Systems and methods for digital workforce intelligent orchestration

1. A system for digital workforce intelligent orchestration:

a task queuing system configured to receive a first task;

a first robotic process automation bot configured to execute actions to perform the first task; and

orchestration hardware configured to manage the first robotic process automation bot to execute the actions to perform the first task based on an application predictive model and a task predictive model, wherein the application predictive model is configured to determine a first future expected performance of computing resources associated with performing the first task at a first time period compared to a second future expected performance of the computing resources associated with the performing the first task at a second time period, and the task predictive model is configured to determine future tasks to be added to the task queuing system over a segment of time, the segment of time includes the first time period and the second time period, wherein the first time period is a different future time period in which to initiate the first task than the second time period, wherein the first future expected performance of computing resources is different than the second future expected performance.

2. The system of claim 1 , wherein the orchestration hardware is configured to commission and decommission a second robotic process automation bot responsive to the first robotic process automation bot executing actions to perform the first task.

3. The system of claim 1 , wherein the task predictive model is configured to determine the future tasks to be added to the task queuing system based on a history of tasks added to the task queuing system.

4. The system of claim 1 , wherein the first robotic process automation bot is configured to generate metadata based on performance metrics of the first robotic process automation bot executing actions to perform the first task.

5. The system of claim 1 , wherein the first task includes a prescribed run book of keystrokes associated with a client computing device that will be automated by the first robotic process automation bot.

6. The system of claim 1 , wherein the first task includes business metrics, the business metrics being assigned a numerical value based on priority.

7. The system of claim 1 , wherein the task queuing system receives a second task, wherein to conserve the computing resources the orchestration hardware idles the first robotic process automation bot while the second task is within the task queuing system based on the application predictive model and the task predictive model.

8. The system of claim 7 , wherein the second task is the only task in the task queuing system.

9. The system of claim 1 , wherein the task queueing system is configured to determine an amount of the computing resources requires to complete all received tasks over the segment of time.

10. The system of claim 1 , wherein the expected performance of computing resources associated with the first task at the first time period compared to the second time period is based on a percentage of computing resources required for the first robotic process automation bot to perform the first task at the first time period and the second time period.

11. A method for digital workforce intelligent orchestration:

receiving a first task;

executing, via a first robotic process automation bot, actions to perform the first task;

managing, via orchestration hardware, the first robotic process automation bot to execute the actions to perform the first task based on an application predictive model and a task predictive mode;

determining a first future expected performance of computing resources associated with performing the first task at a first time period compared to a second future expected performance of the computing resources associated with the performing the first task at a second time period; and

determining future tasks to be added to the task queuing system over a segment of time, the segment of time includes the first time period and the second time period, wherein the first time period is a different future time period in which to initiate the first task than the second time period, wherein the first future expected performance of computing resources is different than the second future expected performance.

12. The method of claim 11 , further comprising:

commissioning and decommissioning a second robotic process automation bot responsive to the first robotic process automation bot executing actions to perform the first task.

13. The method of claim 11 , further comprising:

determining the future tasks to be added to the task queuing system based on a history of tasks added to the task queuing system.

14. The method of claim 11 , further comprising:

generating metadata based on performance metrics of the first robotic process automation bot executing actions to perform the first task.

15. The method of claim 11 , wherein the first task includes a prescribed run book of keystrokes associated with a client computing device that will be automated by the first robotic process automation bot.

16. The method of claim 11 , wherein the first task includes business metrics, the business metrics being assigned a numerical value based on priority.

17. The method of claim 11 , further comprising:

receiving a second task;

idling the first robotic process automation bot while the second task is within the task queuing system based on the application predictive model and the task predictive model; and

conserving the computing resources by idling the first robotic process automation bot.

18. The method of claim 17 , wherein the second task is the only task in the task queuing system.

19. The method of claim 11 , further comprising:

determining an amount of the computing resources requires to complete all received tasks over the segment of time.

20. The method of claim 11 , wherein the expected performance of computing resources associated with the first task at the first time period compared to the second time period is based on a percentage of computing resources required for the first robotic process automation bot to perform the first task at the first time period and the second time period.