If there’s any doubt that mainframes will have a place in the AI future, many organizations running the hardware are already planning for it.
While the 60-year-old mainframe platform wasn’t created to run AI workloads, 86% of business and IT leaders surveyed by Kyndryl say they are deploying, or plan to deploy, AI tools or applications on their mainframes. Moreover, in the near term, 71% say they are already using AI-driven insights to assist with their mainframe modernization efforts.
Running AI on mainframes as a trend is still in its infancy, but the survey suggests many companies do not plan to give up their mainframes even as AI creates new computing needs, says Petra Goude, global practice leader for core enterprise and zCloud at global managed IT services company Kyndryl.
Many Kyndryl customers seem to be thinking about how to merge the mission-critical data on their mainframes with AI tools, she says. In addition to using AI with modernization efforts, almost half of those surveyed plan to use generative AI to unlock critical mainframe data and transform it into actionable insights.
“You either move the data to the [AI] model that typically runs in cloud today, or you move the models to the machine where the data runs,” she adds. “I believe you’re going to see both.”
Meanwhile, AI can also help companies modernize their mainframe strategies, whether it be assisting with moving workloads to the cloud, converting old mainframe code, or training workers in mainframe-related technologies, Goude says.
For most users, mainframe modernization means keeping some mission-critical workloads on premises while shifting other workloads to the cloud, Goude says. A huge majority of survey respondents plan to move some workloads off the mainframe, but nearly as many say they consider mainframes important to their business strategies.
Goude sees more business and IT leaders embracing a hybrid IT environment now than in past years, when many organizations were taking an all-or-nothing approach.
“The survey is cementing the fact that the IT world is hybrid,” she says. “It’s all about the right workload in the right platform. How do you make the right choice for whatever application that you have?”
AI assisting with code
The Kyndryl survey rings true to Lisa Dyer, senior vice president of product at Ensono, an MSP that works with mainframes. Dyer sees significant customer interest in using AI to help with mainframe modernization efforts.
Ensono itself uses AI to help customers with modernization, she says. AI can be especially helpful for translating or updating code on customer mainframes, she says. AI can, for example, write snippets of new code or translate old COBOL to modern programming languages such as Java.
“AI can be assistive technology,” Dyer says. “I see it in terms of helping to optimize the code, modernize the code, renovate the code, and assist developers in maintaining that code.”
It makes sense for mainframe users to turn to AI to help modernize the platform, adds Chris Dukich, CEO of digital marketing technology company Display Now, who has worked with companies turning to AI to navigate the complexities of mainframe modernization.
“Many institutions are willing to resort to artificial intelligence to help improve outdated systems, particularly mainframes,” he says. “AI reduces the burden on several work phases, such as code rewriting or replacing databases, which streamlines the whole upgrading stage.”
Moving AI to the mainframe
Like Kyndryl’s Goude, both Dyer and Dukich have seen early efforts to run AI workloads on mainframes. This year, dozens of companies appear to be in the pilot or proof-of-concept phase, Dyer says, with more momentum coming with the next generation of mainframes.
Many organizations have their mission-critical data residing on mainframes, and it may make sense to run AI models where that data resides, Dyer says. In some cases, that may be a better alternative than moving mission-critical data to other hardware, which may not be as secure or resilient, she adds.
“You have both your customer data and then you have what I’ll call the operational data on the mainframe,” she says. “I can see the value of being able to develop and run your models directly right there, because you don’t have to move your data, you have very low latency, high throughput, all those things that you would want for certain types of AI applications.”
Many mainframe users with large datasets want to hang on to them, and running AI on them is the next frontier, Dukich adds.
“The relative reliability, security, and scalability of mainframes make them refractory to the competing clouds and render them very useful in analytic and decision-making work lubricated by AI,” he says.
