The rapid acceleration of digital transformation initiatives in the modern business landscape has brought emerging technologies such as artificial intelligence, machine learning, and automation to the fore. Integrating AI into IT operations or AIOps has empowered IT teams to perform complex tasks with ease and resolve agility issues in complex settings.
Gartner sees great potential in AIOps and forecasts the global AIOps market to touch
US$3127.44 million by 2025 at a CAGR of 43.7% during the period 2020 to 2025. From just US$ 510.12 million in 2019, it is expected to touch a whopping US$ 3127.44 million in 2025. It believes 50% of organizations will use AIOps with application performance monitoring to deliver business impact while providing precise and intelligent solutions to complex problems.
A global survey of CIOs iterates why AIOps is so critical for IT enterprises. The survey pointed out that despite investing in 10 different monitoring tools on average, IT teams had full observability into just 11% of the environments. Those who needed those tools didn’t have access to them. 74% of CIOs were reportedly using cloud-native technologies, including microservices, containers, and Kubernetes, and 61% said these environments changed every minute or less. In comparison, 89% reported their digital transformation had accelerated in the past 12 months despite a rather difficult 2020.
70% felt manual tasks could be automated though only 19% of repeatable IT processes were automated, and 93% believed AI assistance is critical in helping teams cope with increasing workloads.
AIOps offers IT companies the operational capability and the business value crucial for a robust digital economy. But AIOps adoption must be consistent across processes as it would fail to serve its purpose if it merely highlights another area that is a bottleneck. AIOps capabilities must therefore be such that the processes are perfectly aligned and automated to meet business objectives.
Now that we understand how crucial AIOps is let’s dive deeper to understand its scope.
Artificial intelligence, machine learning, big data have all been spoken about in great length and form the very backbone of Artificial Intelligence for IT operations or AIOps. AIOps are multi-layered technology platforms that collate data from multiple tools and devices within the IT environment to spot and resolve real-time issues while providing historical analytics.
What is AIOps?
Artificial intelligence, machine learning, big data have all been spoken about extensively and form the very backbone of AIOps. AIOps comprises multi-layered technology platforms that collate data from multiple tools and devices within the IT environment to spot and resolve real-time issues while providing historical analytics.
It is easier to understand its importance if we realize the extremely high cost of downtime. As per an IDC study, an infrastructure failure’s average hourly cost is $100,000 per hour, while the average total cost of unplanned application downtime per year is $1.25 – 2 billion.
The trends and factors driving AIOps include:
- Complex IT environments are exceeding human scale, and monitoring them manually is no longer feasible.
- IoT devices, APIs, mobile applications, and digital users have increased, generating an exponentially large amount of data that is impossible to track manually.
- Even a small, unresolved issue can impact user experience, which means infrastructure problems should be addressed immediately.
- Control and budget have shifted from IT’s core to the edge as enterprises continue to adopt cloud infrastructure and third-party services.
- Accountability for the IT ecosystem’s overall well-being still rests with core IT teams. They are expected to take on more responsibility as networks and architectures continue to get more complex.
With 45% of businesses already using AIOps for root cause analysis and potential forecasting problems, its role is evident in several use cases, as mentioned below.
Detection of anomalies and incidents – IT teams can leverage AIOps to see when anomalies, incidents, and events have been detected to follow up on them and resolve them. The thing with anomalies is that they can occur in any part of the technology stack and hence necessitates constant processing of a massive amount of IT data. AIOps leverages machine learning algorithms to detect actual triggers in almost real-time to prevent them. Full-stack visibility into applications and infrastructure helps isolate the root cause of issues, accelerate incident response, streamline operations, improve teams’ efficiency, and ensure customer service quality.
Security analysis – AI-powered algorithms can help identify data breaches and violations by analyzing various sources, including log files and network & event logs, and assess their links with external malicious IP and domain information to uncover negative behaviors inside the infrastructure. AIOps thus bridges the gap between IT operations and security operations, improving security, efficiency, and system uptime.
Resource consumption and planning – AIOps ensures that the system availability levels remain optimal by assessing the changes in usage and adapting the capacity accordingly. Through AI-powered recommendations, AIOps helps decrease workload and ensure proper resource planning. AIOps can be effectively leveraged to manage routine tasks like reconfigurations and recalibration for network and storage management. Predictive analytics can have a dynamic impact on available storage space, and capacity can be added as required based on disk utilization to prevent outages that arise due to capacity issues.
AIOps will drive the new normal.
With virtually everyone forced to work from home, data collected from different locations varies considerably. Thanks to AIOps, disparate data streams can be analyzed despite the high volumes.
AIOps has helped data centers and computer environments operate flawlessly despite the pandemic through unforeseen labor shortages. It allows data center administrators to mitigate operational skills, reduce system noise and incidents, provide actionable insights into the performance of services and related networks and infrastructure, get historical and real-time visibility into distributed system topologies, and execute guided or fully automated responses to resolve issues quickly.
As such, AIOps has been instrumental in helping enterprises improve efficiency and lighten workloads for remote workers. AIOps can reduce event volumes, predict outages in the future, and apply automation to reduce staff downtime and workload. As Travis Greene, director of strategy for IT operations with software company Micro Focus explains, “The end goal is to tie in service management aspects of what’s happening in the environment.”
AIOps and hyperautomation
A term coined by Gartner in 2019, hyperautomation is next-level automation that transcends individual process automation. IDC calls it digital process automation, while Forrester calls it digital process automation. As per Gartner ‘Hyper-automation, organizations rapidly identify and automate as many business processes as possible. It involves using a combination of technology tools, including but not limited to machine learning, packaged software, and automation tools to deliver work’.
Irrespective of what it’s called, hyperautomation combines artificial intelligence (AI) tools and next-level technologies like robotic process automation (RPA) to automate complex and repetitive tasks rapidly and efficiently to augment human capabilities. Simply put, it automates automation and creates bots to enable it. It’s a convergence of multiple interoperable technologies such as AI, RPA, Advanced Analytics, Intelligent Business Management, etc.
Hyperautomation can dramatically boost the speed of digital transformation and seems like a natural progression for AIOps. It helps build digital resilience as ‘humans plus machines’ become the norm. It allows organizations to create their digital twin (DTO) or a digital replica of their physical assets and processes. DTO provides them real-time intelligence to help them visualize and understand how different processes, functions, and KPIs interact to create value and how these interactions can be leveraged to drive business opportunities and make informed decisions.
With sensors and devices monitoring these digital twins, enterprises tend to get more data that gives an accurate picture of their health and performance. Hyperautomation helps organizations track the exact ROI while attaining digital agility and flexibility at scale.
Those on a hyperautomation drive are keen on making collaborative IT operations a reality and regard AIOps as an important area of hyperautomation for breakthrough IT operations. When AIOps meets hyperautomation, businesses can rise above human limits and move with agility towards becoming completely autonomous digital enterprises. Concludes John-David Lovelock, distinguished research vice-president at Gartner, “Optimization initiatives, such as hyper-automation, will continue, and the focus of these projects will remain on returning cash and eliminating work from processes, not just tasks.”
It’s time you adopted AIOps too and deliver better business outcomes.
Accelerate AIOps adoption with Trigent
Trigent offers a range of capabilities to enterprises with diverse needs and complexities. As the key to managing multi-cloud, multi-geo, multi-vendor heterogeneous environments, AIOps need organizations to rethink their automation strategies. Our consulting team can look into your organizational maturity to know at what stage of AIOps adoption you are and ensure that AIOps initiatives are optimized to maximize your business value and opportunity.