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How IT Teams Can Unleash the True Potential of AIOps Through 5 Levels of Maturity

Sean McDermott
Windward Consulting Group

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.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

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How IT Teams Can Unleash the True Potential of AIOps Through 5 Levels of Maturity

Sean McDermott
Windward Consulting Group

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.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

Hot Topics

The Latest

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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