ITSM Predictive Intelligence Workbench dashboard

Release version: Yokohama

Updated January 30, 2025

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Summary of ITSM Predictive Intelligence Workbench dashboard

The ITSM Predictive Intelligence Workbench dashboard provides a comprehensive interface to measure and monitor the impact of machine learning on automating IT service management (ITSM) processes, specifically incident management. It enables ServiceNow customers to visualize operational and automation metrics, track model lifecycle stages, and communicate business value to stakeholders effectively.

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Important: Starting with the Yokohama release, this Workbench is deprecated and will no longer be supported or enhanced. Customers are encouraged to switch to the Task Intelligence for ITSM application plugin for the latest machine learning capabilities.

Key Features

Dashboard Tabs:
- Business Value: View key performance indicators that demonstrate the value of predictive intelligence in automating ITSM incidents.
- Monitoring Models: Track model performance, including the Net Automation Threshold graph which alerts stakeholders when automation performance falls below expected levels.
- Model Statistics: Access detailed statistics on prediction accuracy, coverage, and other metrics.
Automated Comments: The dashboard automatically generates comments on Performance Analytics indicators reflecting model lifecycle events such as integration, removal, changes, and retraining, providing clear visibility into model status.
Performance Indicators: Key indicators include:
- Prediction Coverage for Incident: Percentage of applied predictions out of attempted predictions.
- Prediction Precision for Incident: Accuracy of successful predictions compared to total predictions made.
- Net Automation for Incident: Combined measure of prediction precision and coverage indicating the overall automation effectiveness.
- Estimated Net Automation: Daily estimated percentage of automation achieved through predictive models.
Data Breakdowns and Visualizations: Various charts and scores present data on prediction quality, class distributions, excluded classes due to low data, and the number of predictions skipped due to low confidence. These visualizations help in understanding model behavior and data characteristics.
Use Case Filtering: New database views combine Predictive Intelligence platform data with Workbench use case data, allowing filtering by specific use cases to better analyze impact on metrics.

End Users and Roles

Process architects, process owners, and machine learning advocates: Gain insights into the operational and automation metrics to support decision-making and promote the value of predictive intelligence automation.
Workbench managers and viewers: Hold roles [piwbmanager] or [piwbviewer] to access the dashboard and measure automation benefits.

Practical Benefits

Enables customers to quantify and demonstrate the business value of machine learning in ITSM incident automation.
Facilitates proactive monitoring of model performance and alerts on automation thresholds to maintain high-quality predictions.
Improves stakeholder communication through clear visualizations and automated status comments.
Supports data-driven decisions to enhance and scale automation capabilities within ITSM processes.

ITSM Predictive Intelligence Workbench provides the Predictive Intelligence for Incidents dashboard to enable you to measure the value of using machine learning to automate your IT business processes. Monitor use case models and view associated statistics. Effectively demonstrate business value to stakeholders with dashboard views.

Important:

Starting with the yokohama release, the ITSM Predictive Intelligence Workbench will reach its end of life (EOL) as part of its deprecation process. It will be completely deprecated and no longer deployed, enhanced, or supported from the yokohama release. To get the latest experience for this functionality, you must install the Task Intelligence for ITSM application (com.snc.itsm_ml_task) plugin. For more information, see Task Intelligence for ITSM

For details, see the Deprecation Process [KB0867184] article in the Now Support Knowledge Base.

Know exactly what stage your model workflows are in with automated comments on specific ServiceNow® Performance Analytics indicators. Comments appear on the Actual Net Automation indicators. Model stages comments include, when the model is integrated, when an integration is removed, when an existing model is changed to a new model, and when retraining is complete.

Predictive Intelligence for Incidents dashboard Business Value tab — Figure 1. Business Value tab

Predictive Intelligence for Incidents dashboard Monitoring Models tab — Figure 2. Monitoring Models tab

You can view the Net Automation Threshold graph on the Monitoring Models tab. This threshold is a computation of the difference between estimated net automation and the underperformance property values. Thresholds are calculated daily and notifications are sent to stakeholders when thresholds are breached.

