Identifying unkown decision making factors from communications data

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• access a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process, wherein a decision input at the decision making point determines a next step in the process from multiple next-step options;
• obtain, from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process based on timestamps of the communications;
• analyze the electronic communication data to identify decision making content associated with the decision making point of the process;
• input the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point;
• automatically update the process model to explicitly include the predicted decision making factor as part of the process, thereby generating an updated process model.

A system comprising:

• a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
• access a process model comprising a representation of steps in a lifecycle of a process, including a process step associated with a decision making point of the process, wherein a decision input at the decision making point determines a next step in the process from multiple next-step options;
• obtain, from one or more digital collaboration platforms, electronic communication data for communications between human participants in the process;
• analyze the electronic communication data to identify decision making content associated with the decision making point of the process using natural language processing (NLP);
• input the decision making content to a trained machine learning (ML) model, thereby generating an output of a decision making factor predicted to impact the decision input at the decision making point;
• and
• generate and send a report to a user regarding the predicted decision making factor.