Why AIOps is the Future for IT Teams
As executives across industries evaluate the variety of ways that artificial intelligence (AI) can transform their businesses, one area garnering an increasing amount of attention is the evolution of IT systems to meet the demands of modern businesses, as AI migrates from the fringes to the center of the business. Sharp increases in the volumes of data generated within organizations from IT infrastructure and applications continue to put a strain on IT teams as they struggle to identify actionable insights. CIOs are looking to AI to help them not only gain better visibility into the state and performance of their IT systems, but to also to transform their service models from reactive to proactive and ultimately predictive.
IDC predicts that through 2022, the deployment of artificial intelligence to augment, streamline, and accelerate IT operations will be a key IT transformation initiative for 60% of enterprise IT organizations. This evolution of IT operations and AI integration is now being called AIOps.
Defining AIOps and Its Benefits
AIOps is the natural evolution of IT operations to suit evolving business needs in the AI space. Short for artificial intelligence for IT operations, AIOps platforms combine data analysis with machine learning to make better sense out of internal IT management systems and to automate IT tasks. Using existing data sources, including application performance monitoring, log events, and more, AIOps platforms provide insights and identify critical IT issues more quickly and efficiently, reducing the need for human input and allowing IT teams to focus on the most vital tasks.
AIOps helps IT teams – particularly those in complex, high-growth environments – become increasingly agile and responsive to the needs of their organizations. Because AIOps prompts faster resolution to problems, organizations benefit from reduced costs spent on performance-related issues. AIOps takes a proactive approach, driving faster and more informed decision-making.
Why is AIOps on the Rise?
There are several factors motivating the insurgence of AIOps as an essential tool for organizations developing AI. Here are three key reasons for AIOps’ increasing popularity:
Exponential Growth of Data
AI requires enormous amounts of data to work well and also generates a ton of data. The influx of data on IT processes is becoming too burdensome for humans alone to manage, as it creates increasingly more service alerts and high ticket volumes. AIOps adds a level of automation that reduces the need for human effort in managing performance issues.
As technology advances, customers demand speedier resolutions to their issues. Traditional IT models that rely on manual processes can’t keep up with the pace of these needs. With AIOps, machine learning tools immediately triage issues for faster resolution times.
Complexity of AI Environment
The traditional IT approach of human oversight for monitoring operations is no longer functioning well in the digital transformation world of AI. AI algorithms can help parse through complexity that would be impossible or too time-consuming for humans to interpret. These algorithms proactively solve problems before human intervention is required.
How AIOps Works
Both big data and machine learning are crucial components of AIOps. A typical AIOps process may work like this:
- Big Data: AIOps aggregates siloed data into a big data platform. This data can be sourced from performance monitoring, log events, networks, tickets, and several other places.
- Machine Learning: AIOps leverages machine learning tools for any one of the following endeavors
- Anomaly or threat detection: machine learning detects patterns that can impact network availability
- Event correlation: inference models are used to evaluate alerts, group them, and identify root cause issues to weed out the noise and provide IT teams with the most critical alerts
- Capacity optimization: Improve application uptime with AI-based analytics
- Centralize incident management: AIOps can improve the way that companies manage IT incidents across multiple locations around the world, including automated notifications when a problem is found
Companies currently adopting AIOps platforms are positioning themselves for greater long-term success by optimizing their IT infrastructures for their employees as well as for their customers. They can gain better visibility over their entire IT environment, improve the efficiency of their IT teams, and improve their bottom line.
How to Get Started with AIOps
Now that you are familiar with AIOps, you can get started in implementing AIOps tools within your IT teams. Several key steps to get started with AIOps include:
- Pilot with an initial test case that is small so that you learn quickly and iterate for success
- Work to gain leadership and colleague buy-in by making AIOps approachable and explainable, while also identifying skills and experience gaps so a clear plan can be presented and implemented
- Because there are many AIOps platforms and tools, be prepared to experiment and research which tools will work best for you – whether it’s a more substantial and robust platform that comes with a similar cost, or it is an open-source, low-cost ML model to help explore test cases, there are options to experiment for most teams
- Be prepared to take AI beyond IT – data and analytics will be an output of AIOps, and if that data management is handled with grace, it can be a massive opportunity for the business to become an AI-first company and utilize the data to a competitive advantage.
- Feed your ML models that serve as the basis of your AIOps platform high-quality training data to ensure they provide the most reliable results
- Consider working with a data partner – given the importance of training data, you may want to seek out a data provider to help you collect and annotate data quickly and scalably
AIOps can help your organization tackle a myriad of challenges: how to evolve with the pace of digital transformation, how to adopt cloud, and how to support DevOps and other key teams involved in AI deployment.
The Future of AIOps
While companies are beginning to look at and invest in AIOps, it has yet to reach its full potential. This is likely short-term as IT requirements are continuing to scale, while budgets and teams are becoming more efficient. To help IT teams stay on top of these challenges, IT tooling needs to adapt, paving the way for AIOps. Organizations that are well prepared to invest in and implement AIOps will be able to continue to support company growth and innovation. By adopting an AIOps approach, companies can expect:
- Data to become critical to the company and be an opportunity for monetization
- Improved user-experience as users can self-service with ease
- DevOps to improve as agility extends to operations within the business
- Decreased costs due to increased productivity by freeing employees from more tedious tasks, allowing them to focus on more enjoyable achievements
Adopting AIOps will be both beneficial and critical to success for any AI-first organization. Organizations that embrace AIOps and other tech-forward systems will have the edge over those that fail to evolve quickly in the ever-changing field of AI.
What Appen Can Do for You
Appen has over 20 years of experience delivering thousands of AI projects. We understand the complexity of the AI environment and offer our platform, access to a global crowd, and our expertise to help you develop a data pipeline to support your AIOps system. Our platform enables you to collect, label, and prepare high-quality training data with speed and accuracy.
You can also learn more about strategies and resources to help establish a robust training data pipeline to fuel your AIOps platform in Appen’s AIOps for Business Leaders eBook.
To discover how Appen can help with your AIOps data needs, contact us today.