AI is reshaping the mainframe from a static workhorse into a dynamic innovation partner. Today’s leading organizations are moving beyond basic automation, using AI to interpret data, explain code, and provide actionable guidance tailored to their unique environments. In the second of a three-part conversation with Anthony DiStauro, Senior Director of Architecture for AI at BMC Software, I explored how organizations are beginning to infuse intelligence into their mainframe environments. What emerged from the discussion was a set of practical examples on how AI can help interpret complex systems, preserve expertise, and support modernization efforts.
AI: Your Partner in Progress
In the first conversation of this series, we explored the idea of AI acting as an advisor, helping teams support, but not replace, human decision-making.
Practical applications of generative AI are already making a significant impact. DiStauro pointed to BMC’s AMI Assistant as one example of how generative AI is already being applied in the mainframe environment. This AI tool can interpret complex COBOL code and explain it in plain language. AI is also being used at BMC to identify the root causes of environmental issues, providing teams with clear, actionable steps for resolution. DiStauro states that customer feedback on these capabilities is overwhelmingly positive, as customers see tangible improvements in efficiency and problem-solving.
The Real Value: Knowledge-Infused AI
The true power of AI extends beyond simple automation; it lies in the ability to capture, preserve, and infuse institutional knowledge directly into operational workflows. As experienced team members retire, organizations face the critical challenge of losing decades of specialized expertise. AI guards against this knowledge drain by creating a living repository of insights. By capturing patterns from experienced practitioners, AI systems can surface recommendations and next steps aligned with an organization’s processes and terminology. This transforms AI from a generic tool into a deeply integrated partner that understands an organization’s specific environment and helps maintain continuity.
How Hybrid AI Powers Practical Solutions
Another topic we discussed was the role of hybrid AI approaches that combine multiple techniques to address complex operational challenges. Instead of relying on a single model, organizations can tap into the strengths of different approaches—combining rules-based AI, machine learning, and large language models—to address complex challenges with precision and flexibility.
DiStauro also described how BMC applies this hybrid approach in its AMI Ops Insight product, which utilizes advanced machine learning models to monitor the mainframe environment, effectively detecting anomalies and predicting potential disruptions before they occur. It then pairs this predictive power with generative AI to offer clear explainability and precise recommendations. While machine learning identifies the root of an issue, the generative component translates that technical data into plain language and suggests the next best steps. This helps ensure that teams don’t just see the data—they understand it and know precisely how to act.
Starting with Explainability
Drawing on the examples we discussed, one practical starting point for organizations exploring AI in the mainframe environment is explainability. Tools that clearly explain AI decisions and alerts can help teams build trust and move past early uncertainty. With that foundation, organizations can progress from simply flagging issues to resolving them. By embedding organizational knowledge into the system, AI is better positioned to deliver recommendations that fit your operations. This stepwise approach sets you up for practical, sustainable results, not just innovation for its own sake.
Infusing Intelligence for AI Results
Infusing intelligence into mainframe operations ultimately means combining AI capabilities with the operational knowledge organizations have built over decades. This combination delivers context-aware insights, automates complex tasks, and helps teams adapt quickly to new challenges. By embedding institutional expertise into AI systems, you not only streamline operations but also pave the way for ongoing innovation. Starting with explainability, making AI decisions transparent, creates a foundation organizations can build on over time. This approach can ensure the mainframe environment isn’t just running smarter; it’s evolving alongside your business.
To learn more, watch the interview on Techstrong TV.
