Why Your Enterprise Needs AIOps More than Ever

By Richard Chart, Chief Scientist and Co-Founder, ScienceLogic.

  • 1 month ago Posted in

Today’s enterprise IT estates are incredibly complex. Rapid adoption of hybrid cloud architectures and diverse technologies, infrastructure, and applications is essential to digital transformation, but this spread of tools and services simultaneously poses a significant challenge for organisations striving to manage their IT operations and deliver top-notch business services and customer experiences. 

As IT operations (ITOps) teams grapple with an intricate web of interconnected services, there’s mounting pressure to monitor, maintain, and optimise systems to meet service level agreements. But IT’s capacity to discover digital assets, extract data, sift through alert storms, and gain intelligent insights has now surpassed human processing abilities. 

And, with more digitisation, there’s even more data to deal with. Tool sprawl has created silos of analysis hindering teams from gaining full observability over IT estates, and obstructing their view of operations, making it challenging to take swift action and resolve issues before the business is impacted. 

In the unending pursuit of operational efficiency, artificial intelligence (AI) for operations (AIOps) no longer feels like an option – it’s the way forward. 

How AI enables modern IT operations

AIOps is not a new concept. But, until now, ITOps teams have struggled to extract meaningful, actionable insights from their AI implementations. Much of this difficulty stems from the complex and siloed nature of modern IT estates, compounded by the lack of human-friendly interfaces, but technology leaders also often find it hard to make AI work for their specific business needs or scale its deployment across the enterprise.

But innovative approaches have emerged that take AIOps to the next level. 

To accelerate the adoption of AIOps, these novel methods combine AI insights with a user interface designed for simplicity and comprehension. Once fully developed, AI functions as a co-pilot, analysing vast amounts of data across various cloud platforms and distributed systems, highlighting critical findings about system health in a consolidated and digestible manner.

In doing so, AI enables IT teams to view their digital environment as a cohesive system, eliminating blind spots in infrastructure visibility and automating troubleshooting and solutions in real-time. 

When performance issues arise, AI-based anomaly detection tools quickly determine the root cause. These are coupled with generative AI components capturing and leveraging organisational knowledge to recommend automated remediation actions. These actions are designed to be easily comprehensible by IT teams and can reduce the signal-to-noise ratio and expedite mean time to repair (MTTR).

Moreover, modern AIOps can discern performance patterns and anomalies autonomously, even rare events that have not occurred before, without requiring explicit user guidance, streamlining workflows and ensuring optimal service uptime.

How can enterprises realise modern AIOps?

Setting aside the advantages of AIOps, integrating AI and automation into IT operations management (ITOM) necessitates change in both tools and processes.

This shift starts with ensuring teams understand why things are changing. Whether it's to reduce downtime, speed up analysis, consolidate monitoring tools, or simplify IT and business processes, everyone from ITOps to DevOps must be on the same page and work toward the same goals.

The primary obstacle for any enterprise is complexity. The legacy state of ITOps, characterised by disparate monitoring tools and dashboards, can lead to mistakes and inefficiencies. Implementing a unified framework for automated device discovery, data synchronisation, analysis, and event reporting can streamline matters and unite data and personnel so that coordinated action can be taken to achieve desired business outcomes.

In order to attain this reality, ITOps teams first need to understand their infrastructure, no matter how complex it is. This means mapping each element of their hybrid IT environment, identifying interdependencies, and understanding how each part of the estate connects to business service offerings. The reality of modern IT environments is that nothing is static. Applications move based on load patterns or scheduled activities, traffic paths change to avoid failure points, new components are integrated - all creating a rich tapestry of dynamic relationships.

ITOps team must rely on tools at-home in this dynamic, hybrid cloud world to ensure that the model of the entire tech stack is complete and current, achieving full observability over the entire IT environment.

With data now collected and consolidated, AI can dig through it, analyse it, and deliver useful insights into how the system behaves and performs. It can also suggest actions to fix any problems. At the same time, ITOps teams can set up automated workflows to do specific tasks, like rebooting servers or applying patches, based on what the AI recommends. 

Moving to a business-first architecture

In terms of business outcomes, modern AIOps is transformative. It enables IT pros – even Level 1 and 2 engineers – to predict issues before they occur, identify root causes, and address problems before they impact the user experience. With the aid of AI and automation, they are empowered to tackle tasks that previously required Level 3 expertise and interventions. Which in turn, frees valuable IT resources to focus on growth initiatives and furthering technology innovation. 

Human-friendly AIOps also eliminates the need for a broad range of monitoring tools by aggregating IT information across clouds and devices, and deriving insights that every team can use to inform responses and resolve issues. 

Of course, full task automations don’t need to happen at once. As enterprises advance along their AIOps journey, human oversight and control is vital to reducing risk and ensuring optimal outcomes. Some organisations may choose to automate only specific systems or processes. As human operators gain confidence in AI’s ability to match their intentions, they can move towards a state where AI works alongside them, a trusted copilot directing their actions without the need for constant oversight. In fact, according to Gartner, 84% of organisations view AIOps as a pathway to a fully automated network.

This journey culminates with a state of “autonomic IT,” a fully realised state of AIOps, where technology estates and IT capabilities don’t simply run themselves, but are self-empowered. Autonomic IT operations combines data, AI, and automation across every area of observability, analytics, and remediation to monitor, optimise, and even heal technology investments while they run. And with tech running itself, IT teams can achieve and deliver even greater value to the business.

It's time to unlock IT and business potential with AIOps

Imagine seamless integration of tools, complete visibility into hybrid-cloud setups, and minimal disruptions that are resolved before affecting customers – this is the essence of what AIOps offers. And in today’s complex modern enterprise, it’s a necessity.

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