Not only has the use of AI in Australian enterprises grown by 65 percent in 2018 but the percent of companies that ranked AI as a “game-changing technology” increased 471 percent in the last year alone.  

Further, businesses around the globe are moving beyond mere experimentation and are implementing “AIOps,” to overcome the challenges presented by big data and the growing complexity in application environments. However, AIOps is still a fairly new concept in Australia & New Zealand. So, what is it, why should enterprises care, and what does this mean for the future of performance monitoring?  

What is AIOps?  

Originally coined by Gartner in 2016, AIOps uses infrastructure management and monitoring tools to automate data analysis and operations. It utilises big data, modern machine learning and other advanced analytics technologies to enhance IT operations. However, the definition is still highly fluid and is shifting alongside changes in the broader application performance monitoring (APM) space. But to break it down, the core elements should consist of: machine learning, performance baselining, anomaly detection, automated root cause analysis, and predictive insights.  

Why enterprises should invest in an AIOps strategy  

Over the last decade, application environments have exploded in complexity, making it difficult for IT professionals to ensure the performance and reliability of distributed systems across virtualised and multi-cloud environments.

Credit: ID 131817012 © Allies Interactive Services Private Limited | Dreamstime.com

This is where AIOps comes in. By leveraging advances in machine learning and AI, IT professionals can proactively solve problems that arise in the application environment. This helps IT professionals get ahead of problems and dodge the unplanned application downtime that costs large companies between $1.25 billion to $2.5 billion every year. Through this lense, investing in an AIOps strategy is a good way to mitigate, if not prevent, such negative impact on revenue.  

How AIOps drives collaboration  

According to recent AppDynamics research, 74 percent of IT leaders want to use monitoring and analytics tools to proactively detect business-impacting issues. However, of these IT leaders, 42 percent are still using these tools reactively.  

AIOps can help close this gap by driving collaboration between IT and business teams to provide a holistic view of data and can map application metrics to business objectives by detecting unsuspected dependencies – completely automated by AI. However, to do this, infrastructure and operations leaders need to collaborate with key business stakeholders to identify mission-critical priorities of the business relative to their corresponding applications.  

From here, the data supporting the measurement of these objectives—such as orders, registrations, renewals—can be collected. AIOps allows businesses to use this critical data to run algorithms that detect patterns or clusters in the combined business and IT data and  infer relationships between specific data sets determining causality. This has a direct impact on business operations as it allows IT leaders to make sense of these data sets and make more informed decisions based on the analysis provided by AI.  

AIOps can help a business forecast future problems – such as application load balancing errors - to increase accuracy and offer deeper insight into future events. With AIOps IT can take action sooner to mitigate negative impacts on business outcomes.  

Credit: ID 125661582 © Allies Interactive Services Private Limited | Dreamstime.com

The pattern recognition, advanced analytics and machine learning capabilities of AIOps tools also allows businesses to extend their APM tools’ historical insights into application delivery and performance to provide business impact. Through this, businesses can analyse customer behaviour—like a customer’s actions during a buggy order process—and relate it back to the events affecting the underlying infrastructure.  

With these combined capabilities, businesses can correlate IT problems with changes in business metrics and establish how changes in application performance and availability impact customer sentiment. Collaboration is the key to moving from a reactive monitoring approach to a proactive monitoring approach, and without an AIOps strategy, enterprises will struggle with this.  

The convergence of AI and IT operations will inevitably reshape enterprise IT here in Australia & New Zealand. By investing in an AIOps strategy, enterprises can have better visibility, predictability, and agility in their IT operations. It’s now up to Australian enterprises to seize the moment and start driving ROI from this collaborative approach to IT management.

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