The Mainframe DBA: From Custodian to Strategist in the Age of Intelligent Automation
New models and tools mean database administrators can now focus less on routine tasks, and more on higher-level functions, Craig Mullins writes
The IBM Z platform continues to anchor the world’s mission-critical workloads, processing transactions at volumes and speeds unmatched by any other enterprise platform. Availability, stability and rock-solid reliability remain the top concerns, and the mainframe continues to excel at providing all three. But while the platform endures, the role of the database administrator (DBA), particularly in the mainframe world, is standing at a critical inflection point.
A Question of How, Not If
The role is not diminishing or disappearing, but is instead shedding its custodial skin and emerging as a strategic, value-add function. This transformation is being driven by the rapid adoption of AI and advanced intelligent automation. The question is no longer if AI will impact the DBA, but how the DBA must evolve to exploit, manage and govern it.
For decades, the mainframe DBA’s world has been filled with a relentless cycle of operational tasks: performing routine backups, handling recovery requests, refreshing test data, managing database utility processing (e.g., running REORGs and RUNSTATS), resolving batch failures and manually monitoring performance bottlenecks. Much of this work has been essential, but also repetitive. It has required precision, expertise and deep system awareness, but not always creativity or strategic insight. Automation has long promised relief, but traditional automation only went so far. It could schedule jobs, enforce thresholds and trigger alerts, but it could not learn or adapt well, even as ISVs promised that it could.
A New Model Takes Hold
That is now changing. Intelligent, AI-driven automation has begun to take root across the mainframe ecosystem. Mainframe sites are adopting machine learning models to assist with query optimization, performance tuning and systems management. These tools can detect anomalies before they escalate, predict capacity shortages days or weeks in advance and, in some cases, automatically remediate common performance issues before a critical outage occurs. Instead of merely notifying a DBA that a threshold has been breached, AI-assisted tooling can generate actionable recommendations, or even perform the fix autonomously.
This new model of operation is not about replacing DBAs. It is an operational necessity in an era when workloads are growing, experienced talent is retiring and the complexity of hybrid IT environments continues to increase. Rather than diminishing the role of the mainframe professional, intelligent automation frees them from the never-ending to-do list and allows their expertise to be applied where it matters most: architecting systems, improving resilience, ensuring data integrity and providing the strategic insights required to move the business forward.
The Decision Maker Is Still Human
The real shift is from doing to overseeing. As AI and automation assume responsibility for routine tasks, the DBA’s new responsibilities focus on governance, oversight and validation. The modern DBA becomes the chief auditor of the automated environment. In other words, the DBA is the human authority who understands the context, nuances, and business rules that AI cannot infer on its own.
Only the DBA possesses the technical and business expertise to interpret AI-generated recommendations, validate whether they align with the business’s data architecture and detect potential “hallucinations” that even advanced AI systems can produce. When an automated tool recommends reorganizing a high-volume table or altering an index structure, the DBA must be the one to confirm whether the suggestion is beneficial or harmful. And if beneficial, the DBA must decide when to implement it based on the importance of the data and the production workload it could impact.
Furthermore, as AI models are increasingly trained on mainframe-resident data, the DBA plays a key role in safeguarding that data. They must ensure that any data exported for AI training purposes is protected, masked when appropriate, compliant with internal and external regulations, and free of hidden biases that could influence model behavior. This requires close collaboration with compliance officers, security teams, legal departments, business subject matter experts and data governance stewards. The DBA becomes a strategic link between operational technology and enterprise risk management.
Boundaries Blur
The modern mainframe DBA must also broaden their reach beyond the traditional boundaries of Db2 or IMS administration. Enterprise architectures are no longer siloed. Mainframe data must flow seamlessly into hybrid cloud environments, mobile applications, API ecosystems, analytics engines and real-time decisioning platforms. To support this interconnected world, DBAs must become skilled in data integration technologies, RESTful API management, DevOps pipelines and cloud-native architectural patterns. They must understand how mainframe-resident data is consumed by microservices, how to support CI/CD workflows that require database schema changes and how to ensure high performance and security across distributed and mainframe components alike.
In this sense, the DBA evolves from database mechanic to data strategist and architect. They become the expert responsible not just for keeping systems running, but for ensuring that data—which is still the organization’s most valuable asset—is accessible, trusted, well-governed and architected for future growth. Their value shifts from operational responsiveness to proactive enablement. They help shape modernization initiatives, design resilient data architectures and guide how mainframe data participates in enterprise-wide digital transformation efforts.
In conclusion, the mainframe DBA is far from an obsolete profession. It is an indispensable role that is being elevated, redefined and aligned with the strategic priorities of the modern enterprise. DBAs will not be replaced by AI but DBAs who do not use AI will be replaced by DBAs that use AI.
By embracing AI, automation, and modern integration practices, DBAs shed repetitive tasks and take on responsibilities that directly influence business outcomes. The DBA who evolves by becoming an expert in AI oversight, data governance, and hybrid-cloud data architecture will not just survive the changes ahead but thrive in them. They will secure their place as the essential guardians and strategists of the digital economy, ensuring that the systems at the heart of global business remain reliable, secure, and future-ready.