ServiceNow Predictive Intelligence Governance Model

Governance Structure and Decision Authority

Predictive Intelligence in ServiceNow uses machine learning to forecast incident volume, predict incident priority and severity, and estimate resolution time. Governing Predictive Intelligence requires clear decision authority about which predictions the platform will use and how recommendations will be applied.

A Predictive Intelligence Steering Forum meets monthly to review predictive model performance, approve changes to model configuration and decide how predictions are used operationally. Attendees include service delivery leadership, incident management leadership, business analysts and data governance roles.

Day-to-day decisions about incident handling based on predictions can be delegated to incident managers and team leads, but high-risk decisions (automatic escalation, automatic incident closure) should go through the Steering Forum.

Standards for Model Configuration and Accuracy

Document standards for which predictions are actionable (used to drive operational decisions) versus informational (displayed to teams for awareness). A prediction about incident priority based on historical data may be actionable. A prediction about an incident being similar to another incident may be informational.

Define minimum accuracy thresholds for predictions to be used operationally. If a prediction model is right only part of the time, using it to drive automated actions (such as automatic incident reassignment) creates risk. Informational use (displaying the prediction to agents for their judgment) is appropriate for lower-accuracy models.

Establish baseline metrics for models before deployment. How accurate was the old prediction method or manual process? The new predictive model should outperform the baseline to justify adoption.

Change Control and Configuration Management

Predictive Intelligence model configurations should be treated as production configuration. Document changes, version them and require approval before deployment to production. A model configuration change (for example, retraining on new data or adjusting prediction thresholds) can significantly impact operational behavior.

Maintain a configuration management system documenting all active models, their configuration, accuracy metrics and deployment dates. This provides an audit trail and allows you to understand why predictions changed over time.

Establish an approval process for model changes. Business stakeholders should review and approve changes that affect operational decisions (such as incident prioritization predictions). Technical stakeholders should review for implementation correctness and data integrity.

Data Governance and Model Training

Predictive models require high-quality training data. Define standards for data used to train models: the data must be representative, accurate and complete. Poor training data leads to inaccurate or biased models.

Establish processes for retraining models as new data arrives. Models degrade over time if business patterns change but the model is not updated. Annual retraining is common; more frequent retraining may be warranted if business patterns are rapidly evolving.

Document the data sources and features used in each model. This documentation helps teams understand how predictions are generated and identify when model behavior has changed due to data changes or model updates.

Bias Detection and Mitigation

Predictive models can inadvertently encode bias if training data is biased. For example, if historical incident data shows that certain teams receive higher priority assignments (due to past staffing decisions), a priority prediction model trained on that data will reproduce that bias.

Audit models regularly to detect bias. Compare predictions across incident types, teams and customer segments. If the model systematically predicts different priorities for similar incidents based on team or customer, investigate whether bias exists.

Retrain models with balanced training data if bias is detected. Documenting and correcting bias is an ongoing process, not a one-time fix.

Transparency and Explainability

When Predictive Intelligence predictions affect operational decisions (incident priority, assignment), teams should understand why the prediction was made. A priority prediction based on historical incident volume is more defensible than one based on opaque machine learning factors.

Provide visibility into model confidence scores. A prediction made with high confidence (the model is certain) is more reliable than one made with low confidence. Display confidence scores so teams can weight predictions appropriately.

Document model behavior changes over time. If predictions change dramatically after a model update, teams should understand why and whether the change is justified.

Approval and Escalation Procedures

Define which predictions trigger automatic actions (no human review needed) versus those requiring human approval. Automatic actions should be limited to low-risk predictions (such as incident categorization suggestions). High-risk decisions (incident closure, major priority changes) should require human review.

Establish escalation procedures if predictions appear to be incorrect. Incident teams should have a mechanism to report when predictions are significantly wrong and request model review.

Compliance and Audit Requirements

Some regulations require transparency in automated decision-making. If Predictive Intelligence is used to make decisions that affect customers or compliance (such as automatic priority assignment or SLA escalation), ensure that model decisions are auditable and explainable to regulators.

Maintain audit trails showing when predictions were made, what actions were taken based on predictions and whether predictions were correct in hindsight. These trails provide evidence of responsible AI use and help identify problematic patterns.

Matching decision rights to the size of the consequence

Governance for a prediction model is mostly a set of answers to one question: who is allowed to change what, and who has to know when they do. Those answers should follow the size of the consequence rather than the seniority of the person asking. A change that only alters the wording of a suggestion needs little ceremony. A change that moves work between teams, or that touches a customer-facing deadline, needs a named approver and a record. The table sets out a working allocation of rights for the usual kinds of change.

The final row is the one many organizations forget. A model that is switched off leaves a decision that somebody still has to make, and if no one has written down who makes it now, the decision can quietly drift back to whoever was most recently doing it by hand.

A change procedure that keeps a clear trail

A consistent procedure makes governance quick, because people know what to expect. The sequence below suits most changes and can be shortened for the smaller ones.

1. Write the change in one paragraph: what will change, what it is for, and which records or decisions it affects.
2. Attach the evidence named in the decision-rights table, and state what result would make the change a mistake.
3. Route the change to the approver, who signs against the stated result rather than against a general impression of the model.
4. Apply the change in the test environment first, then record the date and the person who applied it in production.
5. Notify the people listed in the table, using the same wording the approval record uses, so that no one receives a different account of the change.
6. Check the first results after the change against the stated result, and record whether the change met it.

Step six is the one that closes the loop. A change that is approved and applied but never checked is a decision with no outcome on record, and the next review has nothing to learn from.

Keeping a register that someone actually reads

A register of models, their settings, their owners and their last review date is the simplest governance tool available, and it is also the one most often abandoned. Keep the register short. One line per model, with the decision it supports, the owner, the date of the last change and the date of the next review, is enough for most teams. Attach the detail to the change records rather than to the register itself, so the register stays readable. Put a standing item on the governance agenda to read the register from top to bottom, because a model with an overdue review date can be the first sign that the governance is slipping.

When this much governance is too heavy

A team with one model, one decision and one owner does not need a steering group. Governance for that team is a register, a named approver and a short change note. Scale the structure up when the number of models grows, when more than one business owner depends on the same model, or when an auditor or a customer is entitled to ask how a decision was reached. Adding structure before those conditions are met tends to produce meetings that approve changes already made and a register that nobody updates.

Who should chair the governance forum?

Whoever owns the decisions most affected by the models, not the platform team. The platform team should attend and advise, but a forum chaired by the people who answer for the outcomes makes better calls about trade-offs.

How should a dispute between two business owners be settled?

Settle it on the evidence for the specific decision, with the sponsor as the final approver if the two cannot agree. Avoid settling it by changing the model, because that moves the disagreement into the training data where nobody can see it.

What is the minimum record for an audit?

The register entry, the change records with their evidence, and the results checked after each change. Those three together show what the model was, why it changed, and whether the change did what it was meant to do.

Does governance need to cover third-party data sources?

Yes, wherever a model learns from or writes to a source the team does not control. Record the source, who owns it, and what happens to the model if the source changes its format or its meaning.

For the people behind these decisions, the roles and responsibilities guide is the companion piece, and operating rights are covered in the security and access design guide. Measures for the register come from the KPI definitions, and the approval evidence is checked against the testing and acceptance checklist. Where audit and evidence requirements apply across the platform, the policy and audit evidence service area covers the wider approach.