Data Management for the Agentic Era

As enterprise AI evolves from passive assistants to autonomous agents, organizations are rethinking the very foundations of their data environments. The Chicago CDAO Community recently convened for a Town Hall to explore this pivotal transition – from traditional, human-centric dashboards to machine-readable, context-rich data ecosystems designed for agentic AI.

This gathering brought together data and analytics leaders to discuss the challenges and opportunities of building “agent-ready” environments, shifting from data engineering to context engineering, and deploying role-based agentic AI for end-to-end workflows. The conversation also touched on new metrics for productivity, ROI, and risk mitigation in this emerging landscape.

The Town Hall was moderated by Dapo Adeshiyan, Global Practice Lead, AI at Google Cloud. Discussion leaders included Sid Raina, VP & Global Head of Artificial Intelligence and Data Analytics at Medline; Yuan Luo, Chief AI Officer at Northwestern University; Karthik Josyula, Head of Data and AI platforms at Kohler Co.; Ravi Vemuri, Global Leader, Planning, SIOP & MFG Ops, Technology Delivery and Strategy at Carrier; and Sayeed Shariff, Head of Data, AI and Digital Platforms at Hollister Incorporated US – who facilitated small group discussions.

Here, we are sharing five key insights from the Town Hall that are shaping the future of agentic AI in the enterprise.
 

Key Takeaways:

1. Early Stages of Agentic AI Adoption: Many CDAOs acknowledged that their organizations are in the early stages of agentic AI maturity, with some describing the current environment as “agents running wild” or “premature.” While there is widespread skepticism about agentic AI fully replacing traditional systems in the near term, leaders are actively preparing their foundations for broader adoption of agentic AI in the coming years.
2. Evolving AI Strategy and Data Architecture: CDAOs are navigating the complexities of integrating agentic AI into their data strategies, including the choice between third-party solutions and in-house development. One CDAO highlighted efforts to consolidate various offerings into a cohesive AI strategy, emphasizing the importance of contextual data and semantic layers.
3. Managing Unstructured Data for AI Models: There was consensus that unstructured data remains a significant challenge. One CDAO shared that their organization is leveraging advanced techniques, such as graph-based Retrieval-Augmented Generation (RAG), to better utilize unstructured data in agentic AI systems. Addressing this challenge is a top priority, especially as organizations seek to deploy specialized AI models tailored to specific tasks, rather than relying solely on general-purpose large language models (LLMs).
4. Continued Reliance on Dashboards: Executives discussed the ongoing importance of dashboards for organizational data visibility. Despite the push toward more agentic, autonomous AI systems, dashboards are expected to remain central, as they provide a shared perspective across the organization. As one CDAO noted, “I personally believe humans leveraging dashboards is not going anywhere. It has a place in a world, even where machines are leveraging data.”
5. Shifting Data Governance Frameworks for Agentic AI: As CDAOs move from traditional visualization and reporting to enabling machine consumption and autonomous action, the focus is on delivering high-quality, well-governed data. One executive described the shift from “strict decision control to designing boundaries within which agents can operate autonomously.” Another CDAO emphasized the importance of policy, lineage, and metadata management in evolving data governance frameworks, as this enables AI agents to act within trusted environments while maintaining essential human oversight to ensure trust and governance.

Overall, agentic AI is an emerging field with great promise, but organizations are still in the early stages of adoption. Dashboards and human oversight remain essential, and the focus is on evolving governance, data quality, and AI strategy to enable more autonomous, machine-driven insights in the future.

To collaborate with your CDAO peers on strategies for implementing agentic AI and other critical D&A priorities, apply to join your regional CDAO community. If you are already a community member, sign in to find upcoming opportunities to get together with your peers.