Recently I was searching for verbatim “AIOps” on Google and got 624K results. Without many surprises noticed that there have been over 100 times rise in search trends since July 2017. That signifies the momentum for AI led Operations.

As my curiosity on AIOps increased, I looked at market opportunity for AIOps. From MARKETSandMARKETS analyst data, the global AIOps platform market size is expected to grow from USD 2.55 billion in 2018 to USD 11.02 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 34.0% during the forecast period (2018–2023).

In this blog post, I am attempting to capture some highlights gathered from my learning curve over a past year or so. Refer to the schematic above that provides a high-level “AIOps Framework”. The following are key elements of the framework.

“AIOps” Verbatim Defined: Simply stating AIOps stands for Artificial Intelligence for IT Operations. Extending AIOps to business operations is inevitable in near future. Adding further, AIOps automates various aspects of IT and utilizes the power of artificial intelligence to create self-learning programs that help revolutionize IT services

AIOps Context: There is a significant opportunity to leverage AI for analyzing enormous data being created by IT and business operations tools, to increase the efficiency of operations, speed up services delivery and ultimately create superior user experiences. The resulting power of AIOps is enabling the progress from siloed to integrated operations backed by intelligent insights.

Signals: In today’s business and IT operations environment, the user is adapting multiple channels of communication for ease and enriched experience. So the backend operations teams as well should expand their ability to sense, analyze and respond to such structured, unstructured and semi-structured data signals. With this in mind, the AIOps platforms are being developed with built-in capabilities to receive and response signals that can encompass any events, alerts, service requests, IoT sensor data, Email, Video, Text, Voice support, UX, Social channels and many other forms.

Interfaces: The way enterprise operations backbone interfacing with signals and external queries also is shaping up in this transformation.

• The first layer is Machine-First: Giving software/machine/bot the first act on sensing and responding to operations requisitions not only improves the automation of repetitive tasks but also augments cognitive intelligence in complementing human intelligence.
• Human-Next Touchpoints: Human-next layers take up the operations requisitions that are not solvable by machines. These are the requests which involve human interventions.
• Ensuring Reliability of Services: Alongside the above two layers, taking an engineering approach to services reliability for constant monitoring, triaging and incorporating insights from advanced analytics of enterprise data brings the culture of continuous improvements and stability to operations.

AIOps Platform: The entire AIOps ecosystem is based on the underlying Platform and Enterprise Core that ties all the components together. As Gartner defined, “Artificial Intelligence for IT operations (AIOps) platforms are software systems that combine big data and AI or machine learning  functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”

As businesses are increasingly software-driven, operations downtime is becoming more costly and slow is the new down. This is leading businesses to proactively manage and improve experiences of services, applications, cloud, and networks. Along with this business 4.0 is digitally shifting the businesses offering the technologies that increase the volume, velocity, and variety of data. As traditional systems and manual efforts are facing challenges in correlating and analyzing the data or alerts, AIOps is stepping up to augment the enterprise intelligence in operations.

To conclude, the future is bright for IT and business operations with AIOps. The increasing shift of organizations core business toward the cloud, raising investments in the AIOps technology ecosystems, exponentially growing data volumes and increasing end-to-end business application assurance and uptime are driving the growth of AIOps market demand.

Related Reading: Operational Excellence in New Age Businesses

Recommended Reading

Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations

Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. It summarizes various techniques tested by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require programmatic micro-decisioning – targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.

Accelerated DevOps with AI, ML & RPA: Non-Programmer’s Guide to AIOPS & MLOPS

It is predicted that a large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5 percent in 2018 to 30 percent in 2023. Also, only 47 percent of machine learning models are making it into production (Comes MLOPS!). This audiobook is my attempt to update you on the unfolding story of AIOPS and MLOPS as “story till now“. So here is to our continuous learning and progress! Cheers.