The demand for increased business agility has made IT systems harder to manage and support, leading to a requirement for more intelligent automation.
To succeed in today’s hyper-competitive business world, organizations must digitally transform. But the disruptive IT innovations that make organizations more nimble also create hairy IT management challenges.
At issue are technologies like cloud, containers, serverless computing, mobility, and IoT. Their adoption magnifies the complexity of managing IT environments, sometimes diluting digital transformation benefits.
How? As IT environments become distributed, hybrid, and heterogeneous, IT Operations and DevOps teams alike struggle to monitor systems, detect problems and implement fixes with full visibility. This affects applications’ performance and reliability — hurting revenue, reputation, and customer satisfaction.
These problems also defeat DevOps’ ultimate purpose: to unify enterprise IT’s development and operations functions under a common, more agile approach.
This article explains how AIOps — the application of artificial intelligence to IT Operations — helps address this problem and maintain an organization’s business agility, flexibility, and adaptability.
The implications of IT modernization
Digital transformation demands greater agility from the business and its IT teams. As IT has changed, becoming agile both in development and production, DevOps has played a prominent role.
With DevOps, software pipelines become rapid, automated and iterative, allowing organizations to create, integrate and deliver code continuously, and align it with business objectives.
A recent study from Google and the Harvard Business ReviewOpens a new window found that DevOps has impacted the bottom line of two-thirds of the companies surveyed by improving productivity, speed to market, innovation, and product and service quality.
These agile processes and architectures have made IT systems more modular, distributed, dynamic, and ephemeral. This has heightened the scale and complexity of the infrastructure and application stack.
This complexity has clouded IT’s ability to anticipate downtime and to diagnose the cause of outages. The volume of self-describing data generated by the monitoring of modern IT systems has grown by an order of magnitude every five years.
When complexity affects system performance, the financial impact is real. In an Information Technology Intelligence Consulting (ITIC) surveyOpens a new window of large companies, 86% said an hour of downtime costs them at least $300,000.
The challenge for IT Ops and DevOps teams
IT Operations and DevOps teams must analyze all data generated to understand system health. No strategic data subsets will give them a vantage point to accurately describe end-to-end behavior, nor to anticipate and diagnose outages.
These data sets are too large and varied for human beings to process unassisted. Even if we could, the patterns structuring those data sets and the potential inferences exceed humans’ capacity to reason and discover.
Luckily, advances in artificial intelligence and machine learning have risen to meet IT’s complexity challenge. The robots are coming.
How AIOps can help
While enterprises have deployed commercialized AI for IT Operations for decades, AI’s centrality only became apparent in recent years. Enterprises have realized that without AI for IT Operations — AIOps — digital transformation is much harder to achieve.
AIOps is the sequential automation of five algorithms: data set selection, pattern discovery, inference, communication, and automated remediation. Its goal is reducing the length and severity of IT system outages with rapid problem detection, diagnosis, and resolution.
AIOps integrates IT infrastructure monitoring tools with IT Service Management processes as part of a more holistic approach to IT Operations Management.
IT organizations are in a position where AIOps is essential for three automation imperatives: the enhancement of human cognitive abilities, how this cognition gets communicated, and how what’s communicated gets implemented.
Without AIOps, the frequent outages to which agile systems are prone will last too long and, consequently, cost too much. The business agility required for digital transformation is placed at risk.
A recent surveyOpens a new window of enterprise IT executives conducted by the not for profit AIOps Exchange found that 45% of respondents are looking to AIOps to analyze and determine the probable root cause of incidents as well as helping to predict future problems.
Does AIOps require ripping and replacing existing IT tool sets and operational processes? The simple answer is “Noâ€. AIOps is an integrated set of technologies. Any solution employing its five types of algorithms can take input from monitoring tools and orchestrate service management technologies already in place.
What’s more, it acts as a value multiplier for most existing IT Operations Management investments — whether in tools, skills, or processes. The change such an “incident response robot†does enable is a unified and coherent view of an enterprise’s digital environment.
Without the unification and coherence that AIOps enables, such environments are proving unmanageable for humans alone. On the flip side, when IT Operations teams are able to respond quickly to outages and other system problems, the business can enjoy the benefits that IT modernization brings.