Generative AI (GenAI) adoption is happening, and it’s happening quickly and seemingly everywhere. GenAI, large language models (LLMs), and other AI models have burst on to the marketplace and smart companies are finding opportunities across their operations to build big potential business value. There will be a time where we won’t be able to live without it and will always be looking for the next great AI assistant. The technological progress we’ve seen in the past two years would resemble a decade’s worth of progress a short while ago. Remember when everything was “mobile-first”? Now it’s all “AI-first.”
This has put new challenges on IT leadership. As AI apps are being launched into customer- and employee-facing uses, IT is tasked not only with finding the coolest LLMs and models to deploy, but also with rethinking their entire IT architecture: from infrastructure to model deployment to data access and security, governance and more.
Reimagining IT Architecture for AI Success
As IT races to enable AI business apps, they also have a profound opportunity to improve their own environment and leverage AI apps to monitor and improve IT operations, quicken their application modernization journeys, build new levels of resilience and security, and add observability to the entirety of their IT estates. Called AIOps, it is the growing practice of using GenAI tools to vastly enhance the observability, performance, and management of the application and infrastructure environment using data from the environment itself.
Incidents in IT operations can be incredibly costly. A single downed application, even if only for hours, can cause loss of revenue, dangerous security risks, loss of customers, reputation damage, and productivity disasters, and it can also rack up huge remedial costs. For instance, the 2023 FAA Notice to Air Missions (NOTAM) system mishap likely cost the organization and the industry millions of dollars for less than a day’s length of downtime. This is one recent and well-publicized incident that could’ve been resolved much faster with better observability and visibility across applications. Any site reliability engineer (SRE) knows that lesser-known incidents happen all the time and understands the pain they cause.
Harnessing AIOps to Stay Ahead of IT Challenges
AIOps helps you be more proactive in IT operations, identify problems sooner, and more efficiently solve them faster. It helps reduce human errors and downtime. AIOps can simplify manual processes by automating them. It’s a path to being more nimble, more agile, faster, and more scalable. It is especially valuable as companies embark upon journeys of cloud adoption, app modernization, and, especially now, AI.
The first keys to successful AIOps are observability and automation. You can’t monitor what you can’t measure. If you can’t qualify, then you can’t quantify. If you are looking at a system modernization strategy, modernizing your business, or modernizing your applications, it must include AI.
The next part of our “Observe and Operate Your Generative AI Architecture” series is coming soon. Stay connected for more insights!
This article is adapted from a presentation by Manoj Khabe, Converge Vice President, Observability, AIOps and Automation, and Brian Zied, IBM Senior Automation Technical Specialist. View the presentation here.
