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.

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.