HR PIWB template: Recommend estimated time to resolve

  • Release version: Zurich
  • Updated August 8, 2025
  • 6 minutes to read

    • Train your solution by using historical data to predict numeric outputs based on the historic data. Configure the solution definition to predict the estimated time to resolve a HR case.

    Before you begin

    Role required: sn_piwb_hr_content.admin

    About this task

    Regression solutions enable you to predict a point estimate and prediction interval. When making predictions, regression also enables you to specify a confidence level for the prediction interval (range). Understand the ETTR configuration information from Estimated time to resolve HR cases.

    Procedure

    1. Navigate to All > Predictive Intelligence Workbench > Use Cases > Create New from Templates.
    2. From the templates list, go to Estimated Time to Resolve an HR case and click Start.
      A pop-up with model name appears. This use case is handled in the classic Predictive Intelligence. You will be taken there to complete setup. You can perform the following steps:
      1. Ensure you click View product documentation for setting up this use case to review the instructions on how to configure the solution definition.
        Note:
        Ensure you review and understand the documentation for creating the solution definition.
      2. Click Take me there to get started with the solution definition.
      Machine Learning Solutions landing page appears.
      Figure 1. HR ML usecases
      List of ML usecases for HR PIWB
    3. Go to Regression > ml_sn_sn_hr_core_global_hr_case_resolution_time from the available Solution Definitions, click Configure.
      Regression solution definition for HR case resolution time appears.
    4. On the solution definition form, configure these fields per the following guidance.
      Table 1. Form fields and configurations for solution definition
      Field Value
      Label Enter a unique name for your regression solution. For example, enter HR Case Resolution Time.
      Name As you enter your solution Label value, this field automatically populates with a system-assigned name that's similar to your label value ml_sn_sn_hr_core_global_hr_case_resolution_time
      Word Corpus

      Select an existing word corpus that's relevant to your solution. For example, in this use case you select a word corpus that has a title such as Word Corpus Regression.

      If you don’t have a relevant word corpus, follow the steps in Create a word corpus. When the word corpus is complete, you can select it from the Word Corpus field in your Regression Definition form.

      However, the word corpus selection is optional. If your input data has text columns and you don't choose a word corpus, your regression solution trains a new word corpus model by using the text columns in your input data. The resulting word corpus can be reused in any other regression solution or other ML solution type.

      Note:
      The number of records per table for word corpus creation used in regression solutions is limited to 300,000.
      Table Select the database table on which you’re applying regression. The table should contain historical records the system can use to predict the length of time for its database restore HR Case [sn_hr_core_case].
      Output Field

      Select the field whose value you want the predictive model to set. In general, a good output field is a numeric, integer, or floating point field.

      In this example scenario, you use the actual_resolution_time field to measure a length of time. The output field should generate a numeric value.
      Fields Select one or more field types that help the system identify the records you want to train using regression. In this scenario, you use short_description, description, hr_service,assignment_group, topic_detail, topic_category, priority,sys_class_name. Input field types can be string, nominal, or numeric.
      Filter (Optional) Add filter conditions to the output field records you want to train using regression. Ensure you have sufficient records by adjusting the filters.

      The minimum number of records for regression training is 10,000 records.

      The maximum number of records for regression training is limited to 300,000.
      Processing Language Select the dominant language of the dataset you're training on the solution definition. If the dataset language is English, choose English. Also, English processing is applied to all datasets by default. For example, if you select Italian, the system processes the data in both English and Italian.
      Note:
      The term processing indicates some of the language-specific steps used as part of training a solution. For example, tokenizing words, removing stop words, and stemming.
      Stopwords When you select your processing language, the system automatically adds a Stopwords list that uses the same language. For example, if your processing language is Italian, the Default Italian Stopwords list appears. The Default English Stopwords list also appears in your selection as well. If you create a custom stopwords list, you can select it from the Stopwords field to add it to your solution. In this scenario, you use the Default English Stopwords list.
      Training Frequency
      Select how often the system regenerates the solution based on records matching the Filter. Your options include:
      • Run Once
      • Every 30 days
      • Every 60 days
      • Every 90 days
      • Every 120 days
      • Every 180 days

      In this scenario, you select Every 30 days

      By default, the system runs training once. This practice provides you time to review and update the solution definition as needed until it provides acceptable coverage and precision values.

      The minimum number of records required for regression solution training is at 10,000.

      The ML scheduler limits the number of trainings an instance can commit to 50 new ML training requests per instance within a 24 hour window. This excludes scheduled re-training requests. In addition, clustering and similarity updates are also excluded from this limit, even if the new training requests exceed 50 within a 24 hour window.
    5. Click the appropriate context menu option or button for your solution definition.
      OptionDescription
      Save or Save & Train Save your solution definition record so you can return to it later, or save and submit it for training.
      Submit or Submit & Train Create your solution definition record and submit it, or submit and train it.
    6. If you submitted the solution for training, click OK on the Training Activation window to confirm.
      The system schedules the solution for training with the nearest training service. The system sends you a notification when the training completes, including any errors that may have occurred in the training. Any other users can subscribe to the Predictive Intelligence Notifications category. When training completes, the system uploads the solution as an Attachment record.

      When the configuration is complete, the employees and agents see the estimated time to resolve at the request sections.

    What to do next

    In this scenario, you created an ML solution from your solution definition. In the Related Links section of your ML solution, see the Solution Statistics, Test Solution, and Solution Definition tabs. On the Solution Statistics tab, review the Point Estimate and Range (prediction interval) statistics your solution has provided.
    Figure 2. Regression solution definition
    Regression solution definition statistics for HR case

    On the Test Solutions tab of your solution, you can test the prediction output for the records you used as input to the prediction by entering values for the input fields. You can also use the default prediction confidence level of 95, or enter a different level between 0 and 100. Using 95 as the value means that the system is 95% confident that the actual prediction falls within the prediction interval. Click the Run Test button to find the prediction output.

    After you run the test, the prediction output statistics appear. The Point Estimate on the screen is a single value at one point in time. When the configuration is complete, the employees and agents see the estimated time to resolve at the request sections.

    You can verify the integration status from HR Administration > HR AI Configurations > Solution definition. The use case is now mapped to the selected solution definition.