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Last Updated on November 16, 2022 by Editorial Team

Author(s): Sathyan Sethumadhavan

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

AIOps is an emerging IT practice of applying analytics and machine learning
AI Operations for Emerging IT Practice [Image by Freepik>

AIOps is an emerging IT practice of applying analytics and machine learning to IT operations that enables reduced MTTR [Mean Time To Respond], predictive analysis, proactive performance monitoring, and provides actionable insights for faster decision-making. In addition to improving IT operational efficiency, AIOps solutions can deliver transformative benefits to both IT and business as it is defined to be the next-generation mission control and command center with predictive intelligence for many IT and business operations. Additionally, organizations or startups that prioritize agility at their core will require a high degree of ephemerality in their IT operations. The implementation of AIOps enables modern platforms to capture multidimensional data flow such as application operations, DevOps, security operations, infrastructure operations, and service management. Thus, businesses are able to operate more efficiently and effectively, which is key for many organizations today.

Early adopters

According to research from IDC, medium size firms with 250–1000 employees are the most aggressive when it comes to embracing AI adoption. Moreover, it is also predicted that 75% of global IT organizations will have adopted automated operations in order to support just-in-time operations, and data proliferation at an unprecedented scale. Additionally, the pandemic underlines the importance of implementing AIOps for every industry, especially to a certain extent. Financial services, retail, media, and healthcare are driving the demand for AIOps adoption as data is now required to be available on all customer experience platforms.

The APAC region, in particular, is a leader in a multitude of industries, such as contact-center, manufacturing, customer service, and financial services. In order to remain a leader in these sectors, secured data must be made available anywhere, and automation must be treated as a first-class discipline. Adoptive operations help create efficiency and bring together the platform with talent. AIOps use cases will also span all industries in the future as the next phase shifts from cloud to edge computing. Early use cases of AIOps include predictive analysis, anomaly detection, intelligent alerting, natural language processing [NLP], and correlation/cohort analysis, which provides instant benefit and quantitative metrics in the form of customer experience KPIs.

Myths and Misconception

In today’s world, while many IT leaders are considering implementing AIOps to enable proactive approaches to performance, they must also consider challenges that come with the adoption of AIOps — Such as embracing new business processes, mastering new skills, and integrating newer platforms into enterprise systems. However, these are some common myths when it comes to AIOps:

  • By implementing AIOps, autonomous decisions are made by the machines.

It enables humans to interpret data faster by detecting anomalies, highlighting patterns/trends, and possibly finding root causes. It is still a long way off for machines to make autonomous decisions as it requires advances in observability, automatic remediation, and workflow automation.

  • In AIOps, humans are replaced by robots to complete business processes

It is only through the elimination of mundane, repetitive tasks and the handling of regular routine tasks that robots are making an impact — designed to augment humans and enable them to switch their attention to higher-value tasks with better efficiency and creativity.

  • AIOps are used for large-scale operations

AIOps solutions help build AI-enabled product lines from start to finish using well-defined systems and frameworks. Irrespective of size, it enables business agility, time to market, and shared services for all functions, from product ideation to application production.

Benefits of AIOps

By implementing AIOps, we can proactively prevent failures and resolve problems before they happen. Data, people, process, product, and platform should all be a part of an organization’s AIOps strategy in order for it to be fully beneficial. The strategy should also cover three key areas:

  • Organizational capability for scale and AI governance
  • Operational competencies for lifecycle management
  • Analytics platform modernization for technological advancements

Since AIOps is data-driven, it requires organizational-wide data to be ingested into a central data lake platform. For instance, in order to analyze outages, data from applications, networks, computing, and security must be brought in to create traceability. Additionally, throughout the organization, stakeholders need to be informed about the transformation process, the data-driven mindset, AIOps as an enabler, and a roadmap for upskilling, so that the symphony between different departments and individuals is clearly understood. The implementation strategy should begin by identifying high-value, less complex, and low-effort problem statements along with a roadmap for continuous improvement.

Next for AIOps

According to The Insight Partner, the AIOps market is estimated to grow at a CAGR of 32.2% from 2021 to 2028. In today’s world, AIOps is mostly focused on detection, root cause analysis, and recommendations. The future AIOps solutions would focus on data fabric, such as data with automation, lineage, security, governance, and access, as well as observability, edge computing, and hyper-automation.

This would pave the path towards actionable AI insights, mitigate risks with explainability, and scale operations to support business agility.

AIOPS was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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