Delivering Business Outcomes On Time, Every Time
Workload automation is a business-critical discipline which ensures outcomes are delivered on time across diverse and complex technology environments. It has evolved far beyond its origins as a back-office job scheduler into industries where a missed processing window results directly into revenue loss or regulatory penalties. Workload automation enables the ability to orchestrate, predict and remediate issues across mainframe, distributed and multi-cloud infrastructures.
Industries Where Workload Automation is Mission-Critical
Workload automation finds its most critical applications across industries where business outcomes must be delivered with absolute reliability. Banking, financial services, insurance, travel, and hospitality sectors all share a common imperative: execute complex sequences of interdependent tasks across multiple systems without fail.
The challenge these organizations face is significant. Business-critical processes like financial close, order fulfillment, and regulatory reporting must flow through a variety of backend systems—infrastructure layers, applications, and data platforms—all working in concert. Modern workload automation delivers these outcomes by coordinating workflows that span mainframes, distributed systems, cloud platforms, and often multiple cloud providers simultaneously.
Consider a financial close process at a major bank or retailer. A series of steps must be completed across a wide variety of systems, from legacy mainframes to modern cloud applications. Each step depends on the successful completion of prior tasks, and any failure or delay must be quickly identified and resolved. This is where workload automation shifts from a technical necessity to a business imperative.
Beyond Task Execution: Business Impact Correlation
What distinguishes modern workload automation from traditional job scheduling is its focus on business outcomes rather than merely technical task completion. When a workflow encounters a delay or failure, the system must do more than send a generic alert—it must notify the right people and clearly identify the business impact of that failure.
This capability—correlating technical problems to business consequences—enables operations teams to prioritize their response appropriately. Instead of treating all failures equally, teams can focus immediately on issues that threaten revenue, customer commitments, or compliance deadlines.
Workload automation as a practice now helps organizations achieve the consistent delivery of business outcomes on time, every time, across an increasingly diverse technology stack.
Real-World Impact: How Predictive Automation Prevented a Major Disruption
A compelling example comes from Hershey’s, the global confectionery and snacks company. Beyond their iconic chocolate brand, Hershey’s operates a complex supply chain fulfilling product orders to major retailers including Walmart and Target across the United States and internationally.
The company relies on SAP as a core platform, surrounded by numerous integrated applications and data flows. In one incident, an SAP instance encountered problems processing certain jobs. Using Control-M from BMC for workload automation, the system recognized the downstream dependencies and predicted that if the problem wasn’t resolved within a specific timeframe, the business impact would be significant: product orders to major retailers would not be fulfilled.
Because the workload automation platform correlated the technical issue to a clear business impact, the operations team’s reaction was immediate and appropriately urgent. They understood exactly what was at stake and could escalate accordingly.
Two critical capabilities made this outcome possible. First, the automation of complex, cross-platform workflows with full understanding of upstream and downstream dependencies. Second, business impact correlation that reframed a technical fault as an imminent revenue and SLA risk, prompting faster and more decisive action. The alternative—waiting seven or eight hours only to discover that major orders went unfulfilled—would have resulted in significant financial impact and damaged customer relationships.
AI and Machine Learning: Transforming Every Phase of Workload Automation
The most significant evolution in workload automation since 2020 has been the infusion of artificial intelligence and machine learning capabilities throughout the entire automation lifecycle. What were once rule-based and policy-driven processes are rapidly becoming AI-driven.
Consider a typical failure scenario. Traditionally, when something fails, a notification goes out and a human must analyze the alert, review logs, identify that there was a network problem in a specific segment, and determine the necessary fixes. With AI-enhanced workload automation, intelligent agents can detect the failure, analyze the logs automatically, understand the root cause (such as a network problem in a particular segment), and even recommend remediation steps—all before a human needs to intervene deeply.
When human involvement is required, the person no longer needs to perform the time-consuming analysis. The AI has already summarized the problem and provided recommended solutions. In more advanced implementations, where business rules and regulations permit, AI agents can even execute remedial actions automatically, always with appropriate governance and human-in-the-loop approvals for critical changes.
The MTTI and MTTR Revolution
This AI-driven approach fundamentally changes two critical metrics: Mean Time to Identify (MTTI) and Mean Time to Restore (MTTR). Since MTTR depends heavily on MTTI, reducing the time to identify and diagnose problems has a multiplicative effect on overall restoration time.
AI dramatically lowers MTTI by not only detecting problems but understanding their causes and recommending solutions. In high-stakes environments where minutes of diagnosis time can cost hours of missed processing windows, this compression of identification and remediation timelines represents a transformation in operational capability.
Managing Unprecedented Technology Stack Complexity
The underlying complexity of enterprise technology stacks continues to accelerate. Organizations in banking, financial services, healthcare, and other established industries face a unique challenge: their longevity means their technology estates are highly diverse.
Modern AI, cloud platforms, and machine learning systems are being rapidly adopted while business-critical systems continue to run on proven platforms like mainframes. This creates a dual reality where organizations must automate end-to-end business processes that span this very diverse technology stack—from decades-old systems to bleeding-edge cloud services.
Companies are adopting new technologies at a rapid pace but aren’t necessarily retiring what has served them well for years. This creates a compounding complexity challenge. Modern workload automation must bridge these worlds, orchestrating workflows that touch legacy infrastructure, modern distributed systems, and multiple cloud platforms—often within a single business process.
Automation That Knows What’s at Stake
Organizations looking to advance their workload automation capabilities should focus on business-critical processes with strict SLAs—financial close, order-to-cash cycles, regulatory reporting, or nightly batch processes that must complete within tight windows. Mapping dependencies across the entire technology stack, from mainframe to cloud, and identifying the business impact of failure at each step provides the foundation for effective automation.
Integration with observability platforms, logging systems, and IT service management tools ensures that alerts carry context and business impact information. As AI capabilities are introduced, establishing clear guardrails around where AI can diagnose, recommend, and potentially auto-remediate becomes essential, with appropriate approvals required for sensitive operations.
Success should be measured not just in technical metrics but in business outcomes: SLA adherence, reduction in MTTI and MTTR, percentage of incidents auto-remediated, and ultimately, the consistent on-time delivery of business outcomes like order fulfillment and financial close completion.
Solutions: Control-M for Modern Workload Automation
BMC’s Control-M provides comprehensive workload automation capabilities designed for the complexity of modern hybrid and multi-cloud environments. Control-M enables organizations to define, schedule, manage, and monitor workflows across diverse technology stacks with advanced operational capabilities including end-to-end workflow connectivity, predictive analytics, SLA management, and proven enterprise-scale stability.
With fully automated and event-driven workflows, Control-M helps prevent failures and ensures timely execution of critical business services. The platform’s ability to correlate technical events with business impact—as demonstrated in the Hershey’s example—enables operations teams to respond proactively rather than reactively, protecting revenue and maintaining customer commitments.
Outcomes, Not Just Outputs
Workload automation in 2025 is fundamentally about guaranteeing business outcomes on time, every time, across the most diverse technology estates enterprises have ever operated. With predictive intelligence, business impact correlation, and AI-driven analysis and remediation, organizations can shift from reactive firefighting to proactive assurance—reducing risk, protecting revenue, and enabling faster change while maintaining control and compliance.
The organizations that will thrive are those that recognize workload automation not as a technical implementation but as a strategic capability that directly enables business execution.
BMC named a Leader in 2025 Gartner® Magic Quadrant™ for Service Orchestration and Automation Platforms
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
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