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ServiceNow Introduces “Predictive Intelligence for Change Management” to Reduce ITSM Risk

Executive Summary

ServiceNow has launched Predictive Intelligence for Change Management, a new feature designed to help IT teams identify, assess, and mitigate risks associated with change requests. Leveraging AI and machine learning, the tool aims to improve change success rates and reduce service disruptions for enterprise ITSM operations.

What Happened?

On 9 December 2025, ServiceNow announced the general availability of Predictive Intelligence for Change Management as part of its Now Platform enhancements. Key capabilities include:

  • AI-driven risk scoring for every change request, based on historical outcomes and environmental factors

  • Automated recommendations to reduce risk, such as additional testing or stakeholder approvals

  • Real-time alerts for high-risk changes and potential conflicts

  • Integration with existing ITSM workflows to ensure seamless adoption

This update is intended to empower IT leaders to make more informed decisions, proactively manage risk, and streamline the entire change management process.

Why Does It Matter?

For medium to large organisations—especially those operating across Europe and Africa—this feature delivers:

  • Reduced change failure rates and fewer service disruptions

  • Faster, more confident approvals with data-driven risk analysis

  • Stronger compliance and audit trails for regulated environments

  • Proactive risk management embedded directly into ITSM workflows

By embedding predictive intelligence into change management, ServiceNow enables IT teams to balance agility with operational stability.

Impact on ITSM

  • Improves change success rates and reduces risk of outages

  • Supports continuous improvement and compliance in ITSM

  • Frees up IT staff to focus on innovation rather than firefighting

What Should Organisations Do?

  • Enable Predictive Intelligence for Change Management in the Now Platform

  • Train change managers and approvers on interpreting risk scores and recommendations

  • Monitor change outcomes to refine best practices and automation rules

  • Use real-time alerts to prioritise oversight of high-risk changes

Sources

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