Robotic Process Automation, an important step towards Hyperautomation

In the previous blog, process mining as one of the six key technologies of hyperautomation was visited. This blog explores Robotic Process Automation and its contribution to hyperautomation.

Process mining helps us identify and prioritize automation opportunities and determine the best candidates for Digital Decisioning to pursue #hyperautomation. As argued, #RPA can be a quick fix to create and free the resources for more permanent hyperautomation solutions. Therefore its contributions to hyperautomation should not be underestimated.

How Robotic Process Automation works

Robotic Process Automation (RPA), such as the commercial UiPath or the open-source Robot Framework, uses software robots to automate repetitive and rule-based tasks. Doing so can eliminate the need for human intervention in these automated processes. RPA software robots or bots can do this without changing the legacy systems. However, the bot's actions, such as clicking buttons, filling out forms, or extracting data, need RPA-specific programming and require an additional capability of an often different programming language. Therefore, RPA should always be considered extra software requiring further management and maintenance.

Once the software robots are programmed to perform these tasks, they can work around the clock without needing breaks or rest, thus increasing efficiency and reducing errors. RPA can be a valuable tool for automating manual and repetitive tasks, but there may be better permanent solutions for some use cases.

Is RPA a permanent solution?

However, in other cases, RPA may not be a permanent solution. For example, if the business process is subject to frequent changes, the RPA implementation may require updates to keep up with the changes. Additionally, suppose the business process requires significant judgment or regulations-based decision-making. Then, RPA may not be sufficient, and other technologies, such as digital decisioning and machine learning, may need to be incorporated.

Therefore, whether RPA is a permanent solution depends on the situation, the specific use case, and the business context. Organizations should consider the benefits and limitations of RPA and their long-term automation strategy when deciding whether to implement RPA as a permanent solution.

Factors to consider replacing RPA with a permanent solution:

• Changeability: If the automated process is subject to frequent changes, often caused by changing regulations, RPA may not be a robust solution.
• Complexity: RPA is best suited for automating simple, repetitive tasks. Suppose the automated process involves complex decision-making or requires unstructured data processing; a more advanced solution may be necessary, such as digital decisioning, artificial intelligence (AI), or machine learning (ML).
• Robustness: RPA is susceptible to errors if the inputs or outputs of the process being automated are not consistent or reliable. A more robust solution may be necessary if the automated process requires high accuracy or reliability.
• Scalability: If the volume of transactions in the automated process is expected to increase significantly, the scalability of the RPA solution may become a challenge. In such cases, it may be more appropriate to consider a more scalable solution, such as an end-to-end process automation platform.
• Cost-effectiveness: RPA can automate simple, repetitive tasks but may not be the most cost-effective solution for more complex processes. In such cases, investing in a more advanced solution like digital decisioning that can provide excellent value over the long term may be more cost-effective.

In summary, organizations should consider the scalability, complexity, robustness, and cost-effectiveness of RPA when deciding whether to replace it with a permanent solution. If the automated process requires advanced capabilities, a more robust solution may be necessary to achieve the desired outcomes.

RPA is a quick fix for automating repetitive and time-consuming manual tasks

Temporarily solution with a goal?

RPA and Digital Decisioning can work together to streamline business processes and improve efficiency. One way to do this is by using RPA to elicitate business rules that can later be implemented using Digital Decisioning and embedded into the business processes. In addition, by using process and decision mining, organizations can identify the decision points. Once the decision points and the involved manual labor, organizations can use RPA to elicitate, document, and implement the business rules with RPA, refine and validate them, and later implement them using #digitaldecisioning. The result is a more efficient and accurate way to automate business processes, with better maintainable decisioning capabilities built into the process, instead of on top of it.

Robotic Process Automation and Digital Decisioning are different technologies that can streamline business processes and improve efficiency. While RPA is designed to automate repetitive tasks, Digital Decisioning focuses on automating complex decisions based on predefined business rules, data analytics, and machine learning.

Here's how this can be done:

• Start automating decision points: As the RPA bots perform manual tasks, decision logic can be identified and refined. In addition, these decision points can be documented and analyzed to determine the actual business rules being used.
• Refine and validate the business rules: Once identified, they can be refined and validated. This refinement involves analyzing the regulation-based business rules to ensure they are accurate and up-to-date and refining them as necessary to ensure they are effective and correct.
• Implement Digital Decisioning: Once the business rules have been refined and validated, they can be implemented using Digital Decisioning and integrated into the business processes. This implementation involves developing decision models that can automate the decisions based on the rules.

Preparing for digital decisioning

Replacing RPA with Digital Decisioning embedded in business processes can offer several benefits:

• Increased flexibility: RPA bots are typically designed for a specific task or process and may require significant changes if the process changes. Digital Decisioning, on the other hand, can be designed to adapt to changing business rules and requirements.
• Improved accuracy: RPA bots rely on predefined rules to automate tasks, which means they can make mistakes if the rules are not updated or accurate. On the other hand, Digital Decisioning can use advanced analytics and Machine Learning to make decisions, resulting in more accurate outcomes.
• Better efficiency: RPA bots are designed to automate specific tasks, but they may not be able to make decisions independently. With Digital Decisioning, decisions can be automated as part of the process, allowing for faster and more efficient operations.
• Cost savings: RPA bots require ongoing maintenance and may need to be updated frequently to remain effective. Digital Decisioning can offer cost savings by automating decisions as part of the process, reducing the need for manual intervention and ongoing maintenance.

Replacing RPA with Digital Decisioning can offer the next step to hyperautomation. By embedding digital decisioning capabilities into the process, organizations can further improve operations and reduce costs while maintaining the flexibility to adapt to changing requirements.

Final thoughts

RPA is, in many situations, a quick fix for automating repetitive and time-consuming manual tasks. However, this temporary fix introduces technical debt that needs resolving later on. Therefore, RPA should never be seen as the final solution; however, it can be beneficial to elicitate the conditions and business rules of manual tasks, preparing for the next step in hyperautomation: automating operational manual decisions.

More articles published in the hyperautomation series

• Enterprise motivation to pursue hyperautomation
• Core technologies for driving hyperautomation
• How enterprises are optimizing business processes and value chains for driving hyperautomation
• Mining business processes preparing for hyperautomation, and discovering decision points
• Robotic Process Automation, a step towards?Hyperautomation
• Digital Decisioning at the heart of hyperautomation, creating business value
• How applying Artificial Intelligence helps hyperautomation
• Six preparation steps to leverage Machine Learning for hyperautomation
• 8 technologies around the core in the epicenter of Hyperautomation