HR Predictive Intelligence Workbench implementation

  • Release version: Washingtondc
  • Updated February 1, 2024
  • 3 minutes to read
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    Summary of HR Predictive Intelligence Workbench Implementation

    The HR Predictive Intelligence Workbench (PIWB) enables ServiceNow customers to leverage machine learning for optimizing HR service delivery processes. By utilizing pre-built use case templates, users can create and implement predictive models that enhance existing workflows, ultimately improving efficiency and productivity.

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    Key Features

    • Pre-built Use Case Templates: Users can access guided templates that simplify the model setup process, including options for auto-trained models that expedite integration into business processes.
    • Solution Definitions: Customers can create custom solution definitions to manage various HR functions, such as predicting HR services for incoming cases and automating email case categorizations.
    • Model Creation Phases: The process involves creating, training, testing, and integrating models, allowing for continuous refinement based on predictions and business needs.
    • Prediction Maintenance: Users can manage prediction accuracy by periodically retraining and modifying models to adapt to changing business conditions.

    Key Outcomes

    By implementing the HR PIWB, ServiceNow customers can expect to:

    • Improve case resolution efficiency through accurate predictions of HR services and assignment groups.
    • Enhance knowledge management by identifying gaps and recommending relevant articles for HR agents and employees.
    • Optimize productivity and reduce costs through automated processes, such as email case categorization and knowledge recommendations.

    This implementation ultimately empowers organizations to make data-driven decisions, leading to enhanced HR service delivery and improved employee experiences.

    You can use machine learning to optimize your business processes. You can train and implement HR Predictive Intelligence Workbench use cases to augment your existing application workflows.

    Explore common use case templates

    With the sn_piwb_hr_content.admin role, you can explore the use case templates and create predictive machine learning models. To create a machine learning model, you first select a pre-built use case template. You can use one of the following templates for setting up the use cases.
    • Guided templates include a comprehensive setup process to help you through implementation. Templates with available auto-trained models accelerate your setup process, by providing a pre-generated model based on your data.
    • Classic templates include a comprehensive setup information to help you through implementation. Leverage the existing templates to configure, test, and train the models based on your business requirements.

    When a template indicates Auto-trained model available, this means you can go directly to the evaluation phase of the use case setup. If the auto-trained model is acceptable, you can directly integrate the model with your business processes. Otherwise, you can tune this model or create another model. You may change the name and description of the use case later.

    Solution definitions

    These solution definitions are available as templates on instances where both Predictive Intelligence and HR are active. Create your own solution definition records to customize the behavior.

    Table 1. HR Solution Definitions
    Solution Type Solution Definition Description Implementation
    Classification Predict the HR service for incoming cases Predicts the correct HR service for cases. Guided
    Classification Predict the assignment group for incoming cases Predicts the correct assignment group for cases. Guided
    Classification Email Case Categorization Auto-categorizes the HR service for the email cases for improved productivity by saving time and costs. Guided
    Similarity Similar Closed HR Cases Recommends similar cases closed in the past to assist an HR agent for faster and better resolution. Classic
    Similarity User profile based recommendation Recommends top 3 relevant articles and catalog items based on users with a similar profile for content discovery and personalized experience. Classic
    Similarity Similar HR Cases and knowledge Automates the discovery of knowledge gaps in your knowledge bases and recommends insights into improving knowledge that is driven by demand. Classic
    Similarity Similar Knowledge Articles for HR Task Displays related articles to assist employees in completing the HR or Content or Campaign to-dos. Classic
    Similarity Similar Knowledge Articles for HR Case Uses ML to identify the most relevant knowledge articles to assist an HR agent for faster and better resolution. Classic
    Regression HR Case Resolution Time Determines the resolution time expected a case by analyzing similar closed cases in past for better visibility and transparency. Classic
    Clustering Demand Insights: HR Case Clusters Need Knowledge Identifies the case clusters that do not have knowledge and helps with filling the knowledge gaps in your knowledge base. Classic

    Use case creation phases

    Creating a predictive machine learning model involves several phases. After you create and train your model, evaluate and tune, test prediction results, and integrate the model with your business process. Use case model creation phases include:
    • Create and train models: Define parameters to create a model that you train based on your unique data. Create multiple models as you tune and refine your models by defining the right combination of coverage and precision to use.
    • Test your models: Get prediction results from your models to decide which one is best to integrate with your business process. To see if a model returns a correct result, you can use either the single or batch testing process.
    • Integrate the best model: Deploy the best model into your business process. After you determine which model returns the best, correct result, integrate it into production.

    Prediction accuracy maintenance

    You can manage prediction drift by retraining, modifying, or creating new solutions to reflect changes in your business conditions. Test and modify your business rule over time to ensure it works as desired across multiple consumption points and user persona.