Organizations around the world rely on IBM Z to execute some of their most critical business processes. Financial settlement, customer transactions, regulatory reporting, payroll processing, and supply chain operations often depend on batch workloads executing successfully every day.

For decades, organizations relied upon experienced personnel to review, validate, and maintain production JCL. That approach has served the industry exceptionally well. However, the environment in which these workloads operate is changing rapidly.

Organizations are modernizing applications, accelerating change cycles, adopting DevOps practices, and increasingly embracing AI-assisted software development. At the same time, many experienced IBM Z professionals are approaching retirement, creating a growing skills challenge for enterprises worldwide.

According to the annual BMC Mainframe Survey, organizations continue to report concerns regarding skills availability even as the strategic importance of the platform continues to increase. The challenge is no longer simply maintaining systems. It is ensuring that critical business processes continue to execute reliably as organizations modernize and transform.

These trends raise an important question:

Can we confidently execute the critical business processes that run the enterprise?

Too often, conversations surrounding JCL focus exclusively on syntax checking or standards enforcement. While these capabilities remain important, they no longer fully address the challenges organizations face. The real objective is not validating JCL; the real objective is ensuring that critical business processes execute successfully.

Business leaders care about outcomes. They care that payroll runs on time, financial settlements complete successfully, customers can access services, and regulatory reports are delivered as required. A failed workload can have consequences far beyond an individual batch job. Delayed processing may impact downstream applications, disrupt customer experiences, create compliance issues, and ultimately affect revenue and organizational reputation.

The rapid adoption of AI introduces an additional consideration. AI can significantly accelerate software development activities, but acceleration without governance introduces risk. As organizations increasingly incorporate AI into development processes, an important question emerges: who validates the output produced by AI?

AI-generated JCL, like manually created JCL, must still be reviewed, validated, and governed before it enters production environments. Organizations require operational controls that ensure AI-assisted development improves outcomes rather than introducing new execution risk. This is particularly important in highly regulated industries where reliability, auditability, and operational resilience are foundational business requirements.

The National Institute of Standards and Technology’s Artificial Intelligence Risk Management Framework (NIST AI RMF) reinforces this need by emphasizing governance, measurement, and management practices designed to promote trustworthy AI. As AI becomes embedded within software engineering processes, organizations must establish validation and governance mechanisms that maintain confidence in production systems.

Workload readiness intelligence therefore becomes essential. Organizations need the ability to validate workloads before execution, identify potential issues early in the lifecycle, and establish consistent operational standards across development and production environments. Doing so not only reduces operational disruptions but also lessens dependence on scarce expertise while improving confidence in modernization initiatives and AI adoption.

Ultimately, operational resilience begins long before recovery. It begins with confidence that the critical business processes supporting the enterprise are ready to execute successfully when the business depends upon them. That shift in perspective moves the conversation beyond JCL validation and toward what truly matters: business process success.

References

1. BMC, State of the Mainframe in 2025 and 20th Annual BMC Mainframe Survey. Key findings include continued modernization investment, increasing use of AI and automation, and ongoing focus on workforce transformation and skills development.
2. National Institute of Standards and Technology (NIST), Artificial Intelligence Risk Management Framework (AI RMF 1.0). The framework provides guidance for establishing trustworthy AI through governance, risk management, measurement, and ongoing operational controls.
3. Gartner, Hot Topics Across the C-Suite (May 2026). Research highlights cybersecurity and resilience as board-level business priorities and emphasizes business outcomes as the benchmark for technology leadership.
4. Gartner, CIO Report 1H26. Research identifies resilience, AI governance, and operational effectiveness as strategic priorities for technology leaders.
5. NIST, Cybersecurity Framework 2.0. Reinforces governance and organizational resilience as foundational capabilities for managing operational and cyber risk.