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Administrator Guide
Last Updated: 2026-02-06
Steps: Set Up Machine Learning for Time Anomalies

Steps: Set Up Machine Learning for Time Anomalies

You might need to take additional steps to enable this feature based on your organization's subscription service agreement. Your organization is either on the Main Service Agreement (MSA) or the Universal Main Service Agreement (UMSA). To determine your organization's subscription service agreement:
  1. Select your profile avatar on Workday Community.
  2. Select
    Profile
    .
  3. On your profile page, select your organization's name, which is beneath your name and next to your job title.
  4. View your
    Subscription Service Agreement
     value.
If the value is:
  • UMSA
    , the feature is automatically available. You can skip the
    Enable Innovation Services Feature and AI Data Contributions for MSA Customers
     step. For more information on Machine Learning data contributions, see Concept: Workday AI for Universal Main Subscription Agreement Customers.
  • MSA
    , you must enable this feature through Innovation Services using the
    Enable Innovation Services Feature and AI Data Contributions for MSA Customers
    step.
Note: UMSA customers don't have Innovation Services tasks and reports in their tenants as these are for MSA customers only. UMSA customers can ignore all information regarding Innovation Services.
  • Deploy Workday Time Tracking. We recommend you deploy Time Tracking at least 1 year before implementing time anomaly detection.
  • Create time approval templates.
You can enable Workday to use machine learning to detect atypical time entries so that you can:
  • Create custom reports to view time anomaly scores and top time anomaly reasons for time blocks.
  • Access the
    Time Anomaly Trends
     report to view time anomaly trends by supervisory organization.
  • Enable managers and timekeepers to see and take action on time anomalies within the
    Review Time
     report.
  1. On the
    Innovation Services and Data Selection Opt-In
     report, select the
    HCM: Workforce Management Machine Learning Services
     section.
    To build a machine learning model that can detect time anomalies, you must opt in to all of these categories:
    • Time Configuration Data
    • Worker Data
    • Historical Schedule Block Data
    • Worker Time and Absence Data
    You can also opt into
    User Feature Delivery Data
     to help Workday detect time anomalies.
    You might need to take additional steps to enable this feature depending on your organization's subscription service agreement. For more information, see this Community article.
  2. Access the
    Edit Tenant Setup - Machine Learning
     task.
    Select the region in which Workday hosts data used for improvement and personalization of machine learning and analytics functionality.
    Security:
    Set Up: Tenant Setup - Machine Learning
     in the System functional area.
  3. (Optional) Access the
    Edit Time Approval Template
     task.
    Select the
    Include Time Anomalies
     check box to display time anomalies in the
    Review Time
     report.
    Security:
    Set Up: Time Tracking
     in the Time Tracking functional area.
We recommend that you train the machine learning model in the Production tenant for at least 3 weeks. When you train in a Production tenant, you ensure that the model has a constant stream of real data to train on so that it can learn patterns of acceptable and unacceptable time blocks.
If you need to test in a nonproduction tenant, you can enable time anomaly detection in Implementation tenants. If you began testing in an Implementation tenant before 2024-10-04, you now need to enable on-demand data extraction to continue regular data extraction.