Why Your AIOps Deployments Could Fail

Print Friendly, PDF & Email

One thing that has become apparent this past year is that digital transformation is a business imperative needed to thrive in this new era of work. Innovative technology is no longer a “nice to have” – businesses must innovate in order to survive. In fact, IDC estimates that digital transformation investments worldwide will total more than $7.8 trillion by 2024.

With this shift, we’ve seen organizations flock to AI and ML deployments to streamline their IT processes. However, not everyone has the right plan to get to the success they expected. For example, some organizations that embark on their AI journey through the deployment of AIOps in their IT systems don’t take advantage of known methodologies and strategy developed by early adopters, and are set to see only small, incremental results rather than enterprise-wide changes.

AIOps stands for Artificial Intelligence for IT operations. It exists to make IT operations efficient and fast by taking advantage of machine learning and big data. However, oftentimes, IT teams struggle with manual processes and siloed legacy systems, creating extremely fragmented and disparate workflows. 

With the proper usage, AIOps enables IT teams to act with speed and efficiency and respond to issues proactively and in real-time by accessing historical context of IT issues, providing valuable diagnosis and resolution. To ensure IT teams can reap these benefits and drive ROI on AIOps investments, IT leaders need to keep in mind the following considerations in order to set themselves up for success. 

Focus Leads to Big Outcomes 

The best way for an organization to get started on their AIOps journey is to start with a single use-case, focused approach. Once they are generating the desired outcomes, organizations can scale as appropriate. Enterprises will often be too eager to deploy AIOps and will be tempted to scale too quickly or deploy an initial AIOps solution without determining the desired goals and objectives. This can be detrimental to an organization and create barriers or doubt for AIOps success in the future. 

A good way to determine where an organization’s initial AIOps deployment will deliver the biggest ROI is to take a look at IT incidents and identify issues that are regularly occurring. For instance, my team has been seeing how customers are experiencing more success with deploying AI-powered virtual agents to help resolve and reduce the influx of incident reports amid remote working. 

This is a rather small deployment; however, it’s creating massive results and great experiences. By focusing on an initial deployment that will generate the most ROI, IT leaders can showcase the power and success of AIOps and make the case to invest in even more deployments throughout the enterprise. In doing this, they can begin to establish the data-driven cultural mindset that is needed to successfully scale AIOps deployments across the business. 

Continuous Data Flows are Essential

Many organizations can also experience problems due to the lack of current and historical data that their AIOps solution has access to. This is a common issue in IT, as IT departments often struggle to organize and consolidate the multitude of information from their data sources into one place. However, this obstacle disproportionately hinders the success of AIOps deployments because AIOps relies on historical and real-time data to provide context and resolve issues as they arise. 

For instance, AIOps has the power to detect when VPN outages are going to occur and automatically resolves the outages by identifying patterns and anomalies from data. However, if AIOps can’t easily access that data, it’s basically like working with one hand tied behind your back – it doesn’t have the context and data-driven rationale to efficiently remediate IT issues. 

To ensure AIOps solutions have unobstructed datasets, IT leaders should be taking a consolidated approach to their IT systems. Many IT departments struggle with managing competing solutions that don’t often play nice with each other. Consolidating solutions allows IT to look at all assets holistically, which helps guarantee all data is funneled to a single location, making it easier for AIOps solutions to make educated decisions. 

Harnessing the Power of AIOps

As we’ve witnessed throughout the pandemic, business environments and organizational needs are constantly changing. It has never been more important for IT departments to have the necessary tools to remain agile and respond quickly to issues or outages. 

IT operations will continue to conduct mission critical work and manage the intricacies of an evolving business, and AIOps will remain a powerful tool to support IT departments with this process if deployed with the above considerations in mind. In fact, AIOps represents a prime example of how IT can harness the power of AI to improve service quality, reduce service downtime, and vastly increase operational efficiency. AIOps can ultimately ensure business resiliency in the face of the next major disruption, which is of the utmost importance given what we’ve learned from this past year. 

About the Author

Gab Menachem, Senior Director, Product Management, ITOM at ServiceNow and founder and CEO of Loom Systems (a ServiceNow company). Gab is leading the AIOps practice in IT Operations Management products at ServiceNow. He brings over 15 years of technology innovation and entrepreneurial experience. Before joining ServiceNow, Gab was the CEO at Loom Systems—a Saas company that predicts and automates IT incident resolution with AIOps. Prior to that, Gab was Co-founder and CTO of Voyager Analytics, a product using AI to analyze social network data with a range of customers that include leading financial institutions. In addition, he has held a number of leadership positions including GM and VP R&D in a microwave engineering startup.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1

Speak Your Mind