AIOps have come a long way from its defining moment back in 2017. That year, Gartner coined the term to underscore the evolution IT teams would have to undergo to efficiently operate in businesses undertaking digital transformation. Five years later, Gartner’s fortuitous evaluation of IT’s turning point has proven true: artificial intelligence and machine learning are increasingly incorporated across applications deemed critical to IT management.
Benefits of AIOps
AIOps (artificial intelligence for IT operations) are platforms that use machine learning and analytics to automate and enhance IT operations, including monitoring and service desk functions. The goal of AIOps is to offer insights gleaned from multiple data sources and various analytic technologies to enable:
- Aggregation and correlation of operational data generated within complex IT operations (ITOps), including from applications and performance monitoring tools.
- Identification of patterns and events related to systems performance.
- Better, faster response to IT issues and deployment of IT fixes, including automated troubleshooting and problem solving.
AIOps moves IT teams and infrastructures away from siloed architectures to a single, automate platform that allows IT professionals to proactively and quickly respond to innumerable IT challenges, including outages and slowdowns. As traditional IT infrastructures take on more complex configurations that operate across multiple physical and cloud-based environments, AIOps becomes a tool for IT professionals to make informed, data-driven decisions.
Also read: How AI Might Change the BI Experience
AIOps: The Road Ahead
The AIOps platform market size is expected to grow from USD 1.73 billion in 2017 to USD 11.02 billion by 2023, according to MarketsandMarkets research, representing a Compound Annual Growth Rate (CAGR) of 34.0%. In a world where businesses are increasingly upgrading their IT operations to reflect the rapid shift to digital processes, AIOps, with its promise of speed and accuracy in solving wide-ranging IT problems at scale, is a compelling value proposition.
Here are five trends that are currently defining AIOps.
1. AIOps and Cybersecurity
A 2019 Varonis report found that 15% of organizations leave more than 1 million files and 22% of all folders accessible to employees, while 61% of companies have more than 500 users with passwords that do not expire. These pathways to potential data breaches and cybersecurity threats are joined by the rising use of edge computing and the growing complexity of IT infrastructure that must process gaggles of data across a collage of cloud and on-premise architectures.
Today, IT decision makers are seeing the value in using AIOps to bolster their cybersecurity by integrating the platform with other security tools. Strong AIOps platforms offer full visibility into your organization’s data, enabling proactive action in identifying, isolating, and addressing security threats. Using AI and analytics, the automated marriage of AIOps and security creates insights into your company’s typical operations across departments — a baseline that allows the system to then perform continual reassessments that set off alarms if set parameters are breached. IT security teams, now informed of the threat, are then empowered to eliminate it.
Also read: How AI Will Be Pushed to the Very Edge
2. Remote Work Boosts AIOps
The COVID-19 pandemic has been a world-altering event, reshaping how and where we work. While many organizations were able to make the transition from an in-office to remote workforce, the learning curve was steep and new operational obstacles arose. In addition to cybersecurity risks, the new work-from-home model has forced revaluation and restructuring of IT support.
In its report on the impact of the acceleration of digital transformation on 528 IT, DevOps, and Site Reliability Engineering (SRE) professionals, Transposit’s recent survey, The State of DevOps Automation, revealed:
- 90.4% of respondents saw an increase in service incidents that have affected their customers since the start of the pandemic.
- 93.6% stated that, on average, incidents take longer to resolve while working remotely.
- 68.4% reported that the cost of downtime has increased since the pandemic started.
As organizations move from pandemic-induced WFH to permanent hybrid work models, the imperative to streamline ITOps becomes more urgent, especially as not doing so will stymie productivity across all departments. Transposit’s recommendation — to hire more SREs and introduce better, reliable automation — underscores the value of AIOps to:
- Improve visibility and monitoring of your organization’s remote and enterprise systems.
- Reduce response time to problems identified through machine learning.
- Reduce remediation time by automating responses and setting alerts for issues that need IT assistance.
Also read: AI Priorities Shift in Wake of COVID-19 Pandemic
3. The Marriage of DevOps and AIOps
Hand in hand with improving IT support across all work models, the continuous collaboration between ITOps and DevOps teams can be refined and streamlined with the integration of AIOps. DevOps, which is focused on bridging the gap between development and operations, is spurred on by automation and agility. Integration with AIOps will help streamline the six stages of DevOps — plan, build, integration and deployment, monitor, operate and continuous feedback — by providing monitoring, testing, and security through this development cycle. As AI and machine learning continue to improve, the integration between AIOps and DevOps is on track to get tighter in the coming year.
4. Observability and AIOps
With IT and network architectures growing more complex, IT and DevOp teams are tasked with identifying, monitoring, solving, and preventing system problems as quickly as possible. Observability, which allows these teams to view the raw data (metrics, traces, logs, and events) generated by these systems and immediately directly perform analytics on this data, is a game changer.
In addition to giving DevOps and ITOps the tools to effectively monitor and identify issues, observability provides visibility into the entire architecture of their systems, making them easier to manage. The addition of AIOps to observability streamlines the processing of this raw data with the addition of automation and gives IT and DevOps teams a high-level view of their systems. These actionable insights can then aid in developing and delivering better, faster architectures.
5. AIOps and Hyperautomation
Cited as one of the leading technology trends of 2021 by Gartner, hyperautomation blends robotic process automation(RPA), AI, machine learning, business process management (BPM), and advanced analytics to automate enter business processes. As large and small businesses accelerate their digital transformation initiatives, hyperautomation becomes a means of aligning human intelligence with AI — a cyclical partnership that sees the automation of tasks fed by data that subsequently aids humans with meaningful decision making and productivity.
The global hyperautomation market is anticipated to reach $22.84 billion USD by 2027. As businesses across all industries look to replace legacy systems with more complex, automated infrastructures, hyperautomation allows organizations to assign their employees to more meaningful tasks while optimizing operations and saving costs.
Read more: RPA to Hyperautomation: Moving the Needle on Business Digital Transformation
AIOps Vendor Shake Out
AIOps is set to gain traction in 2021, making it critical to identify how your IT team will use your AiOps platform. With a market chock full of vendors claiming to offer comprehensive AIOps solutions it is imperative to map out and define your company’s exact AIOps needs before choosing a solution scaled to meet your operational demands.
Also read: Using Responsible AI to Push Digital Transformation