Advances in artificial intelligence (AI) have greatly expanded the range of tasks that computers can perform, including their application to the hiring process. But how are organisations managing the process of implementation with their workforce?

During the Coronavirus pandemic Walmart needed to rapidly onboard tens of thousands of workers in a short period to meet increased demand. This was a catalyst for the company to bring forward existing plans to update their hiring system for store-level hourly paid associates in the US, leveraging machine learning and predictive analytics.

This research project explored how staff responded to changes to the hiring system and the implications of these responses for the project’s objectives.

Organisational objectives and changes to the hiring process

Walmart’s objectives for the recruitment project were to:

  1. Make better hiring decisions in terms of length of tenure and employee performance.
  2. Minimise the influence of human bias in hiring decisions.
  3. Speed up the hiring and onboarding system to enable the recruitment of a greater number of associates within a shorter time period.
  4. Protect the health and safety of hiring staff and applicants during the pandemic by reducing the need for face-to-face contacts.

Changes to the hiring system included:

  • Using a machine learning algorithm to help rank applicants in Walmart’s ‘Hiring Helper’, a hiring database system which manages job requisitions and applications.
  • Replacement of in-person interviews with shorter telephone interviews.


Qualitative interviews with 14 Walmart employees

  • Seven employees from home office with a range of responsibilities for the development and implementation of the project.
  • Five people leads and store managers.
  • Two recently recruited hourly paid associates.
  • Enabling people leads and managers to make job offers over the phone.

Key findings

Implementation was successful and the changes were largely valued by hiring staff.  However, lack of awareness and confidence in some changes threatened to undermine some of the project objectives. For example, users had reservations about the pre-employment assessment and the algorithm’s ability to predict quality hires.  Concerns about the ability to assess candidates over the phone also meant that some users had reverted to in-person interviews.

Research outputs

‘Walsmart’: when AI hits the shop floor
A new AI-hiring system at Walmart was designed to speed up recruitment and reduce bias, but would hiring managers be willing to put their faith in the AI to choose the right person for the job?


University of Sussex Business School
University of Sussex Business School