The Evolution of Workload Automation: A Guide for the Future

It is the era of interconnected systems and 24/7 operations. So, it goes without saying that IT needs to support seamless, reliable services.

That is where the concept of workload automation (WLA) stemmed from. It was a way for IT to schedule "jobs" that otherwise would rely on a human operator to do.

But what exactly is workload automation, and how has it evolved?

Organizations today are grappling with increasing complexities in IT infrastructure. IT teams must optimize resource utilization and ensure critical business processes run like clockwork.

Whether it's processing payroll, updating databases, provisioning cloud resources, or enabling real-time data analytics, the need of the hour is an engine that drives IT efficiency.

In this blog, we’ll explore the evolution of workload automation, its practical applications, and how it has morphed to support modern IT operations.

Ready to unlock the full potential of automation? Let’s begin by tracing the journey of workload automation!

Workload Automation – A Brief History

The concept of workload automation has evolved significantly over time.

Its roots can be traced back to the early days of computing when batch processing dominated IT operations. In those days, operators manually scheduled tasks, such as running payroll or processing transactions, on mainframes. These manual tasks were very time consuming.

Workload Automation Evolution

1960s–1980s: Batch Job Scheduling

The first wave of workload automation tools emerged in the 1960s and 1970s and focused on job scheduling for mainframe systems. Tools like IBM's Job Control Language (JCL) were used to script and sequence batch jobs. These tools, while groundbreaking at the time, were limited to specific systems and lacked flexibility.

1990s: The Rise of Cross-Platform Scheduling

As IT infrastructures became more diverse, organizations needed solutions that could manage tasks across multiple platforms.

This led to the development of cross-platform job schedulers, which could coordinate workloads across UNIX, Windows, and mainframe environments. Vendors like BMC (Control-M) and CA Technologies emerged during this period, offering job scheduling tools.

2000s: The Shift to Workload Automation

By the 2000s, the term "workload automation" replaced "job scheduling." This shift reflected the growing complexity of IT environments and the need for more dynamic, event-driven automation. WLA tools started integrating with enterprise applications, and databases.

2010s: Limitations with Cloud and Hybrid Environments

The explosion of cloud computing further pushed the barriers of WLA. Organizations required tools that could manage workloads on-premises and across public and private clouds. Automation solutions that offered these hybrid capabilities began to evolve, and the focus became orchestration across diverse environments.

2020s: The Era of AI

In recent years, there has been an increasing need for automation to incorporate artificial intelligence and machine learning for predictive analytics and intelligent decision making. Modern automation tools analyze patterns, predict workload bottlenecks, and automatically optimize task execution.

This evolution aligns with broader trends in IT, such as AIOps and autonomous IT systems.

Understanding the Differences: Job Scheduling, Workload Automation, and Workflow Automation

While often used interchangeably, job scheduling, workload automation, and workflow automation are distinct concepts in IT operations.

Each plays a unique role in streamlining processes but differ in scope, functionality, and complexity.

Let’s break them down.

Job Scheduling

What it is:
Job scheduling refers to the basic process of automating the execution of individual tasks (or "jobs") at specific times or based on predefined triggers. Think of it as the foundational layer of automation, focused on running tasks like batch jobs, backups, or data transfers.

Key Characteristics:

• Trigger-based: Jobs run at specific times, intervals, or events.
• Standalone focus: Primarily manages individual tasks with limited interdependencies.
• Traditional use case: Common in legacy systems like mainframes to handle repetitive IT tasks.

Example Use Case:
Scheduling a nightly data backup to occur at 2 a.m.

Workload Automation (WLA)

What it is:
Workload automation builds on job scheduling but operates at a broader and more integrated level. It coordinates and manages complex workloads across multiple systems, applications, and environments, ensuring they run efficiently and in sequence.

Key Characteristics:

• Cross-platform orchestration: Handles tasks across different IT environments (on-premises, cloud, or hybrid).
• Dependency-aware: Takes into account interdependencies between tasks and workflows.
• Scalable: Adapts to modern IT infrastructure, including multi-cloud and containerized environments.
• Modern use case: Supports business-critical operations, integrating with enterprise resource planning (ERP) or customer relationship management (CRM) systems.

Example Use Case:
End-of-day financial processing tasks on a mainframe include reconciling transactions, updating account balances, generating reports, and backing up critical data.

Workflow Automation

What it is:
Workflow automation focuses on streamlining business processes by connecting multiple tasks into cohesive workflows. It often involves automating decisions, approvals, and notifications, blending operational and business workflows.

