Here is how CIOs can use AIOps to create a strategy and foundation for the digital future.

CIOs and operations teams across the globe are tuned into the rapidly developing area of AIOps (Artificial intelligence for IT operations). As Artificial Intelligence and Machine Learning continue to evolve and advance, IT professionals are rightly looking at how they can apply these technologies to the areas of ITSM and ITOM in order to gain the efficiencies afforded by intelligent automation.

In this article, we will explore the rise of AIOps, as well as how AI and ML, when combined with automation, can drive better decision making, optimize resources, and ultimately enable businesses to operate in a more intelligent, intuitive way. More importantly, we will outline how CIOs can create a strategy and foundation for the digital future.

What are AIOps?

Today’s IT professionals are being increasingly challenged by the need to ensure maximum performance and reliability across distributed systems and complex, multi-cloud environments. These challenges are further compounded by the massive volume of data that is rapidly being generated – data which must then be gathered, analyzed and transformed into meaningful action.

Amidst these challenges, making sure that urgent and high-severity incidents are identified and resolved quickly can be difficult. This is especially true for IT operations teams working in disconnected silos – a problem that still exists in shocking numbers. To address these and other pressing concerns, more and more organizations are turning to artificial intelligence for IT operations (AIOps) to connect between business and IT teams and systems’ silos.

Gartner, who originally coined the term, defines AIOps as the process of using “big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight.” They further go on to explain that: “AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

In simplest of terms, AIOps combines Machine Learning and Data Science and applies it to the pressing problems facing IT operations today. AIOps platforms are designed to enhance and in some cases even replace primary ITOps functions while simultaneously consuming, analyzing and transforming data into useful insights.

Real World Impact of AIOps

So, what does all this mean, both from the vantage point of an ITOps team in the trenches as well as a thirty-thousand foot overview of the enterprise as a whole? Let’s take a look at three key ways AIOps will have a measurable impact.

Streamline the IT Environment

As mentioned, one of the biggest hurdles today’s IT operations teams struggle with is the existence of silos. In addition to the obvious lack of collaboration, data can become trapped within those silos, causing costly delays and the potential for high-severity outages. Meanwhile, highly-skilled agents are wasting precious time on routine, manual tasks instead of applying their abilities to more purposeful business initiatives.

AIOps frees up trapped data to create a centralized pool of information from which both real-time insights as well as deep, historical knowledge can be drawn. This accelerates the root-cause analysis and remediation process, thereby reducing and preventing downtime. At the same time, underlying automation takes over tedious manual tasks – including IT support – facilitating better end-user service and freeing up talented IT personnel to allocate their human resources more effectively.

Real-Time, Data-Driven Decision-Making

According to a recent survey of over 6,000 global IT leaders, 74% say they want to use monitoring and analytics tools to proactively detect issues that will impact their organization. Unfortunately, 42% of those IT leaders are still using these tools from a purely reactionary standpoint. And as we all know, even just a few brief moments of downtime can have costly and lasting ripple effects.

AIOps is designed to provide a holistic view of data while also automatically connecting metrics to key business objectives. So, rather than having to rely on incomplete or inconsistent data, as has typically been the case in the past, IT and other organizational leaders can leverage artificial intelligence to access highly accurate data-based models and simulations. This eliminates the guess-work and facilitates much more confident decisions based on real-time and up-to-the-second data gathering and trend analysis.

Forecast and Prevent Future Problems

They say the best defense is a good offense, and this is certainly true in the context of IT operations. In particular, ITOps teams can now lean on machine learning algorithms to help detect anomalies and patterns that may lead to future problems so they can be addressed before they have a chance to have an impact. When organizations can leverage the power of data to proactively navigate the path forward, they can better position themselves for a more successful, sustainable future.

Laying a Foundation

According to a recent survey by PMG, 68% of respondents agreed that automation could help lower the costs of IT operations. Furthermore, 82% acknowledged that automation has fundamentally changed the way cloud and virtual environments are managed and 65% credit automated technology as being instrumental in integrating and managing Big Data.

Nearly all survey respondents (98%) agreed that automation already provides clear and measurable business benefits, including:

  • Improved customer satisfaction
  • Increased productivity and subsequent gains
  • Enhanced knowledge sharing
  • New product and/or service delivery
  • Data-driven decision-making

In order for AIOps to truly generate measurable benefits across the enterprise, however, it must be aligned as closely as possible with broader organizational goals. For those CIOs looking to start laying the groundwork for digital transformation, here are a few recommended steps to follow:

  1. Identify existing use cases
  2. Determine a minimum set of data and agree on a system of record
  3. Establish success criteria
  4. Set clear roles and responsibilities
  5. Experiment, iterate and standardize where possible

To put it plainly, the future isn’t tomorrow. It’s now. Only those IT leaders that recognize this will avail their organizations of the virtually limitless opportunities afforded by AIOps. In fact, Gartner predicts that by 2023, 40% of enterprise-level I&O teams will use AI-augmented automation, resulting in higher IT productivity with greater agility and scalability. Will your organization be one of them?

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