To get started with Workload Management (WLM), understand how the capability functions and the efficiency, scalability, and performance improvements it delivers when it is successfully implemented.

WLM is designed to optimize the execution of automated processes, especially when there is a high volume of tasks or when specific service level agreements (SLAs) have to be met. WLM essentially orchestrates how individual pieces of work are distributed and processed by a pool of available devices.

The following image shows WLM as the orchestration engine that optimizes the execution of automated tasks, particularly for high-volume processes.


Getting started with WLM

How WLM works

WLM creates a streamlined automation pipeline that efficiently processes large volumes of work through an intelligent, coordinated process.

  1. The process begins when data is made available from various sources such as CSV files, APIs, or automated tasks. This incoming data is transformed into individual work items that are systematically added to designated work queues.
  2. After work items are queued, business rules and filtering criteria are applied to determine how tasks should be prioritized and distributed. Work items are filtered and sorted based on factors such as urgency, complexity, or business value of the tasks, ensuring that the most important tasks are prioritized.
  3. Work items are distributed to device pools (groups of devices that can process multiple tasks simultaneously).

    These devices pull work items from the queues and execute the required automation steps. By utilizing multiple devices working in parallel, the Control Room can handle numerous work items at the same time, reducing processing time compared to sequential handling.

    Throughout this process, you can get real-time visibility into the queue status, device performance, and overall workflow, enabling you to monitor progress and make decisions as needed to optimize your automation efficiency.

What results to expect

WLM delivers measurable improvements across operational, financial, and strategic criteria:

Optimized resource utilization
A global company achieved 95% bot efficiency using workload management.
Faster processing
WLM enables dynamic queuing and multi-bot execution, cutting cycle times significantly through parallel processing.
Stronger ROI
Customers report an average 250% ROI within 6–9 months, with top performers reaching 380%.
Scalable performance
WLM supports dynamic scaling and prioritization, letting businesses manage surges (For example, seasonal demand) using existing resources.