View automatic comments on the Actual Net Automation indicator at various stages of the model monitoring. A comment is generated when any of the following model criteria exists:

Model is integrated
Model integration is removed
Existing model is changed to a new model
Integrated model is retrained

Predictive Intelligence for Incidents dashboard Model Statistics tab — Figure 3. Model Statistics tab

End user and roles


End user and goal	Required role	Benefits
Process architect, process owner, or machine learning advocate: Visualize operational and automation metric performance. Communicate machine learning value to stakeholders to increase the automation capabilities of predictive intelligence.	Predictive Intelligence Workbench manager or viewer [piwb_manager] [piwb_viewer]	Ability to measure the value of using Predictive Intelligence to automate ITSM processes.

Indicators

Prediction Coverage for Incident

The score for this indicator is calculated according to the formula:

if ([[Number of attempted predictions based on Created Today for Incident]]==0){0}else{[[Number of applied predictions based on Created Today
                        for Incident]]/[[Number of attempted predictions based on Created Today for Incident]]*100}

Number of predicted results based on final value date for incident

The number of predicted results is based on the final value date for an incident. The score is measured daily as unit #.

Net Automation for Incident

The score for this indicator is calculated according to the formula: [[Prediction Precision for Incident]]*[[Prediction Coverage for Incident]]/100.

Number of applied predictions based on Created Today for Incident

Number of applied predictions based on Created Today for Incident is measured daily as unit #.

Number of attempted predictions based on Created Today for Incident

Number of attempted predictions based on Created Today for Incident is measured daily as unit #.

The goal for this indicator is to maximize the quality of predictions.

Prediction Precision for Incident

The score for this indicator is calculated according to the formula:

if ([[Number of predicted results based on final value date for Incident]]==0){0}else{[[Number of successful predictions not skipped based on
                        final value date for Incident]]/[[Number of predicted results based on final value date for Incident]]*100}

Estimated Net Automation

Estimated net automation measured daily as unit %.

Number of successful predictions not skipped based on final value date for Incident

Number of successful predictions not skipped based on final value date is measured daily as unit #. It is the count on data source MLPredictorResults.FinalValueDate.Incident, which is using the table: ml_predictor_results.

Number of Predicted for Incidents

Number of Predicted for Incidents is measured daily as unit #. It is the count on data source MLPredictorResults.CreatedToday.Incident, which is using the table: ml_predictor_results.

Breakdowns

Use Case.

Data visualizations


Title	Type	Source table	Description
Predictions Skipped	Single Score	sn_piwb_itsm_conte_dbv_ml_predictor_results	% Estimated Precision. Estimated precision measured as unit %, based off the data the model was trained on.
Predictions Skipped	Line	sn_piwb_itsm_conte_dbv_ml_predictor_results	Predictions Skipped (num). Number of ___, due to low confidence.
Class Distribution - Training Data	Bar	sn_piwb_itsm_conte_dbv_ml_classes	Predictions Skipped (line).
% Estimated Precision	Single Score	sn_piwb_itsm_conte_dbv_ml_solutions	Predicted Correctly (num). Number of ___, comparing initial predicted value to final record value.
Classes Excluded due to Low Distribution	Single Score	sn_piwb_itsm_conte_dbv_ml_excluded_classes	Predicted Correctly (line).
Class Distribution - Actual	Bar	sn_piwb_itsm_conte_dbv_ml_predictor_results	Predicted Incorrectly (num). Number of ___, comparing initial predicted value to final record value.
Predicted Correctly	Single Score	sn_piwb_itsm_conte_dbv_ml_predictor_results	Predicted Incorrectly (line).
Predicted Correctly	Line	sn_piwb_itsm_conte_dbv_ml_predictor_results	Class Distribution, Training Data. Distribution of classes in data that the solution was trained on.
Predicted Incorrectly	Single Score	sn_piwb_itsm_conte_dbv_ml_predictor_results	Class Distribution, Actual. Current distribution of classes in live data.
Predicted Incorrectly	Line	sn_piwb_itsm_conte_dbv_ml_predictor_results	Predicted Classes. Number of values the model can return as a prediction.
Prediction Classes	Single Score	sn_piwb_itsm_conte_dbv_ml_classes	Classes Excluded due. Number of values the model was not confident enough to return as a prediction, due to not enough data.

Note:

Regarding source tables, all indicators and reports are built off of newly added database views, combining ServiceNow platform Predictive Intelligence tables with Predictive Intelligence Workbench use case tables. With these new views, you can filter platform data by use case to better understand its impact on your metrics. All database views for this content application have a prefix of sn_piwb_itsm_conte_dbv.