Market intelligence firm IDC predicts that by 2024, enterprises that are powered by AI will be able to respond to customers, competitors, regulators and partners 50% faster than those that are not using AI.[1] This is good news for frustrated CIOs and IT departments that are struggling to use the vast volumes of data from IT sources to monitor and manage incidents in real time.

Unresolved incidents can lead to costly outages, impacting client experience and revenue. AIOps enables the IT department to predict or rapidly detect issues in near real time. As workloads shift to cloud environments, AI helps cope with new complexities proliferated by cloud-native architectures. AI also helps decide what action to take, ultimately automating the remediation or resolution activity.

IBM Watson AIOps, a new product that leverages machine learning, natural language understanding, explainable AI and other technologies to automate IT operations, is now generally available. Powered by innovations from IBM Research, Watson AIOps can help businesses transition from a reactive to proactive posture. It is designed to help businesses detect issues in real time to speed incident resolution.

Early clients have already seen results from Watson AIOps. CaixaBank is a leading financial institution in Spain and Portugal, the main banking relationship for 26.7% of Spaniards and a leader in online and mobile banking in Spain. They serve more than 15.5 million customers with 5,379 branches and 9,427 ATMs and continually aim to provide a best-in-class omnichannel platform.

“Using IBM’s Watson AIOps, we’ve gotten much better at understanding some of the issues buried within our data,” says David Almendros, Artificial Intelligence Director at CaixaBank. “Being able to draw insight from within our logs and other unstructured data has helped us to progress in addressing anomalies quickly. It also has addressed a challenge our engineers have had with the task of combining and working with data and chatter across different tools. Watson AIOps brings it all together, allowing our engineers to respond faster and much more effectively.”

Watson AIOps is also being explored within IBM to speed issue detection and resolution for cloud-based SaaS applications. In a recent simulation, Watson AIOps was able to detect anomalies in real time and provide easy tracing to the root cause leveraging log data. This enabled the simulation team to have headlights into irregular activity 47 minutes before the incident occurred, an advantage over their prior process, in which root causes of detected anomalies were difficult to uncover, taking around two hours on average to understand.

Watson AIOps integrates with various best-in-class monitoring solutions to deliver holistic insights across the IT environment. It is highly customizable and uses Red Hat OpenShift to run on any cloud. With the insight and recommendations from Watson AIOps, you can improve incident resolution, drive more automation and shift your operations teams to higher-value work.

[1] IDC FutureScape: Worldwide Digital Transformation 2020 Predictions, Doc #US45569118, Oct 2019

To learn more on how you could put Watson AIOps to work for your organization, explore the Watson AIOps product page.

Categories

More from Artificial intelligence

Achieve your AI goals with an open data lakehouse approach

3 min read - Artificial intelligence (AI) is now at the forefront of how enterprises work with data to help reinvent operations, improve customer experiences, and maintain a competitive advantage. It’s no longer a nice-to-have, but an integral part of a successful data strategy. The first step for successful AI is access to trusted, governed data to fuel and scale the AI. With an open data lakehouse architecture approach, your teams can maximize value from their data to successfully adopt AI and enable better,…

IBM and ESPN use AI models built with watsonx to transform fantasy football data into insight

4 min read - If you play fantasy football, you are no stranger to data-driven decision-making. Every week during football season, an estimated 60 million Americans pore over player statistics, point projections and trade proposals, looking for those elusive insights to guide their roster decisions and lead them to victory. But numbers only tell half the story. For the past seven years, ESPN has worked closely with IBM to help tell the whole tale. And this year, ESPN Fantasy Football is using AI models…

Open source large language models: Benefits, risks and types

6 min read - Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content. There are two types of these generative AI models: proprietary large language models and open source large language models. https://www.youtube.com/watch?v=5sLYAQS9sWQ In this video, Martin Keen briefly explains large language models, how they relate to foundation models, how they work and how they can be used…

Unleashing the power of Presto: The Uber case study

7 min read - The magic behind Uber's data-driven success Uber, the ride-hailing giant, is a household name worldwide. We all recognize it as the platform that connects riders with drivers for hassle-free transportation. But what most people don't realize is that behind the scenes, Uber is not just a transportation service; it's a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. This blog takes you on a journey into…