Workload automation (WLA) and robotic process automation (RPA) are valuable technologies for businesses’ automation infrastructure. Both technologies reduce the number of manual tasks in an organization by automating repetitive processes. Both have benefits such as decreasing human error and cost, increasing efficiency, and creating transparency.
However, WLA tools and RPA tools have different functionalities and use cases that cannot substitute for each other. We explain the differences between WLA and RPA and how they can work together as complementary technologies.
| Aspect | Workload Automation (WLA) | Robotic Process Automation (RPA) |
|---|---|---|
| Definition | Automates and orchestrates IT tasks, workflows, and batch jobs. | Automates repetitive, rule-based business processes. |
| Scope | Broad, focused on IT and backend system operations. | Narrower, focused on front-end user tasks. |
| Primary Users | IT teams and developers. | Business users and process owners. |
| Task Focus | Orchestration of complex workflows across systems and applications. | Automates specific tasks like data entry, invoice processing, etc. |
| Integration | Deep integration with IT systems like databases, servers, and ERPs. | Interacts with user interfaces (e.g., clicking buttons, typing). |
| Technique | System-level automation, often using APIs and command-line scripts. | Mimics human actions using UI interaction and screen scraping. |
| Error Handling | Built-in mechanisms to handle system-level errors. | May require additional configuration for complex error handling. |
| Technical Knowledge | Requires technical expertise to adjust workflows. | Easier to modify by business users for changing requirements. |
| Implementation Time | Medium to long, involving system integration and testing. | Relatively short, focused on quick deployment of bots. |
| Cost | Higher initial investment due to infrastructure and integration. | Lower initial costs, especially for smaller-scale implementations. |
What is workload automation?
Workload automation is the process of using software to centralize and automate scheduling, initiating, and monitoring business workflows and transactions across physical, virtual, and cloud systems. Workload automation tools provide centralized control over the workflows across the enterprise. As certain workload automation tools work both on-premise and on cloud systems, it is easy to integrate it into complex enterprise environment.
WLA tools enable businesses to optimize information exchange between departments, facilitates data access and transfer, and eliminates manual-work-induced errors. Moreover, compliance and transparency through audit trail, optimized data storage and incident management are among the benefits of integrating workload automation in workflow management.
It is important to note that workload automation interacts with back-end code. Hence, it requires a certain level of technical expertise to use. However, in recent years many vendors have produced tools that are low-code/no-code, making it possible for professionals without a technical background to utilize the benefits of automation on workflows. There are also more specialized versions of workload automation tools, such as enterprise job scheduling and software catered to specific operating systems such as windows job scheduling software.
What is RPA?
Robotic process automation (RPA) is a productivity technology for creating customized bots that can observe and recreate user’s interactions with GUI components to execute rule-based tasks. Unlike workload automation, RPA bots interact with the user interface. For example, IT professionals can build RPA bots for data extraction and processing.
However, the capabilities of RPA are not limited to screen-scraping. Next-generation RPA bots, also known as cognitive RPA, can utilize AI to execute more complicated tasks by analyzing data. For instance, by reviewing previous reporting scenarios, RPA tools can implement repetitive tasks for financial reporting.
Top 5 Differences between Workload Automation and RPA
Scope of work
- RPA is focused on tasks involving the frontend used by humans. It is designed to automate repetitive, rule-based tasks traditionally performed by humans using GUI. It can handle data entry, form filling, and generating reports. If the process is complicated or requires human judgment, RPA may not be suitable for that specific process. See RPA use cases.
- WLA is focused on automated software process in the backend. It focuses on scheduling, initiating, and managing system tasks within complex business or IT processes. It is used for running batches of jobs in a coordinated sequence across multiple, complex, interdependent systems and applications in a fault-tolerant manner, see WLA use cases. RPA bots can also be used to coordinate backend processes however, WLA solutions are more specialized for this purpose and may have more advanced features.
Users
- RPA is typically used by business users and process owners.
- WLA tends to be used by IT teams and technical users due to its more technical scope of work.
Integration
- RPA is good at integrating with systems that lack APIs or are difficult to integrate through traditional methods. It is non-invasive and doesn’t require deep system integration.
