Abstract: The present disclosure discloses methods and systems for digital transformation and integration. The method includes receiving a collection of documents related to operations of an entity. Thereafter, a business process hierarchy based on the collection of documents is generated, using a model and a graph database of accumulated documents. The nodes of the business process hierarchy are divided into different levels of logical groups and includes a plurality of nodes of each level of at least four levels. Further, the business process hierarchy is displayed in a user interface. The user interface allows user to input to modify the business process hierarchy to modify or add at least one node of the business process hierarchy. Furthermore, a plurality of documents is generated, corresponding to different document types for implementing each node of the business process hierarchy, wherein the plurality of documents includes different modalities.

Abstract: A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

Abstract: A device may receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and may process the historical project data and the client data, with machine learning models, to generate recommendations for the problem and confidence scores for the recommendations. The device may process the recommendations and the confidence scores, with an NLG model, to generate a solution to the problem and content for the solution, and may generate a digitized dynamic client solution to the problem based on the solution and the content. The device may provide the digitized dynamic client solution to a user device, and may receive feedback on the digitized dynamic client solution from the user device. The device may generate a final digitized dynamic client solution based on the feedback, and may perform actions based on the final digitized dynamic client solution.

Abstract: A device may receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and may process the historical project data and the client data, with machine learning models, to generate recommendations for the problem and confidence scores for the recommendations. The device may process the recommendations and the confidence scores, with an NLG model, to generate a solution to the problem and content for the solution, and may generate a digitized dynamic client solution to the problem based on the solution and the content. The device may provide the digitized dynamic client solution to a user device, and may receive feedback on the digitized dynamic client solution from the user device. The device may generate a final digitized dynamic client solution based on the feedback, and may perform actions based on the final digitized dynamic client solution.

Abstract: A system for value prediction for dynamic performance optimization includes a project value predictor that receives a Key Performance Indicator (KPI) and an initiative relating to an active project having a closure date. The KPI is associated with a KPI period including multiple intervals. The project value predictor operates to identify a relevant cluster of KPIs for the KPI based on historical data, forecast a future value of the KPI based on attributes/features of the KPI relative to the closure date, predict a possibility of failure of the KPI using a trained data model to pre-classify the KPI, categorize the KPI based on the future value or the pre-classification, where the KPI is categorized as failure based on the future value being less than a target KPI value after the KPI period and added to list for retraining the model based on the categorization. The system also leads to the identification and subsequent validation of Initiatives that impact the KPIs with quantification of the level of impact.

Abstract: A system for value prediction for dynamic performance optimization includes a project value predictor that receives a Key Performance Indicator (KPI) and an initiative relating to an active project having a closure date. The KPI is associated with a KPI period including multiple intervals. The project value predictor operates to identify a relevant cluster of KPIs for the KPI based on historical data, forecast a future value of the KPI based on attributes/features of the KPI relative to the closure date, predict a possibility of failure of the KPI using a trained data model to pre-classify the KPI, categorize the KPI based on the future value or the pre-classification, where the KPI is categorized as failure based on the future value being less than a target KPI value after the KPI period and added to list for retraining the model based on the categorization. The system also leads to the identification and subsequent validation of Initiatives that impact the KPIs with quantification of the level of impact.

Abstract: A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

Abstract: A device may receive a request for a service management plan that is to be used to implement a fully-integrated enterprise resource planning (ERP) system for an organization. The device may receive organizational data associated with the organization. The device may determine, based on the organizational data, observations that serve as hypotheses for deficiencies associated with a current state of a system of the organization. The device may identify, based on the observations, priorities that define a target state for the fully-integrated ERP system that is to be generated. The device may select, based on a machine-learning-driven analysis of the observations and the priorities, a recommendation for a configuration of the fully-integrated ERP system. The device may generate, based on the recommendation, the service management plan for the organization. The device may perform a set of actions to cause the configuration to be implemented.

Abstract: A device may receive a request for a service management plan that is to be used to implement a fully-integrated enterprise resource planning (ERP) system for an organization. The device may receive organizational data associated with the organization. The device may determine, based on the organizational data, observations that serve as hypotheses for deficiencies associated with a current state of a system of the organization. The device may identify, based on the observations, priorities that define a target state for the fully-integrated ERP system that is to be generated. The device may select, based on a machine-learning-driven analysis of the observations and the priorities, a recommendation for a configuration of the fully-integrated ERP system. The device may generate, based on the recommendation, the service management plan for the organization. The device may perform a set of actions to cause the configuration to be implemented.