Data Governance is critical for a successful Agentic AI adoption

[Sujit Agrawal]

Data Governance is a key part of ServiceNow technical governance. It defines the standards and practices for owning and managing data assets created, stored, and processed on the Now Platform.

Agentic AI in ServiceNow refers to advanced artificial intelligence capabilities that act as “agents”—autonomous digital assistants or bots—able to perform tasks, make decisions, and interact with users or systems on behalf of humans. Unlike simple automation or rule-based bots, agentic AI leverages generative models, reasoning, and contextual understanding to Interpret complex requests, take multi-step actions, learn and adapt based on feedback and historical data.

Why is Data Governance critical for a successful Agentic AI adoption -

1. AI agents depend on accurate, up-to-date data to make decisions (e.g., which assets are assigned to which users, license compliance status). Poor data quality leads to incorrect actions, user frustration, and potential compliance risks.

2. AI agents often access sensitive data (HR records, financial info, asset locations).

Strong data governance ensures only authorized access, proper encryption, and audit trails—critical for regulatory compliance (GDPR, HIPAA, etc.).

3. Agentic AI needs well-structured data models (like CSDM) to understand relationships (e.g., which software is installed on which hardware, who owns which assets). Poorly governed taxonomies can confuse AI agents, leading to errors or incomplete actions.

4. Governance provides traceability for AI-driven actions—who initiated, what data was used, what decisions were made. This is essential for troubleshooting, compliance, and continuous improvement. It also helps mitigate bias in AI decisions, ensuring fair and equitable treatment across users and scenarios.

Imagine a ServiceNow agentic AI tasked with automating hardware refreshes. If asset data is outdated or access controls are not in place, the AI might order unnecessary replacements, miss critical assets, or expose sensitive information. With strong data governance, the AI can reliably identify which assets need refreshing, ensure proper approvals, and maintain a secure audit trail. Agentic AI’s effectiveness and trustworthiness are tightly linked to the quality, security, and structure of the data it accesses.

Conclusion

A robust data governance framework is the backbone for reliable, secure, and effective ServiceNow agentic AI. It ensures AI agents act on trustworthy data, respect privacy and compliance, and provide traceable, auditable outcomes. Organizations must invest in data governance frameworks—defining ownership, quality standards, access controls, data architecture, and data lifecycle management.