rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Tapping into a digital brain
    sub title: AI-powered talent management at Infosys
  name:
    name part: Sawhney, Mohanbir S.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Poddar, Varun
    role:
      role term: author.
  name:
    name part: Kruse, Katharine
    role:
      role term: author.
  type of resource: text
  genre: Electronic books.
  genre: Case studies
  origin info:
    place:
      place term: enk
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations.
  abstract: 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.
  target audience: specialized
  note: Mohanbir Sawhney, Varun Poddar, and Katharine Kruse.
  subject:
    topic: Personnel management
    genre: Case studies
  subject:
    topic: Organizational behavior
    genre: Case studies
  subject:
    topic: Artificial intelligence
    genre: Case studies
  subject:
    topic: Personnel
    topic: Direction
    genre: Études de cas
  subject:
    topic: Comportement organisationnel
    genre: Études de cas
  subject:
    topic: Intelligence artificielle
    genre: Études de cas
  subject:
    topic: Artificial intelligence
  subject:
    topic: Organizational behavior
  subject:
    topic: Personnel management
  classification: HF5549 .S29 2022
  classification: 658.3
  related item:
    title info:
      title: SAGE business cases
  identifier: 9781529622904
  identifier: 1529622905
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 230109
    record change date: 20240923172047.9
    record identifier: in00005500480
    language of cataloging:
      language term: eng