A survey of 800 executive and technical professionals conducted by BMC found close to two-thirds (62%) are employing DevOps workflows to build and deploy applications.

In addition, the survey also finds that just under a third of respondents (32%) are applying artificial intelligence to IT operations (AIOps) across all platforms.

John McKenny, senior vice president and general manager of intelligent Z optimization and transformation at BMC, said the survey shows that organizations continue to invest in mainframe applications alongside distributed computing environments.

In fact, 61% of respondents noted that data volume on mainframes increased in the last year, with 56% reporting an increase in the number of databases running. Well over a third of respondents (35%) also reported increased interest in taking advantage of cloud technologies to handle, for example, storage and data protection.

Overall, the survey found that 94% of respondents have a positive perception of the mainframe, with nearly two-thirds (63%) reporting increased mainframe investments over the past year.

Compliance and security lead the list of priorities for mainframe organizations, with 61% of respondents citing these as top issues, followed by cost optimization (49%), enhancing automation (41%) and data recovery (40%).

In general, organizations with mainframes are adopting modern application development tools such as VS Code to attract a younger generation of developers. It’s critical to continue to attract developers to mainframe platforms that continue to run the most mission-critical applications in the enterprise, said McKenny. Many of those applications are being modernized using DevOps tools and practices rather than being moved to another platform, he added.

He added that the appetite for using generative artificial intelligence (AI) to write code for mainframe applications is less clear because of intellectual property concerns. Most organizations are not going to want to expose their mainframe code to a general-purpose large language model (LLM) that drives a public generative AI platform, but there will be a role for private LLMs that are trained using code that has been validated, noted McKenny.

Regardless of how generative AI evolves, there’s still much work to be done to further the adoption of DevOps best practices in mainframe environments. Each organization will determine how stringently to apply those practices based on the frequency at which applications running on mainframes need to be updated. In most cases, organizations with mainframes will use a mix of waterfall and DevOps-based approaches to building and deploying applications for many years. The decision as to which approach to employ will, naturally, have as much to do with culture as it does the rate at which the application needs to be built and deployed to drive, for example, a digital business transformation initiative.

The days when mainframes were managed in isolation from the rest of a distributed computing environment are slowly coming to an end. A mainframe is, arguably, just another node for deploying applications. The challenge and the opportunity now is getting the right DevOps tools in the hands of the IT team managing heterogeneous IT environments, including mainframes.