What is AIOps, and Why is it 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.
What is AIOps?
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 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.
Some of the current use cases for AIOps in practice today include:
- 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.
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
Tips for Launching AIOps Initiatives
Now that you are familiar with AIOps, you can get started in implementing tooling 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.
The Importance of Training Data to AIOps Success
As with any AI initiative, one of the keys to success is ensuring that the machine learning models used to power the AI applications are trained with enough high-quality data so that they provide the most reliable results. You can learn more about strategies and resources to help establish a robust training data pipeline to fuel your AIOps platform in our AIOps for Business Leaders eBook.