CDAOs empower AI agents with governance and unstructured data

Explore how CDAOs are tackling AI governance, enabling AI agents, and addressing ROI and cost pressures.

September 2025

The rise of AI agents is reshaping the role of the Chief Data and Analytics Officer (CDAO), presenting both opportunities and challenges. CDAOs must now focus on governing data for AI use, especially unstructured data, while balancing innovation with regulatory compliance and cost pressures. To navigate this agentic era, CDAOs need to adopt advanced strategies for data management, integration, and governance. They must also emphasize data lineage and demonstrate clear links between data initiatives and business results.

Some key factors and considerations for CDAOs are:

• AI Governance: CDAOs must establish robust frameworks for governing data used by AI, addressing data sharing hurdles and ensuring compliance with evolving regulations.
• Enabling Agents: With unstructured data being critical for AI agents, CDAOs need to develop strategies for collecting, cleaning, and normalizing this data to unlock its full potential.
• ROI and Cost Pressures: CDAOs should focus on demonstrating the return on investment (ROI) of AI initiatives, especially those involving AI agents, by tying metrics to tangible business outcomes.
• Data Lineage: CDAOs should enhance their focus on data lineage, ensuring transparency and traceability throughout the data lifecycle to build trust and facilitate effective governance.
• Business Outcomes: CDAOs must align data strategies with clear business drivers, ensuring that data initiatives directly contribute to achieving strategic goals and delivering measurable value.