Over the last few years, the need and market for artificial intelligence for IT operations (AIOps) has grown significantly as enterprises look for solutions to scale operations while improving customer experience and overall satisfaction. As the need grows, it's predicted that 40% of organizations will implement an AIOps solution by 2022, and 55% of organizations leverage modern IT operations tools like AIOps to improve overall customer satisfaction.

While many of today's enterprises view AIOps as just another tool in the stack hoping to solve age-old problems, AIOps should be viewed as a holistic, long-term strategy. But before IT teams can envision long-term success, they must develop a foundation that both deploys modern machine learning and automation and allows them to track progress. In turn, this creates transparency throughout the organization and gives IT teams an opportunity to show their value.

I've had the opportunity to work with a number of organizations embarking on their AIOps journey. I always advise them to start by evaluating their needs and the possibilities AIOps can bring to them through five different levels of AIOps maturity. This is a strategic approach that allows enterprises to achieve complete automation for long-term success.

Here's what enterprises should know about the five levels of AIOps maturity:

Level 1: Reactive

When teams are in the first stage of AIOps maturity, siloed operations hinder communication with the rest of the business, leaving IT teams in constant reactive mode as they collect events and logs. IT teams become firefighters attempting to balance putting out internal fires while ensuring customers are satisfied. Additionally, because their time is spent solving major issues in reactive mode, they miss the opportunity to showcase their value to the rest of the business and help produce proactive strategies.

Level 2: Integrated

In the second level of AIOps maturity, operational silos become less of a barrier, and communication between IT teams and other departments becomes easier and more frequent. Additionally, data sources start to weave into a unified architecture and IT service management (ITSM) processes are improved significantly. Teams also begin to layer artificial intelligence and machine learning into the process.

Level 3: Analytical

Teams begin to reap the benefits of artificial intelligence and machine learning in the analytical level of AIOps maturity. They can define more baseline metrics to share with the rest of the organization. In turn, this gives them the opportunity to leverage data to show the overall value of IT and AIOps as it relates to overarching business goals and objectives.

Level 4: Prescriptive

By the fourth level, IT teams have nearly mastered the use of ML and automation to continue improving processes and showing value to stakeholders. In addition, the prescriptive stage optimizes the approach to ITSM processes.

Level 5: Automated

In the fifth level of AIOps maturity, full automation is implemented with little to no human interaction. Teams see complete transparency throughout the organization as they leverage ML through prescriptive models. Finally, teams are able to sit at the executive table and play a more strategic role in improving the business operations, while automation works in the background to keep the lights on.

As teams look to implement AIOps and navigate through each level of maturity, they achieve the true potential AIOps provides them, ultimately preparing them for long-term success.