BMC’s annual mainframe survey shows the changing role of AI in mainframe environments.
When it comes to AI and the mainframe, customers are moving from experimental to operational approaches and feeling comfortable enough to use AI to provide system insights and recommend actions, a new study finds.
Mainframe professionals are increasingly adopting generative AI tools to recommend actions, according to the 2026 BMC Mainframe Survey, and implementing AI technologies remains a top priority for 45% of respondents. BMC surveyed more than 1,300 mainframe practitioners and decision makers globally for the report.
“We are seeing a significant shift as organizations have gone from asking how they can use AI to asking where they can trust it, how it can be governed, and where it delivers measurable value. It’s becoming clear that the path to greater AI autonomy on the mainframe will be earned through trust, with the option of humans remaining in the loop to oversee and implement AI recommendations,” said John McKenny, senior vice president and general manager of Intelligent Z Optimization and Transformation at BMC, in a statement.
After much discussion, planning and investment, the mainframe community is moving from “AI enthusiasm to pragmatic adoption,” according to the report:
“AI has moved from experimentation to strategic planning, with mainframe organizations seeming to take a more pragmatic approach,” the report states. “That pragmatism is visible in the kinds of responsibilities organizations are currently willing to give AI. After the initial excitement and hype concerning AI capabilities, mainframe executives seem to be adopting a cautious realism, turning to the technology to act as an advisor, but not an executor. In short, AI hasn’t gained the full trust of the mainframe world, prompting a ‘human-in-the-loop’ approach.
AI is becoming an increasingly important tool in mainframe transformation, and as expectations mature, “AI use has shifted from experimental to operational; organizations have gone from asking how they can use AI to asking where they can trust it, how it can be governed, and where it delivers measurable value,” the report states.
When it comes to applying AI on the mainframe, the top four concerns when implementing AI-driven solutions are: high implementation costs (cited by 41% of respondents); security and privacy (39%); data integration issues (37%); and regulatory/compliance concerns (22%).
Digital certificate management is one area where many organizations still have an opportunity to improve, the study notes: “As the integration of AI-based tools and use of AI agents increases, so will the need for more, and more frequently renewed, digital licenses, likely reaching a volume to which manual management efforts cannot scale and a complexity that exposes the limitations of home-grown automation solutions.”
“At the same time, connections with AI-based applications will continue to increase as the technology is further integrated with the mainframe,” the report states. “While ensuring secured connections for both humans and applications is a critical piece of the mainframe security puzzle, the 2026 BMC Mainframe Survey shows that the majority of respondents are using either in-house automated solutions to manage their organization’s digital certificates or are managing certificates manually.”
Some other interesting findings in the study include:
- Ensuring data security: Organizations place a high priority on securely and effectively connecting AI to their data, with “data integration issues” ranking as the third-highest concern when implementing AI solutions and “modernizing data management” ranking as the third most important AIOps capability. “A high priority is also placed on protecting that data. Data recovery was the only priority that saw a significant change from last year’s survey, listed by 35% of respondents (up 4 percent),” the report states.
- Agentic AI gaining momentum: “Leaders indicate that over the next two years they plan to target their AI investments toward AIOps, application development, and knowledge transfer. But agentic mainframe management is also a top investment strategy, with 40 percent of Leaders indicating that they plan to invest in creating agents to manage the mainframe and 36 percent planning to invest in third-party agents for that purpose.”
- Time to value for AIOps: “Of those using AIOps on the mainframe, 68% of respondents—and 74% of users—report seeing time to value within one year,” the report states. “Finding causes and determining how to fix issues remain the top challenges in mainframe operations. The use of AI, especially GenAI, addresses these challenges directly. It is little wonder, then, that 58 percent of Leaders who prioritize AI technologies name the implementation of GenAI solutions as the most important AIOps capability.”
- Using GenAI-assisted tools: “While rules-based logic combined with AI and ML have improved problem detection and even enabled proactive remedies before issues affect service, operations teams are still left to determine root causes and determine fixes. The implementation of GenAI-assisted tools provides operators with the advantage of contextual advice on what actions to take next. These tools ingest past issue resolutions, documentation, and other organizational knowledge to suggest next steps in natural language, acting as a trusted advisor who draws on years of experience to provide clear guidance, regardless of the operator’s experience or skill level.”
- AI for knowledge transfer: “AI isn’t replacing mentoring or experience—it’s making both more effective. Alongside mentorship programs and automation, AI gives less experienced mainframers confidence in their actions and knowledge of past solutions to build upon. Forty percent of respondents are using AI for documentation and knowledge transfer; of those who prioritize staffing and skills and are hiring and training staff to address their needs, 49% are choosing to leverage AI assistants.”