Key Characteristics:

• Process-oriented: Maps out and automates entire workflows rather than isolated tasks.
• User-friendly: Often comes with visual tools for designing workflows.
• Business integration: Bridges IT operations and broader business processes.
• Advanced use case: Incorporates low-code/no-code platforms for non-technical users.

Example Use Case:
Automating an IT ticket resolution process: identifying the issue, assigning the ticket, triggering corrective actions, and notifying stakeholders upon completion.

Job Scheduling, Workload Automation, and Workflow Automation Work Together

Understanding the role of these three types of automation can help organizations choose the right tools and strategies based on their operational needs and goals.

Knowing the difference ensures you can build a sound automation strategy for your organization, one that includes the right portfolio of tools. Beyond a strong foundation, it can help scale automation effectively, whether you're running legacy systems, managing hybrid environments, or optimizing business workflows.

• Job Scheduling serves as the backbone of automation, handling singular, isolated tasks.
• Workload Automation expands on this, managing complex dependencies and coordinating tasks across platforms.
• Workflow Automation takes automation a step further, integrating operational and business processes to create seamless workflows.

The Future of Workload Automation

The concept of workload automation (WLA) is rapidly evolving to meet the demands of modern IT landscapes.

While historically focused on scheduling and managing jobs on mainframes, the future of automation lies in its ability to adapt to a world where services are the primary focus and speed, reliability, and scalability are paramount.

Go From Mainframes to Modern Architectures

Traditional workload automation was designed to handle jobs in centralized environments, primarily mainframes. But as organizations embrace containerized applications, multi-cloud infrastructures, and distributed systems, the old approach of running jobs in silos no longer suffices. The future of WLA must address:

• Containerized Environments: Workloads now run in dynamic, ephemeral environments such as Kubernetes clusters, requiring automation tools to manage jobs across microservices seamlessly.
• Hybrid and Multi-Cloud Infrastructures: Enterprises are leveraging a mix of on-premises systems, private clouds, and public cloud platforms, increasing the complexity of orchestrating workloads.

Service-Oriented Automation

The future of automation is service-centric, focusing on how reliably and quickly services are delivered to the business. IT operations are moving away from task-specific job scheduling to broader orchestration of workflows that align with business objectives. This involves:

• End-to-End Visibility: Enabling IT teams to monitor the status and performance of workloads across distributed systems in real-time.
• Dynamic Adaptability: Automatically adjusting workflows based on real-time conditions, such as resource availability or service-level agreements (SLAs).

AI and Machine Learning Integration

AI and ML are set to play a crucial role in the next generation of automation platforms by providing predictive insights and adaptive automation. These technologies will enable:

• Intelligent Scheduling: AI can predict workload peaks and optimize resource allocation to avoid bottlenecks.
• Proactive Issue Resolution: ML algorithms can detect anomalies in workload execution and take corrective actions before failures occur.

Event-Driven Automation

The future of automation is not only scheduled—which is what workload automation tools are currently focused on—but rather both scheduled and event driven. Responding to triggers in real time is critical as businesses demand higher uptimes and better service for their digital offerings.

For example, workflows can be initiated by specific business events, such as a new customer order or a spike in website traffic.

Low-Code/No-Code Automation

To meet the demand for faster innovation, workload automation platforms will evolve to include low-code or no-code interfaces. These interfaces will empower business users and IT teams to design and execute workflows without extensive programming knowledge, accelerating time-to-value.

Orchestration Beyond IT

As automation expands, it will play a vital role in orchestrating workflows across IT and business functions, breaking down silos. For example, workloads could span IT infrastructure, customer-facing applications, and back-office processes, ensuring cohesive service delivery.

The Evolution of Workload Automation?

This is it. The moment we answer the million-dollar question!

It goes without saying that the future of workload automation is not what it has been. In fact, it’s not at all centered around workloads but rather "workflows."

The future of workload automation is about more than just running jobs—it’s about orchestrating complex workflows across diverse systems and environments, ensuring seamless and efficient service delivery.

By integrating AI, adopting event-driven approaches, and embracing modern architectures, automation will remain a cornerstone of IT operations. However, IT needs more than workload automation to meet the needs of increasingly complex and dynamic business environments. It needs to adopt more workflow-centric abilities.

Organizations that embrace this paradigm shift will gain a competitive edge, delivering services faster, more reliably, and with greater agility.