AIOps platforms will overtake observability platforms in terms of importance among global enterprises’ IT operations teams in the next six to 12 months.

While the prospect of operationalising AI has proven to be complex in recent years, generative AI (GenAI) has quickly rectified the situation with new use cases such as code generation.

New comprehensive app platforms will gain significant importance, designed to be used in scaled-out and computationally complex Kubernetes environments. Advanced analytics of data will address IT operations issues, including performance monitoring, troubleshooting unknown issues, capacity planning, and cost optimization.

In short order, users of automation platforms such as the popular Red Hat Ansible tool will have access to GenAI capabilities for use of natural language inputs to quickly create workflow scripts. GenAI-injected security solutions will include the ability to access prompt engineering through security policy management integrations.

AIOps with GenAI capabilities

As a result, a new breed of DevOps platform in the form of AIOps-versioned observability platforms are receiving GenAI capabilities. This alignment between key monitoring, visibility, and process automation will play out in coming months in the form of newly consolidated application platforms, easing the heightened demands associated with app modernization among global enterprises.

Initial comprehensive AIOps solutions will focus on providing developers and IT ops members with code generation capabilities, providing automation and observability platforms with scripts for incident resolution and process automation to bypass cumbersome hand coding. Some initial offerings include IBM IT Automation Solutions, Oracle Cloud Observability and Management Platform, Cisco Full-Stack Observability, and VMware Tanzu Insights. GlobalData provides a comparison of these offerings.

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By GlobalData

GenAI brings the promise of improving operational provisioning required under new application architectures whose IT solutions have not always been optimised to support the agility necessary for frictionless app deployment and operational efficiencies. Using plain English (natural language inputs), members of DevOps teams are starting to leverage new GenAI tools to create automation workflow scripts, which enhance current automation and monitoring solutions by bypassing complex coding requirements. In this way, a broader pool of IT ops team members can participate in the app modernization process.