Using AI and generative AI on the mainframe

An early step for companies (and governments) is to get their data in order. Only a modern data architecture (the digital structures for the collection, transformation, distribution and consumption of data) can enable AI and generative AI to provide the accurate, unbiased and explainable insights users depend on.

Using tools such as Kyndryl Bridge, an AI-powered open integration platform, can help automate and optimize mainframe operations. This enables organizations to decrease manual interventions, process time and software costs. The AI-driven operational insights can enable more proactive and predictive management of mainframe systems, and provide visibility and control over mainframe performance and costs.

In addition, organizations can optimize services delivery and hardware and software costs by implementing and deploying AI-enabled chatbots and other operational processes to help execute day-to-day operations and recommend technology best practices. And running AI models on the mainframe can provide insights that can help companies enhance customer satisfaction and compliance with regulatory requirements and potentially reduce fraud losses.

Developers also can deploy generative AI tools to help write code documentation, increase productivity, and modernize or convert classic mainframe code to languages such as Java and C#. This can help enable faster and more agile development cycles, easier integration with hybrid cloud applications and more effective management of mainframe applications.

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Kyndryl Holdings Inc. published this content on 16 May 2024 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 16 May 2024 12:59:08 UTC.