Use multiple artificial intelligence (AI) engines to determine a next best action for professional development of employees

1. A method comprising: determining, by one or more processors and based on a portion of aggregated data, a plurality of metrics associated with an employee, the aggregated data including activities performed by the employee using a computing device; training individual artificial intelligence engines of a plurality of artificial intelligence engines using at least the portion of the aggregated data to create a plurality of trained artificial intelligence engines; determining, by the one or more processors and based on the plurality of metrics, a unified metric associated with the employee, wherein determining the unified metric comprises: determining a persona variance comprising a difference between a location of the employee on a distribution curve and a persona on the distribution curve, the persona determined based on a set of above average performing employees, the persona having a greater score on the distribution curve than the employee; determining a goal difference between a target goal set for the employee and an actual goal achieved by the employee, the actual goal determined based on the plurality of metrics; determining a scorecard associated with the employee that is determined based on the plurality of metrics; determining a predicted performance associated with the employee for a subsequent time period, the predicted performance predicted by the plurality of trained artificial intelligence engines; and determining the unified metric based at least in part on the persona variance, the goal difference, the scorecard, and the predicted performance; creating, by the one or more processors, a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees; determining, by the one or more processors, the location of the employee on the distribution curve; predicting, by the plurality of trained artificial intelligence engines and based on the location of the employee on the distribution curve, a next best action for the employee; and sending, by the one or more processors, information associated with the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.

2. The method of claim 1 , further comprising:

receiving, from the computing device, activity data associated with activities performed by the employee in a first time interval;

storing the activity data with previously gathered data to created aggregated data; and

selecting a portion of the aggregated data associated with a second time interval, the second time interval greater than the first time interval.

3. The method of claim 1 , further comprising:

determining an accuracy of individual artificial intelligence engines of the plurality of trained artificial intelligence engines; and

selecting, based at least in part of the accuracy, at least a portion of the plurality of trained artificial intelligence engines to determine the unified metric.

4. The method of claim 1 , wherein determining the unified metric further comprises:

performing a personal scaling of the persona variance to create a scaled persona variance;

performing a goal scaling of the goal difference to create a scaled goal difference;

performing a scorecard scaling of the scorecard to create a scaled scorecard;

performing a prediction scaling of the predicted performance to create a scaled predicted performance; and

determining the unified metric based on a weighted sum of the scaled persona variance, the scaled goal difference, the scaled scorecard, and the scaled predicted performance.

5. The method of claim 1 , wherein the next best action comprises:

scheduling a nudge;

scheduling a training class;

scheduling a coaching session with a supervisor;

scheduling the employee to receive a type of positive reinforcement; or

any combination thereof.

6. The method of claim 1 , further comprising:

for a particular metric of the plurality of metrics, dividing the distribution curve into:

a below average portion;

an average portion; and

an above average portion; and

determining the next best action based at least in part on which portion of the distribution curve the employee is located.

7. The method of claim 1 , further comprising:

determining a development plan for the employee based at least in part on the location of the employee on the distribution curve.

8. A server comprising:

one or more processors; and

one or more non-transitory computer readable media storing instructions executable by the one or more processors to perform operations comprising:

determining, based on a portion of aggregated data, a plurality of metrics associated with an employee, the aggregated data including activities performed by the employee using a computing device;

training individual artificial intelligence engines of a plurality of artificial intelligence engines using at least a portion of the aggregated data to create a plurality of trained artificial intelligence engines;

determining, based on the plurality of metrics, a unified metric associated with the employee, wherein determining the unified metric comprises:

determining a persona variance comprising a difference between a location of the employee on a distribution curve and a persona on the distribution curve, the persona determined based on a set of above average performing employees, the persona having a greater score on the distribution curve than the employee;

determining a goal difference between a target goal set for the employee and an actual goal achieved by the employee, the actual goal determined based on the plurality of metrics;

determining a scorecard associated with the employee that is determined based on the plurality of metrics;

determining a predicted performance associated with the employee for a subsequent time period, the predicted performance predicted by the plurality of trained artificial intelligence engines; and

determining the unified metric based at least in part on the persona variance, the goal difference, the scorecard, and the predicted performance;

determining a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees;

determining a location of the employee on the distribution curve;

predicting, by the plurality of trained artificial intelligence engines and based on the location of the employee on the distribution curve, a next best action for the employee; and

sending information associated with the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.

