http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Infosys, an India-based global IT consulting and software services provider, had more than 200,000 employees, mainly software specialists and technical consultants, who were assigned to projects by a talent allocation team that made staffing recommendations based on detailed manual assessments of employees' skill sets and experience. However, Infosys delivery managers, responsible for managing the talent assigned to projects, often rejected the matches proposed by the talent team and hoarded human resources for their own projects. In fall 2017, Infosys leaders assembled a cross-functional team and tasked it with developing a new AI-based talent management system to replace the old manual process. The new system was to provide a 360-degree view of open positions and available employees and use an algorithm to make hiring recommendations for projects. The AI team members had to make various data, design, and deployment decisions before they could begin building the new solution. First, they had to select data variables to define "supply" (i.e., employees available for assignments) and "demand" (i.e., staffing needs for client engagements). They also had to establish a system for ensuring this data was kept accurate and up to date after launch. Last--but perhaps most critical--the team had to develop a plan for maintenance and continuous improvement of the model, even at this early stage.
Mohanbir Sawhney, Varun Poddar, and Katharine Kruse.