As IT infrastructure continues to grow across cloud, hybrid and edge environments, IT operations management (ITOM) teams find themselves drowning in a sea of information about the performance of systems, applications and infrastructure.

This challenge is compounded by the sheer volume of data exhaust (spanning logs, metrics and alerts) which surpasses the capacity of any IT professional to effectively sift through, interpret and contextualize the information. This is further exacerbated by the rapid pace of modern application updates, making it difficult to extract actionable insights regarding system health.

This predicament leaves ITOM teams stuck in a cycle of reacting to incident noise and constantly extinguishing fires instead of accelerating business innovation.

Fortunately, artificial intelligence (AI) and automation have surfaced as a crucial lifeline, rescuing IT professionals burdened by information overload. These technologies cut through the noise to enhance visibility into system health, expedite troubleshooting and automate response and remediation, resulting in increased IT agility and improved business outcomes.

AI + Automation: A Trusted Copilot for ITOM Teams

AI for IT operations (AIOps) is not a new concept in ITOM. However, many companies have struggled with adoption. Existing solutions are often fragmented, complex and aren’t user-friendly. Moreover, these solutions frequently fall short at keeping pace with the rapid evolution of IT environments.

But new approaches are breaking down the barriers for converting AI insights into automated action. For the first time, AI and machine learning (ML) techniques are effectively combined with automation capabilities to learn from enterprise IT environments and provide human-friendly insights for up to 10x faster issue resolution.

I frequently compare these technologies to having a dependable copilot or trusted advisor adept at swiftly navigating through vast datasets, extracting crucial insights and providing recommendations, including the automation of mitigation actions, at a pace exceeding human processing capabilities.

By relieving the relentless burden of continuous monitoring, AI has evolved into an indispensable ally for IT teams, enhancing their productivity and shouldering the bulk of tedious manual analysis tasks. It can correlate information collected from across cloud and hybrid environments and deliver easy-to-understand insights that all levels of IT can use to take action. Modern implementations of AIOps can even automatically spot and resolve issues, deflecting them from IT before they impact business operations and the user experience.

The AIOps Destination: Autonomic IT

When the potential of AI-driven insights coupled with automation is fully realized, it results in a state of IT operations that ScienceLogic refers to as “Autonomic IT.”

In Autonomic IT, human-friendly AI/ML and powerful workflow automation work together to continuously and automatically advise, heal and optimize the IT environment. All this is done in a transparent and auditable manner, allowing for human review and control at any step (depending on the issue type, IT policy, etc). This highly automated state of IT infrastructure and service management represents the full realization of the AIOps promise.

The potential ROI here is significant. A recent Total Economic Impact Study from Forrester revealed that integrating AI and automation in ITOM can help companies save, on average, $12 million in effort and shave 20,100 hours off the time IT support analysts spend on unnecessary trouble tickets. Besides increasing ITOM efficiency, this also reduces downtime, increases tool consolidation by 60% and maximizes IT investment returns.

In addition, auto-remediation translates to increased productivity valued at an average of $2.4 million over three years.

Autonomic IT doesn’t necessarily imply full AI autonomy. When AI detects risk, ITOM teams can retain the ability to approve or reject the suggested resolution action or devise a new one. They can also set approved workflows as automatic so they are applied to similar issues in the future.

This human-approved oversight enables organizations to continue their AIOps journey and transition to autonomous IT operations at a pace aligned with their preferences, allowing staff to remain actively involved until they are fully confident in the AI’s recommendations.

Additionally, as IT assets are added, teams can seamlessly collect and correlate new data sources and extend autonomic IT to new applications, systems and infrastructure.

Establishing a new ITOM Framework for Tomorrow

IT has traditionally concerned itself with understanding, at any given moment, the following:

  • What do I have in my IT estate (infrastructure, applications)?
  • How are they connected to delivering business service(s)?
  • What is going on right now that requires attention (service outage, performance degradation, poor user experience, etc.)?
  • What is the relative priority (service impact)? and
  • What do I do about it?

Countless tools have spooled up over the decades to address these questions.

What we see today, however, is the maturation of certain technologies (AI/ML, particularly generative AI) that promise to rapidly answer these questions and, combined with workflow automation, take action automatically. Once fully implemented, the combination will enable Autonomic IT and radically improve service delivery for the business.

Image source: Photo by Cash Macanaya on Unsplash.

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