Mainframe

How AI will transform the mainframe

Article May 16, 2024 Read time: 1 min

By Petra Goude, Global Practice Leader for Core Enterprise & zCloud at Kyndryl

Enterprise computing began with the mainframe, which has reinvented itself over time to remain an asset for complex and mission-critical workloads. As mainframe customers embrace the hybrid cloud era, they must modernize to allow integration and deployment of data and applications across multiple platforms.

That’s why companies across industries are seeking to manage their technology and skills deficits. It is important that their IT estate runs the right workloads on the right platforms and that their workforce can handle the next generation of computing challenges.

The convergence of AI, mainframe and cloud is shaping the evolution of IT. The world’s biggest financial institutions, manufacturers and healthcare providers have relied on the mainframe and classic programming languages like COBOL, PL/I and REXX since the beginning of enterprise computing. The mainframe continues to serve these companies well. But the very nature of the mainframe — what enables its reliability, resilience and security capabilities — contributes to difficulties with systems modernization. This is where AI and generative AI can help.

Artificial intelligence and generative AI can help accelerate mainframe modernization efforts. However, utilizing responsible AI and generative AI in mainframe environments requires a thorough understanding of the technology, in addition to cloud and mainframe domain expertise. Embedding various types of AI into mainframe and hybrid cloud environments can help augment human capabilities, streamline automation of business processes and generate actionable insights from data.

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.

AI-supported application development

Deploying AI and generative AI on the mainframe can help drive the modernization of IT estates. In addition, a DevSecOps framework can enable organizations to integrate software development, security and operations across mainframe, cloud and distributed environments to accelerate modernization. The framework helps protect a system’s availability and operational integrity while automating the applications development process. DevSecOps, combined with Kyndryl Bridge, can help to monitor and optimize the performance and reliability of mainframe applications, and speed innovation time-to-market to address new business opportunities.

LLMOps (Large Language Model Operations) complement DevSecOps by providing tools and protocols for managing Large Language Models in production environments. LLMOps can automate such tasks as building and managing prompts and expanding and monitoring AI models. Together, DevSecOps and LLMOps can help data scientists build new AI models for mainframe and hybrid cloud environments — including seamlessly integrating modern and legacy tools to help secure business-critical workloads.

A global community of experts supports the mainframe. But mainframe modernization including the infusion of AI and generative AI can help protect against current and future skills shortages by reducing transformation efforts and costs, and enhancing agility and productivity. That way, businesses and governments can continue to improve their operations while providing services their customers rely upon.

As technology rapidly evolves, we will continue to utilize the full power of AI to help businesses transform. It’s an exciting time.