Abstract: Optimization of smart notifications within an organization can lead to increase productivity and reduction in missed deadlines and human work hours. An approach for to dynamic scheduling of smart notifications may be presented herein. The approach may include obtaining historical decision event data for an organization. Historical decision data may include details regarding the importance of the historical decision event to the organization, individuals within the organization associated with the decision event, and benchmarks of the historical decision event. The approach may include analyzing a future decision event based on a decision event model trained with the historical decision event data. The approach may include identifying one or more priority factors associated with the future decision event data based on the analysis of the decision event priority model. A smart notification may be scheduled for the decision event, based on the identified priority factors.

Abstract: In an approach to provide consistent job descriptions for global entities, computer-implemented methods, computer program products, and computer systems for standardizing global entity job descriptions are described. The computer-implemented method for standardizing global entity job descriptions may include one or more processors configured for receiving first job identifier data corresponding to a job posting having a plurality of job descriptions, generating second job identifier data by removing duplicate job descriptions and outlier job descriptions from the plurality of job descriptions, determining job description intents for the plurality of job descriptions based at least on the second job identifier data, generating third job identifier data corresponding to job description similarity groupings based on the job description intents and the second job identifier data, and generating fourth job identifier data comprising a standard job description based on the third job identifier data.

Abstract: An approach for determining most a qualified employee for a job based on analyzing gap in between skill of the employee and the job description requirement is disclosed. The approach utilizes machine learning to extract key skills like functional skills of an employee profile from an organization and job descriptions with a hierarchy of profiles. The approach builds a multi-dimension vector representation for each employee key skills and job descriptions. The approach calculates the vector distance between the key skills in profile vector and job description vector and maintaining the scores for each node. Finally, the approach generates the skill gap summary for the employee by matching the job description with employee profiles.