9. The server of claim 8 , further comprising:

receiving, from the computing device, activity data associated with activities performed by the employee in a first time interval;

storing the activity data with previously gathered data to created aggregated data; and

selecting a portion of the aggregated data associated with a second time interval, the second time interval greater than the first time interval.

10. The server of claim 8 , wherein:

a first artificial intelligence engine of the plurality of trained artificial intelligence engines uses a first type of artificial intelligence algorithm; and

a second artificial intelligence engine of the plurality of trained artificial intelligence engines uses a second type of artificial intelligence algorithm that is different from the first type of artificial intelligence algorithm.

11. The server of claim 8 , wherein determining the unified metric further comprises:

performing a personal scaling of the persona variance to create a scaled persona variance;

performing a goal scaling of the goal difference to create a scaled goal difference;

performing a scorecard scaling of the scorecard to create a scaled scorecard;

performing a prediction scaling of the predicted performance to create a scaled predicted performance; and

determining the unified metric based on a weighted sum of the scaled persona variance, the scaled goal difference, the scaled scorecard, and the scaled predicted performance.

12. The server of claim 8 , wherein the next best action comprises:

scheduling a nudge;

scheduling a training class;

scheduling a coaching session with a supervisor;

scheduling the employee to receive a type of positive reinforcement; or

any combination thereof.

13. The server of claim 8 , the operations further comprising:

determining an accuracy of individual artificial intelligence engines of the plurality of trained artificial intelligence engines; and

selecting, based at least in part of the accuracy, at least a portion of the plurality of trained artificial intelligence engines to determine the unified metric.

14. The server of claim 8 , wherein:

a first artificial intelligence engine of the plurality of trained artificial intelligence engines uses a first type of artificial intelligence algorithm; and

a second artificial intelligence engine of the plurality of trained artificial intelligence engines uses a second type of artificial intelligence algorithm that is different from the first type of artificial intelligence algorithm.

15. A memory device to store instructions executable by one or more processors to perform operations comprising: determining, based on a portion of aggregated data, a plurality of metrics associated with an employee, the aggregated data including activities performed by the employee using a computing device; training individual artificial intelligence engines of a plurality of artificial intelligence engines using training data that includes at least a portion of the aggregated data to create a plurality of trained artificial intelligence engines; determining, based on the plurality of metrics, a unified metric associated with the employee; determining a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees, wherein determining the unified metric comprises: determining a persona variance comprising a difference between a location of the employee on a distribution curve and a persona on the distribution curve, the persona determined based on a set of above average performing employees, the persona having a greater score on the distribution curve than the employee; determining a goal difference between a target goal set for the employee and an actual goal achieved by the employee, the actual goal determined based on the plurality of metrics; determining a scorecard associated with the employee that is determined based on the plurality of metrics; determining a predicted performance associated with the employee for a subsequent time period, the predicted performance predicted by the plurality of trained artificial intelligence engines; and determining the unified metric based at least in part on the persona variance, the goal difference, the scorecard, and the predicted performance; determining a location of the employee on the distribution curve; predicting, by the plurality of trained artificial intelligence engines and based on the location of the employee on the distribution curve, a next best action for the employee; and sending information associated with the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.

16. The memory device of claim 15 , further comprising:

receiving, from the computing device, activity data associated with activities performed by the employee in a first time interval;

storing the activity data with previously gathered data to created aggregated data; and

selecting a portion of the aggregated data associated with a second time interval, the second time interval greater than the first time interval.

17. The memory device of claim 15 , further comprising:

determining an accuracy of individual artificial intelligence engines of the plurality of trained artificial intelligence engines; and

selecting, based at least in part of the accuracy, at least a portion of the plurality of trained artificial intelligence engines to determine the unified metric.

18. The memory device of claim 15 , wherein the next best action comprises:

scheduling a nudge;

scheduling a training class;

scheduling a coaching session with a supervisor;

scheduling the employee to receive a type of positive reinforcement; or

any combination thereof.

19. The memory device of claim 15 further comprising:

dividing the distribution curve into:

a below average portion;

an average portion; and

an above average portion; and

determining the next best action based at least in part on which portion of the distribution curve the employee is located.

20. The memory device of claim 15 , further comprising:

determining a development plan for the employee based at least in part on the location of the employee on the distribution curve.