Survey Surfaces Increased But Limited Reliance on AI to Manage Mainframes

A BMC Software survey of 1,319 IT professionals finds that 94% view the mainframe as a long-term platform and plan to continue investing, but only 18% say growth is driven by new applications. While 40% plan to invest in AI agents for mainframe management, fewer than a quarter are comfortable letting AI complete tasks autonomously, with top AI use cases including performance tuning (37%) and problem detection (36%). Implementing AI ranks second among priorities at 49%, behind compliance and security at 61%.

TL;DR — Key Takeaways - Mainframes remain strategically important: 94% of respondents view the mainframe as a long-term platform or destination for new workloads, and the same percentage plan to continue investing in it. - AI interest is high, but trust remains limited: Organizations are exploring AI for performance tuning, problem detection, testing, documentation and incident management, but fewer than a quarter of respondents are comfortable letting AI complete tasks autonomously. - AI adoption is a major priority: Implementing AI ranks second among respondents’ priorities for the coming year at 49%, behind compliance and security at 61%. A survey of 1,319 IT professionals who work with mainframes https://www.bmc.com/newsroom/releases/trust-powers-ai-on-the-mainframe.html finds that while mainframes remain a strategic platform, the level of criticality also limits the degree to which respondents are willing to rely on artificial intelligence AI , for now at least, to manage them. Conducted by BMC Software, the survey finds 94% of respondents view the mainframe as a long-term platform or a destination for new workloads, with an equal percentage planning to continue to invest. Overall, 69% said capacity is growing on the platform, but only 18% said that growth is being driven by new applications. When it comes to AI, there is a significant amount of interest. A total of 40% of respondents are planning to invest in AI agents to help manage AI operations, with 36% planning to rely on third-party AI agents. Top use cases for AI include performance tuning 37% , problem detection 36% , automated testing 35% , documentation 33% , root cause analysis 32% , code explanation 32% and incident resolution 32% . Top AI areas of investment over the next 12 months are developer assistance 43% , tools for preserving and transferring knowledge 41% , analytics for performance tuning 36% , creating agents to manage the mainframe 36% , AIOps for incident management 33% and using agents from others to manage the mainframe 32% , the survey finds. Survey respondents, however, also have concerns, including high implementation costs 41% , security and privacy 39% , data integration issues 37% and regulatory/compliance challenges 22% . Nevertheless, implementing AI ranks second 49% on the list of top priorities for the coming year, well after compliance/security 61% and slightly ahead of cost optimization 46% . In fact, 68% of respondents expect to see value from AIOps on the mainframe either within less than six months 26% or a year 42% . The degree to which AI is trusted is still relatively nascent. The majority of respondents today rely on AI to create alerts or generate recommendations but less than a quarter are comfortable enough to allow AI of any kind to complete a task on its own. Matt Whitbourne, vice president of product management and design for the BMC Automated Mainframe Intelligence AMI portfolio, said that while the perceived risks associated with AI agents are high, other classes of AI tools are already helping to optimize mainframe environments in a way that also serves to reduce the level of expertise that might otherwise be required. As AIOps continues to mature, it’s also expected that many of the roles within the IT teams that manage mainframes will also continue to evolve, he added. In fact, AI should help more of the IT teams that manage mainframes to adopt best DevOps practices as mainframes are more deeply integrated with distributed computing environments, he noted. The mainframe as a platform will always be unique, but the level of expertise required to manage it continues to decline, added Whitbourne. At this juncture, it’s not so much a question of whether IT teams that manage mainframes will adopt AI so much as how soon and to what degree. Given the mission-critical nature of the workloads that run on mainframes, the appetite for experimenting with any unproven technology is, as always, comparatively limited.