Process evolution for robotic process automation and workflow micro-optimization
Process evolution for robotic process automation (RPA) and RPA workflow micro-optimization are disclosed. Initially, an RPA implementation may be scientifically planned, potentially using artificial intelligence (AI). Embedded analytics may be used to measure, report, and align RPA operations with strategic business outcomes. RPA may then be implemented by deploying AI skills (e.g., in the form of machine learning (ML) models) through an AI fabric that seamlessly applies, scales, manages AI for RPA workflows of robots. This cycle of planning, measuring, and reporting may be repeated, potentially guided by more and more AI, to iteratively improve the effectiveness of RPA for a business. RPA implementations may also be identified and implemented based on their estimated return on investment (ROI).
1. A computer-implemented method, comprising:
receiving, by a computing system, a plan for a robotic process automation (RPA) implementation, the plan comprising business data;
performing analytics on the business data associated with the plan, by the computing system, to measure, report, and align RPA operations with strategic business outcomes;
generating one or more RPA workflows implementing automations based on the analytics, by the computing system, the one or more RPA workflows comprising a set of activities defining an execution order and a relationship of the set of activities to provide automation of one or more rule-based processes; and
generating and deploying the one or more RPA workflows via one or more RPA robots, by the computing system, as automations to be executed by the one or more RPA robots, wherein
the deployed RPA workflows are configured to cause the one or more RPA robots to use one or more drivers to control and interact with a respective computing system.
2. The computer-implemented method of claim 1 , wherein the business data comprises employee interactions with computing systems, financial information, time taken to perform robot operations and/or workflow steps, or any combination thereof.
3. The computer-implemented method of claim 1 , wherein the plan is derived from processes automatically identified from data collected by robots using artificial intelligence (AI).
4. The computer-implemented method of claim 1 , wherein the analytics determine revenue generated by an employee and/or robot, time taken to perform workflow operations by a robot, time taken by an employee to complete a business process, or any combination thereof.
5. The computer-implemented method of claim 1 , further comprising:
collecting and analyzing data from business computing systems, by the computing system; and
automatically identifying and suggesting one or more new processes for potential automations via one or more machine learning (ML) models.
6. The computer-implemented method of claim 5 , further comprising:
iteratively repeating the process of performing analytics on the business data associated with the plan, generating RPA workflows implementing automations based on the analytics, generating and deploying the generated workflows via RPA robots, collecting and analyzing data from business computing systems, and automatically identifying and suggesting one or more new processes for potential automations, thereby increasing effectiveness of RPA.
7. The computer-implemented method of claim 1 , further comprising:
analyzing and prioritizing each activity in an RPA workflow, by the computing system, based on performance criteria.
8. The computer-implemented method of claim 7 , further comprising:
adding an activity to the RPA workflow or deleting an activity from the RPA workflow to create a modified RPA workflow, by the computing system;
generating and running an RPA robot implementing the modified RPA workflow, by the computing system; and
testing the generated RPA robot, by the computing system, to determine whether the RPA robot achieves a goal and realizes an improvement in the performance criteria.
9. The computer-implemented method of claim 8 , wherein the determination of whether the RPA robot achieves the goal and realizes the improvement in the performance criteria is based on a reward function.
10. The computer-implemented method of claim 7 , wherein when the RPA robot does not achieve the goal, does not realize the improvement in the performance criteria, or both, the method further comprises:
reverting to the RPA workflow prior to modification; and
adding another activity to the RPA workflow or deleting another activity from the RPA workflow, generating an RPA robot implementing the modified RPA workflow, and testing the generated RPA robot, by the computing system.
11. The computer-implemented method of claim 7 , wherein when the RPA robot achieves the goal and realizes the improvement in the performance criteria, the method further comprises:
deploying the RPA robot, by the computing system.
12. A computer-implemented method, comprising:
performing analytics on the business data associated with a plan for a robotic process automation (RPA) implementation, by a computing system, to measure, report, and align RPA operations with strategic business outcomes;
generating one or more RPA workflows implementing automations based on the analytics, by the computing system, the RPA workflow comprising a set of activities defining an execution order and a relationship of the set of activities to provide automation of one or more rule-based processes; and
generating and deploying the one or more RPA workflows via one or more RPA robots, by the computing system, as automations to be executed by the one or more RPA robots, wherein
the deployed RPA workflows are configured to cause the one or more RPA robots to use one or more drivers to control and interact with a respective computing system.
13. The computer-implemented method of claim 12 , wherein the business data comprises employee interactions with computing systems, financial information, time taken to perform robot operations and/or workflow steps, or any combination thereof.
14. The computer-implemented method of claim 12 , wherein the plan is derived from processes automatically identified from data collected by robots using artificial intelligence (AI).
15. The computer-implemented method of claim 12 , wherein the analytics determine revenue generated by an employee and/or robot, time taken to perform workflow operations by a robot, time taken by an employee to complete a business process, or any combination thereof.
16. The computer-implemented method of claim 12 , further comprising:
collecting and analyzing data from business computing systems, by the computing system; and
automatically identifying and suggesting one or more new processes for potential automations via one or more machine learning (ML) models.
17. The computer-implemented method of claim 16 , further comprising:
iteratively repeating the process of performing analytics on the business data associated with the plan, generating RPA workflows implementing automations based on the analytics, generating and deploying the generated workflows via RPA robots, collecting and analyzing data from business computing systems, and automatically identifying and suggesting one or more new processes for potential automations, thereby increasing effectiveness of RPA.
18. A computer-implemented method, comprising:
analyzing and prioritizing each activity in a robotic process automation (RPA) workflow, by a computing system, based on performance criteria, the one or more RPA workflows comprising a set of activities defining an execution order and a relationship of the set of activities to provide automation of one or more rule-based processes;
adding an activity to the RPA workflow or deleting an activity from the RPA workflow to create a modified RPA workflow, by the computing system;
generating and running an RPA robot implementing the modified RPA workflow as an automation run by the RPA robot, by the computing system; and
testing the generated RPA robot, by the computing system, to determine whether the RPA robot achieves a goal and realizes an improvement in the performance criteria, wherein
the RPA workflow is configured to cause the RPA robot to use one or more drivers to control and interact with the computing system.
19. The computer-implemented method of claim 18 , wherein when the RPA robot does not achieve the goal, does not realize the improvement in the performance criteria, or both, the method further comprises:
reverting to the RPA workflow prior to modification; and
adding another activity to the RPA workflow or deleting another activity from the RPA workflow, generating an RPA robot implementing the modified RPA workflow, and testing the generated RPA robot, by the computing system.
20. The computer-implemented method of claim 18 , wherein when the RPA robot achieves the goal and realizes the improvement in the performance criteria, the method further comprises:
deploying the RPA robot, by the computing system.