AIOps is a Machine Learning driven technology stack that captures operations and performance data from across the IT landscape and uses that data to automate routine manual processes.
Building responsible technology has become a top priority for enterprises across industries as understanding and scrutiny of the challenges connected with AI have grown. Responsible AI evolves from a “best practice” to the “high-level principles” and guidelines required to drive system-level change, building trust around the business world. Regulators are taking notice, also advocating for regulatory frameworks surrounding AI that include enhanced data protection, governance, and reporting standards.
The current business environment across industries is changing rapidly, causing software and systems to grow more complicated. Cross-functional teams; are expected to generate solutions more quickly and efficiently in these circumstances. Site Reliability Engineering (SRE) and Network Operation Center (NOC) teams in the DevOps setup; are continuously flooded with a mountain of data. At the same time, these teams are increasingly under pressure to handle many dashboards, frequent alerts, and signals from multiple tools, making it harder to identify the root cause of problems/instances. AIOps as a solution; assists these teams in finding solutions to the problems more quickly and effectively, allowing them to focus more on creative work and the software development process.
Businesses are intrigued to start investing/buying an AIOps solution in order to transform their business. But factually, many business leaders and technologists fail to understand that success with AIOps lies within the right preparation and not just limited to the technology adoption.
Enhancing the application development lifecycle is a potential use case for AIOps. Applications are constantly changing as a result of digital transformation, with new iterations frequently surfacing each day. A domain-agnostic solution can be “Shift Left” in the lifecycle to provide observability and control to Site Reliability Engineering (SRE) and Network Operations Center (NOC) teams in DevOps set up that were previously available to ITOM and ITSM teams.
Before discussing the bottlenecks of businesses implementing AIOps, let’s understand AIOps in a nutshell…!
– According to Gartner, “AIOps combines Big Data and Machine Learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.”
It defines artificial intelligence in IT operations and workflow. It refers to the strategic application of AI, Machine Learning (ML), and Machine Reasoning (MR) technologies across IT operations to simplify, streamline, and optimize the use of IT resources.
In a nutshell – AIOps is designed to address the growing challenges of complex IT infrastructures and help organizations make the transition to a better, more efficient future. AIOps can be used to improve IT productivity and reduce costs, automate repetitive tasks, create a more predictive environment by collecting data from multiple sources and analyzing it using ML/MR technologies, reduce time-to-market for new innovations, etc.
Supporting this fact are the other key findings that say –
And the reason they fail to implement or take full advantage of AIOps!
One of the critical factors driving market growth of AIOps is the growing demand for automation across industry verticals, such as banking, financial services, BFSI, healthcare, automation, IT, and logistics.
Businesses use AIOps to prevent and control security breaches by monitoring the activities and transactions of employees, customers, and external agencies. In accordance with this, the Covid-19 influence has resulted in widespread enterprise adoption of the work-from-home (WFH) trend, which is considerably contributing to market growth. When working remotely, AIOps is applied to improve information security and many other operational workflows effectively.
Various technological breakthroughs in cloud-computing solutions are also contributing to the AIOps growth. Businesses across industries are using cloud-based solutions for major business applications, performance, network, and security management that are gaining popularity at pace. AIOps also contributes to improving the overall efficiency of the infrastructure for service delivery with less manual efforts. In other words, considering the relentless improvements in the IT infrastructure, specifically in boosting the economies with extensive research and R&D initiatives, are expected to propel the market growth.
“There is no future of IT operations that does not include AIOps. This is due to the rapid growth in data volumes and pace of change (exemplified by rate of application delivery and event-driven business models) that cannot wait on humans to derive insights.”
~ Gartner
“The global AIOps market is expected to exhibit a CAGR of 21.2% during 2022-2027”
The past and current aftermath COVID-19 outbreak has given the AIOps market a significant boost. As AI-based IT operating solutions become more common, the industry has accelerated significantly, allowing businesses to reduce workflow and resource effort; allowing to focus more on innovation. During the pandemic, AIOps provided various benefits to businesses, including automating monotonous processes, delivering meaningful reporting, and improving risk management. As a result of these efforts, the industry’s post-COVID-19 repercussions have increased drastically.
In the United States, the domain-agnostic segment is expected to produce roughly USD 500 million in revenue by 2023. These solutions primarily rely on monitoring tools to collect data and respond to changing customer prerequisites. AIOps platforms are being used by businesses across the U.S to compete with and replace several traditional monitoring tools in practice. IaaS and observability monitoring is being done comprehensively within AIOps platforms, specifically if the company’s whole IT infrastructure is in the cloud.
The SME segment in Singapore is expected to grow at a 30% CAGR through 2027. It has been observed that SMEs are increasingly turning to AI-based IT operations tools to improve service quality and respond to changing customer needs in a more flexible manner. In contrast to this observation, below graph illustrates the enterprises vs. SMEs AIOps scenario in 2020 and its growth prediction in 2027.
In 2020, the real-time analytics segment in Germany amassed 35% market share followed by other segments such as application performance management, network and security management, and infrastructure management. Since then, AI is increasingly being used by businesses around the country to make insightful decisions rather than depending on human interventions, and to empower IT teams to take immediate action. The below graph denotes, by the year 2027 real-time analytics and infrastructure management sharing the equal market share.
The rapid growth and requirement of AIOps applications in the BFSI sector is one of the primary factors for the global AIOps market’s rise. As part of banking operations, employees, clients, and external agencies engage in a variety of regular and irregular activities and transactions. These operations must be closely monitored due to their complexity and confrontations. Considering these developments, AIOps is expected to boost market growth in 2022 and ahead while offering wide verity of applications to monitor real-time data and automated issue resolution.
I&O leaders must help their organizations become more agile and responsive to the needs of business units. The right operations services, such as monitoring and metrics, can help you continuously improve your systems’ performance and responsiveness. It should start with focusing on the development of a “change engine” within the organization, using automation to reduce manual tasks, errors and inconsistencies and leveraging technology to meet compliance requirements. Thus, these I&O leaders should focus on:
AIOps will result in a sweeping change in how AI-driven IT operations are managed. Even though the primary objective for AIOps adoption is to address operational issues arising from a highly complicated IT ecosystem and keep up with a fast-paced business environment, the ability of humans to understand and trust its insights, decisions, recommendations, and predictions will be extremely crucial.
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