Ai-powered enterprise copilot system and method

A method comprising using at least one hardware processor to:

• obtaining data information of an enterprise from more than one source;
• wherein the data information is associated with users, asset classes and costs,
• wherein the processor detects undiscovered existing users and assets;
• processing the data information to identify valid users and asset classes, anomalies, and relationships between usage and costs;
• generating a mapping of the valid users and valid asset classes to define a relationship and associated trend between an enterprise asset, an enterprise user, and enterprise costs;
• deriving a usage information on the enterprise asset by the enterprise user based on the relationship and associated trend between the enterprise asset, enterprise user, and enterprise costs;
• providing the usage information on the enterprise asset by the enterprise user to an artificial intelligence (AI) system for data analysis and pattern recognition,
• wherein the AI system includes one or more large language model module configured to perform an analysis of the usage information on the enterprise asset by the enterprise user to optimize asset allocation in an enterprise,
• wherein the large language model module utilizes a natural language processing interface;
• analyzing the recommendation based on whether the recommendations have materialized in the enterprise,
• wherein the enterprise performs an action based on whether the recommendation has materialized;
• and
• using a result of the optimizing of the asset allocation to train the large language model module.

A system to generate decision recommendations to optimize asset allocation in an enterprise, the system comprising:

• at least one hardware processor;
• and
• software that is configured to, when executed by the at least one hardware processor,
• obtaining data information of an enterprise from more than one source,
• wherein the data information is associated with users, asset classes and costs,
• wherein the processor detects undiscovered existing users and assets;
• processing the data information to identify valid users and asset classes, anomalies, and relationships between usage and costs,
• generating a mapping of the valid users and valid asset classes to define a relationship and associated trend between an enterprise asset, an enterprise user, and enterprise costs;
• deriving a usage information on the enterprise asset by the enterprise user based on the relationship and associated trend between the enterprise asset, enterprise user, and enterprise costs,
• providing the usage information on the enterprise asset by the enterprise user to an artificial intelligence (AI) system for data analysis and pattern recognition,
• wherein the AI system includes one or more large language model module configured to perform an analysis of the usage information on the enterprise asset by the enterprise user to optimize asset allocation in an enterprise,
• wherein the large language model module utilizes a natural language processing interface;
• analyzing the recommendation based on whether the recommendations have materialized in the enterprise,
• wherein the enterprise performs an action based on whether the recommendation has materialized, and
• using a result of the optimizing of the asset allocation to train the large language model module.