Understanding Predictive Intelligence Workbench in ServiceNow Paris Release

ServiceNow Programmer working on Predictive Intelligence Work bench

As a part of innovating service delivery efficiencies, ServiceNow introduced the Predictive Intelligence feature to integrate the capabilities of Artificial Intelligence (AI) in all their service offerings, such as HR, IT, and CSM. To further leverage predictive intelligence for businesses, the ServiceNow Paris release introduced the Predictive Intelligence Workbench feature, which makes leveraging AI and Machine Learning (ML) effortless.

What is Predictive Intelligence Workbench?

Predictive Intelligence Workbench offers built-in templates to guide users through various Machine Learning implementations to achieve automation and deliver business value. For better performance and the support of various international languages, access in Chrome browser’s latest release.

This image shows the current supported languages by capability, and the release versions in which the language is deployed.

Figure: Predictive Intelligence Workbench Available Languages

What Predictive Intelligence Workbench Offers

ITSM Predictive Intelligence Workbench Implementation

To enhance business processes and workflows, one can choose the available Machine Learning-based default templates or train the Predictive Intelligence Workbench model as per specific business needs. The built-in models can be either guided or non-guided, whereas some are fully pre-trained.

These pre-trained models improve the predictive model implementation process with a fully guided process. The pre-trained model facilitates the evaluation phase in the use case setup process. If the model is a perfect fit, it will then integrate with the business process. Once the models are tuned, they can be tested with appropriate data and then incorporated into business workflows. To know the percentage of precision in choosing the right model, the projected percentage is displayed, as follows:

Using Predictive Intelligence Workbench, all progress can be tracked. we can track the entire progress. Users with either admin or manager roles are authorized to use the predefined templates to create predictive ML models.

Here are the steps involved in the Predictive Intelligence Workbench model creation:

  • Create a model
  • Train the model
  • Evaluate the model
  • Tune the model to requirements
  • Perform Testing
  • Integrate the best model

Predictive Intelligence Workbench Dashboard

Powered by predictive intelligence capabilities, the Predictive Intelligence Workbench dashboard offers various business metrics. These metrics facilitate analysis of the efficiency of predictive use case models and calculate the value ML capacities offer by automating business processes. The dashboard provides easy means to correlate the business metrics with the ML metrics to understand how these models are driving business goals and communicating overall business value generated to the stakeholders.

Figure: Predictive Intelligence WorkBench Dashboard

Some parameters the dashboard displays are:

  • Predicted incidents
  • Prediction classes
  • Total number of predicted incidents
  • Meantime to resolve
  • Estimated and actual net automation
  • Business value generated
  • Prediction coverage for an incident
  • Predicted results based on the incident final date
  • The daily score of predications applied from the day the incident was created
  • Overall automation score
  • Accuracy in predictions

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