BMC Software announced a new generative AI assistant at its Connect 2024, said to make it easier to add AI capabilities inside mainframe environments.

The AI assistant is part of the BMC Automated Mainframe Intelligence (AMI) framework, used in automating mundane tasks. BMC AMI Assistant helps take advantage of tools such as BMC AMI DevX Code Insights, that explains how a piece of mainframe code is structured and works.

In addition to this, BMC AMI Assistant will be integrated with BMC AMI Ops Insights. Here, the beta tool utilizes LLMs to enhance and optimize mainframe applications.

These systems become more accessible for the new generation of IT specialists as the number of AI tools grows," Priya Doty, vice president of Industry Solutions Marketing at BMC, added, saying the assistant will preserve crucial knowledge about mainframe systems, especially as professionals retire.

According to Chief Technology Advisor for The Futurum Group Steven Dickens, many mainframe applications are those versions built on old, poorly documented code. Updating and modernizing such essential systems becomes much easier with the introduction of AI.

One of the AI-powered tools BMC AMI DevX Code Insights is part of many unified consoles that BMC aims to provide.  In fact, these AI models can either be developed in-house by BMC or on top of third-party platforms; additionally, they may include the organization's own LLMs if needed.

Earlier this year, BMC invited organizations to join its Design Program, with early access to new generative AI features as they're rolled out. Ultimately, managing one's mainframe will be done much like any other distributed computing platform, eliminating much of the need for specialty skills.

This shift in AI usage will therefore change the IT team structures and roles to allow the creation and deployment of applications across mainframes and other platforms, as cross-functional teams of developers will be able to build and deploy applications.

Meanwhile, mainframe usage is up-and-coming as more Java and Python applications are landing on these platforms. Python, in particular, will likely serve as the catalyst for new workloads of high-performance real-time AI analysis of transactions that their mainframes deal with to preserve their unparalleled performance at scale. Increasingly diverse workloads' management is challenging going forward, even decades after being hailed as also-ran relics destined for obscurity since enterprises remain critically involved with the mainframes.

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