Abstract: A device may receive organization data defining first capabilities of an organization and industry trend data that is relevant to the organization. The industry trend data may define second capabilities that are relevant to the organization. The device may provide, as input to a capability model, the organization data and the industry trend data. The capability model may have been trained to produce, as output, data specifying recommended changes for the organization. The device may determine, based on the output of the capability model and the industry trend data, a recommendation. The device may perform an action based on the recommendation.

Abstract: A device receives occupational activity descriptions and occupational role attributes, and processes the occupational activity descriptions to generate estimated occupational activity attribute values. The device trains a neural network model based on the estimated occupational activity attribute values to generate a trained neural network model, and receives a new activity description for a new role in an organization. The device processes the new activity description, with the trained neural network model, to generate estimated new activity attribute values, and processes the estimated new activity attribute values, with the logistic regression model, to generate probabilities that the new role is suitable for different workforce types. The device determines a workforce recommendation for the new role based on the probabilities that the new role is suitable for the different workforce types.

Abstract: A device may receive organization data defining first capabilities of an organization and industry trend data that is relevant to the organization. The industry trend data may define second capabilities that are relevant to the organization. The device may provide, as input to a capability model, the organization data and the industry trend data. The capability model may have been trained to produce, as output, data specifying recommended changes for the organization. The device may determine, based on the output of the capability model and the industry trend data, a recommendation. The device may perform an action based on the recommendation.

Abstract: A device may receive information associated with an entity-specific designation of a role, and determine a standardized designation of the role that corresponds to the entity-specific designation of the role after comparing the information associated with the entity-specific designation of the role and information associated with a set of standardized designations of roles. The device may receive information associated with a set of tasks that is associated with the role, and compare the information associated with the set of tasks and information associated with a set of automation-capable tasks. The device may determine a set of automation scores associated with the set of tasks, and provide information associated with the set of automation scores to permit an action to be performed based on the set of automation scores.

Abstract: A device receives occupational activity descriptions and occupational role attributes, and processes the occupational activity descriptions to generate estimated occupational activity attribute values. The device trains a neural network model based on the estimated occupational activity attribute values to generate a trained neural network model, and receives a new activity description for a new role in an organization. The device processes the new activity description, with the trained neural network model, to generate estimated new activity attribute values, and processes the estimated new activity attribute values, with the logistic regression model, to generate probabilities that the new role is suitable for different workforce types. The device determines a workforce recommendation for the new role based on the probabilities that the new role is suitable for the different workforce types.

Abstract: A device may receive information associated with an entity-specific designation of a role, and determine a standardized designation of the role that corresponds to the entity-specific designation of the role after comparing the information associated with the entity-specific designation of the role and information associated with a set of standardized designations of roles. The device may receive information associated with a set of tasks that is associated with the role, and compare the information associated with the set of tasks and information associated with a set of automation-capable tasks. The device may determine a set of automation scores associated with the set of tasks, and provide information associated with the set of automation scores to permit an action to be performed based on the set of automation scores.

Abstract: The present invention generally relates to estimating a customer's lifetime value to a company. The customer's lifetime value to the company can be based on remaining value of existing products and one or both of new purchase value and historic profitability. The remaining value and new purchase value for the customer may be estimated based on the customer's current customer segment and the customer's predicted future migration to a different customer segment. In addition, the remaining value may be estimated based on expected customer attrition, and the new purchase value may be estimated based on expected individual customer purchases.