- WLA requires more sophisticated integration and interaction with the underlying systems. It is better suited for scenarios where a deep level of integration is required and is often used in environments with a mature IT infrastructure.
Complexity of Processes
- RPA is often used for simple, rule-based processes. Handling many different types of exceptions or complex decision making can increase the complexity of RPA bots and can present challenges in maintenance.
- WLA is used for complex business processes involving various dependencies and conditions. It has robust capabilities for handling exceptions, conditional workflows, and dependencies between tasks.
Operational Efficiency
- RPA can help to improve operational efficiency of business units by automating repetitive tasks, freeing up human resources for more complex, higher-value work.
- WLA optimizes IT operations by streamlining and automating the management of business processes across multiple systems and platforms.
Leveraging WLA and RPA together
There are two primary use cases of RPA integration into Workload Automation:
- Workload automation tools feed data into RPA bots from different business departments and can replace the role of a human trigger to initiate an RPA bot.
- Workload automation tools can be used in RPA exception handling and orchestration. WLA tools can monitor the RPA operations and request human intervention if necessary.
An example of RPA integration in workload automation can be seen in profit & loss analysis of equity trading firms. Firstly, workload automation software transfers data from the clearing house to the firm with ETL (extract, transform, load). WLA software can then trigger RPA bots to read the data and take various actions based on the data. For example, journal entries can be generated and reports can be prepared. This allows the business team to modify their own automation solutions based on business or market changes.
Do WLA and RPA solutions have overlapping functionalities?
Workload Automation (WLA) and Robotic Process Automation (RPA) share overlapping functionalities in automating repetitive tasks, data movement, and task orchestration. Both tools streamline workflows by eliminating manual intervention and can trigger processes based on events, schedules, or dependencies. However, their approaches differ significantly: WLA operates at the backend level, leveraging APIs and system-level integrations for complex IT workflows, while RPA focuses on front-end tasks, mimicking human interactions with user interfaces and addressing gaps in systems without APIs.
Moreover, RPA solutions have been investing in backend automation capabilities. 1 . Therefore, RPA orchestrators provided by RPA vendors can also orchestrate RPA bots and most RPA bots have API connection capabilities.
Generative AI’s role in Automation, WLA & RPA
Generative AI is elevating automation from strictly rule-based execution to intelligent, adaptable workflow orchestration. Enterprises that integrate generative AI into their RPA and WLA strategies are seeing smarter bots, faster decision cycles, and an expansion of automation into areas once thought not automatable – positioning them at the forefront of efficiency and innovation in the digital operations realm.
- Hyperautomation and RPA 2.0: The fusion of generative AI with RPA is giving rise to “hyperautomation,” where multiple automation tools and AI techniques work in concert. This trend (sometimes dubbed RPA 2.0) enables end-to-end automation of more complex workflows and is redefining processes across industries. Businesses are investing in unified automation platforms that orchestrate RPA bots, AI models, and other tools together for maximum efficiency.
- AI-Embedded Automation Tools: Automation platforms are increasingly embedding generative AI as a built-in assistant or co-pilot for developers. Leading RPA/WLA vendors now offer features like generative recorders that detect interface changes and auto-fix bot scripts, or AI studios that let users create bots using natural language prompts. This integration helps companies build and maintain automation faster and with less specialized coding expertise.
- Expansion into Creative Tasks: Generative AI is opening new frontiers for what can be automated. Beyond routine data work, organizations are beginning to automate creative and content-driven tasks – for example, generating first drafts of marketing content, designs, or product configurations – something previously outside the realm of automation. By offloading initial creative labor to AI, businesses can accelerate content production and tailor outputs more dynamically.
- Democratization of Automation: Another emerging trend is the push to make automation more accessible to non-technical users through AI. Generative AI tools are expected to revolutionize the user experience so that even staff without programming skills can instruct AI-driven bots to build or modify processes using plain language. This empowers more teams within a business to leverage automation directly, increasing adoption and innovation.
External links
- 1. “UiPath Acquires Cloud Elements to Deliver Expanded API-Based Automation Capabilities“. UiPath. March 23, 2021. Retrieved January 28, 2024.
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