Gartner New York City CDAO Community Executive Summit 

What was the Summit about? 

Thank you for engaging with us at the Gartner New York City CDAO Community Executive Summit.

At the Summit, data and analytics leaders came together to focus on what’s actually required to make AI work in real organizations—not just in theory. Discussions centered on building durable data foundations, putting the right governance in place, and navigating the expanding scope of the CDAO role as AI moves from experimentation into everyday operations. Through practical, peer-‑led conversations, attendees shared what’s working, what’s hard, and how CDAOs can drive meaningful progress without over‑reaching or over‑promising.  Rocket's well-attended session, Michael Curry grounded these themes in the reality CDAOs are working in now and defined a clear pathway for moving forward. 

From Pilot to Production: The 5 Requirements for Agent-Ready Data

Michael’s session examined how agentic AI is changing the CDAO’s role in moving AI from pilots into production without increasing risk or complexity. The discussion focused on five requirements CDAOs must implement to approve and scale autonomous systems responsibly, grounded in existing operating realities and closely aligned to 2026 CDAO priorities. The session provided a practical framework for evaluating readiness, clarifying ownership, and sequencing progress without increasing risk or complexity. 

Key  takeaways:

• Why organizations that scale AI successfully treat production AI as a data management and governance challenge, not a model problem
• How leading teams are operationalizing trust through measurable standards—such as data freshness, lineage, and validation—before autonomy is approved
• What it means to unify structured data and unstructured content at decision time to provide agents with consistent, governed knowledge
• How shared context, policy‑as‑code, and predictable system behavior enable CDAOs to defend outcomes under executive and regulatory scrutiny
• Where organizations are starting now—focusing on one domain or data product—to make progress toward agent‑ready data without large‑scale disruption 

Key highlights: What CDAOs are prioritizing now 

1. Moving AI from experiments to the enterprise

A consistent message from the conference was that the challenge with AI is no longer proving value in pilots but delivering it reliably at scale. Gartner emphasized that production success hinges on data readiness across complex environments—trust, governance, and repeatability—rather than on models or tools alone. Outlining clear conditions that must be in place before autonomous systems can be approved is a crucial step in enabling reliable AI.  This helps to reframe progress as an execution and readiness challenge rather than continued experimentation.

2. Governance that Accelerates, not restricts

As AI systems gain autonomy, speakers highlighted the need for governance that operates as part of execution rather than as an after‑the‑fact control. The emphasis was on policy‑driven, runtime governance that enables faster access to trusted data while maintaining accountability and oversight. This shift will be driven by a focus on enforceable trust, shared context, and policy as operational requirements, It was clear that this isn’t a single project or a new platform. It’s a pathway. By managing the effort realistically—in steps—CDAOs can demonstrate safely how governance can scale alongside AI without slowing progress.

3. Operationalizing “data as a product”

Many sessions discussed the value of data‑as‑a‑product models to clarify ownership, quality expectations, and accountability as AI initiatives expand. As agents will increasingly rely on these models, stable, governed data assets and clear operational responsibility will be prerequisites for agent‑ready data and, ultimately, data products. Together, these perspectives underscored how product‑oriented data foundations are becoming essential to scaling AI with confidence. 

Know where your data strategy stands and what to do next

IDC identifies six pillars of IT modernization. Complete, trustworthy data underscores all of them. If your data strategy can’t deliver on this, AI and advanced analytics will struggle to deliver value at scale. 

Benchmark your organization against industry peers, identify gaps, and clarify priorities. After you receive your results, Rocket Software experts are available to review them with you and discuss practical next steps to strengthen your data foundation for enterprise AI.