AI/agent-based solutions

[BiankaK]

Hi everyone,

We are starting to experiment with AI/agent-based solutions in our organization and I wanted to ask how other ServiceNow admins are approaching this:

What are the recommended best practices for integrating AI tools (e.g., AI Agents, external AI services) with ServiceNow while ensuring platform stability and governance?

• Have you experienced any performance issues or unexpected behavior when introducing AI functionalities?

[Maik Skoddow · Accepted solution]

Integrating AI tools and agents with ServiceNow offers significant potential for automation, efficiency, and user experience—but also introduces new challenges around stability, governance, and performance. Here are recommended best practices and common pitfalls based on current industry experience.

Best Practices for Integrating AI with ServiceNow

• Align AI Initiatives with Business Goals

  • Clearly define the business objectives you expect AI to solve (e.g., reduce ticket resolution time, improve self-service, enhance operational efficiency).

  • Build a roadmap that ties AI use cases directly to measurable outcomes and value streams (ITSM, CSM, HRSD, etc.).

• Leverage Built-In ServiceNow AI Features First

  • Start with ServiceNow’s native AI capabilities (e.g., Virtual Agent, Predictive Intelligence, Now Assist) before considering external integrations, as these are designed for platform stability and governance.

  • Assess if out-of-the-box features meet your needs or require customization for your unique processes.

• Data Quality and Integration

  • Ensure your ServiceNow instance has clean, structured, and relevant data—AI models are only as good as their training data.

  • Break down data silos by integrating key business systems and establishing a centralized data strategy.

  • Use ServiceNow Integration Hub and secure APIs for connecting external AI services, ensuring real-time, accurate data flow.

• Governance, Security, and Compliance

  • Implement robust governance policies covering data privacy, model management, and auditability.

  • Use built-in monitoring, guardrails, and visibility tools (e.g., Now Assist controls) to track AI adoption, performance, and potential risks such as offensive content or sensitive data leakage.

  • Regularly review compliance with regulatory standards, especially in sensitive industries.

• Input/Output Sanitization and Sensitive Data Handling

  • Sanitize all inputs and outputs when integrating generative AI to prevent malicious content or data leaks.

  • Clearly identify the source of AI-generated content for transparency and accountability.

• Model Management and Customization

  • Avoid over-reliance on default models; train predictive models on your organization’s historical data and customize NLP intents for Virtual Agents to improve accuracy and relevance.

  • Use ServiceNow’s AI Model Management tools for ongoing tuning and monitoring.

• Change Management and User Adoption

  • Invest in user training, feedback loops, and change management to ensure adoption and trust in AI-driven workflows.

  • Maintain human oversight, especially for critical decision points, to avoid over-automation and ensure quality control.

Performance Issues and Unexpected Behavior

• Data Issues: Poor data quality, silos, or inconsistent integrations often lead to inaccurate AI predictions or automation failures.

• Model Bias: AI models trained on biased or incomplete data may produce skewed results, impacting fairness and reliability.

• Overreliance on Automation: Excessive automation without human oversight can result in errors, missed context, or compliance risks.

• Resource Consumption: AI features (especially generative AI) may increase platform resource usage, potentially impacting performance if not properly monitored and scaled.

• Security and Privacy Risks: Integrating external AI services can introduce new attack surfaces and risks of sensitive data exposure if not properly governed.

• User Resistance: Lack of training or transparency can reduce adoption and trust in AI-driven processes.

Summary Table: Key Focus Areas

Area	Best Practice/Consideration
Data Quality	Cleanse, normalize, and integrate data
Governance & Security	Use built-in controls, monitor, and audit AI usage
Model Customization	Train models on your data, customize NLP intents
Integration	Use secure APIs, Integration Hub, and real-time sync
User Adoption	Provide training, feedback loops, and maintain oversight
Performance Monitoring	Track resource usage, latency, and impact on workflows
Compliance	Ensure regulatory adherence and privacy controls

In practice, most ServiceNow admins and architects report success when they start small, focus on high-impact use cases, and iteratively expand AI adoption while maintaining strong data governance and user engagement. Investing in governance and security up front pays off both in stability and in long-term business value.

[Praveen13]

Thanks for Information