Artificial intelligence for IT operations (AIOps) is proving its value to large enterprise companies looking to scale their operations and increase customer satisfaction. In fact, according to a survey from the AIOps Exchange, 84% of IT leaders said they were planning and budgeting for an AIOps project in 2019, and 50% were already relying on automation to improve the customer experience. Yet, AIOps isn’t just another automation tool for enterprises — it’s a holistic strategy. When IT teams consider an AIOps strategy, they must start with the vision for long-term success, develop a foundation to identify how they will track this success, and then deploy Machine Learning and Automation to drive business value and transparency across the entire organization. I’ve been in this industry for nearly two decades, and I always encourage companies to start their AIOps journey by evaluating their current and potential capabilities through five levels of AIOps maturity. This helps them achieve the ultimate promise of fully automated operations in a sophisticated, strategic approach.
Level 1: Reactive
In the first level of AIOps maturity, events and logs are collected for reactive purposes. Teams attempt to work through siloed operations with little to no dialogue with the rest of the business and are constantly putting out fires to keep the business running and customers happy. This hinders the ability for IT teams to prove their value to business leaders, as they are stuck in reactive mode, rather than proactive.
Level 2: Integrated
In the integrated level, silos begin to break down and dialogue among IT teams and the business becomes more frequent and productive. Data sources are integrated into a unified architecture, and overall ITSM processes begin to improve. Additionally, Machine Learning and Artificial Intelligence begin to layer into the overall process.
Level 3: Analytical
In the analytical level of AIOps, teams see significant improvement across the board with more AI and ML capabilities. There’s more transparency of data among all stakeholders and the business as a whole, and teams have more defined baseline metrics. When data is available, and metrics become more measurable with the use of AI and ML, it increases the opportunity for IT teams to support the need for AIOps and show the overall business value.
Level 4: Prescriptive
Teams begin implementing Machine Learning and Automation in the prescriptive level of Artificial intelligence for IT Operations, giving them access to more analytics and data to track overall improvements. Additionally, a more optimized ITSM process with human interaction is put into place in the prescriptive level.
Level 5: Automated
In the final level of AIOps maturity, there is full automation with no human interaction, and teams are able to leverage Machine Learning based on prescriptive and predictive models. This provides full transparency across all levels of the business and allows businesses to operate proactively rather than reactively.
As teams maneuver through each level of AIOps maturity, it’s essential to keep the long-term AIOps strategy and goals at the forefront to fully unlock the true potential of AIOps. It’s important to take the process one step at a time in order to maximize performance and goals — after all, it is a journey.
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