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Lessons from industry pioneers building the data foundation for AI agents
Last week, Rewire hosted the third edition of the Data Leadership Roundtables. We gathered a select group of industry pioneers – from retail giants like Albert Heijn and financial leaders like Nexent Bank to biochemical innovators like Corbion – to tackle a pressing question: How do we bridge the gap between fast-moving agentic AI and the scalable rigor of Data Management?
Ontologies talk: coffee is a great way to build relations.
Bridging the gap: context as the universal language
The session opened with a thesis: agentic AI moves fast, but data management scales. The true value lies in the intersection. A shared approach to context and metadata can unify these two worlds. Bridging this gap requires three strategic shifts:
- Investing in new capabilities: Prioritizing Knowledge Graphs and Model Context Protocol (MCP) integration for Data Products.
- Powering AI with trust: Leveraging Data Products to provide AI agents with context built on verified, trusted metadata.
- Democratizing context: Distributing semantics across use cases and domains to ensure AI agents speak the same "business language."
The CDO perspective: data ownership at the source
A highlight of the event was a fireside Q&A with Gabriela Filip (ex-CDO at Knab). Gabriela offered a masterclass in balancing speed with integrity. Her advice: Data ownership must live where the decisions happen.
Key takeaways from her perspective included:
- Local flexibility, global alignment: Keep operational definitions flexible at the local level. Mandate "strong alignment" only for critical, cross-domain business decisions.
- The "Knowledge-First" approach: Combine business and data expertise to effectively capture knowledge, supported by roles like Information Architects and coordinated by leadership (e.g., CDO).
Gabriela Filip and Freek van Gulden sparring over data strategies
Insights from data practitioners: the three recipes for success
Following the plenary, participants broke into deep-dive roundtables covering data governance, operational ownership, and organizational change. Here are some of the "recipes" that emerged:
- Start small, deliver value first: The path to structured governance and knowledge starts by linking data governance to business value. Start with a specific use case, let governance evolve around it, and use visible wins to reduce resistance, drive adoption, and scale.
- Flexibility over rigid architecture: With AI evolving rapidly, the only certainty is that rigid "end-state" architectures will become obsolete before they are even finished. Focus instead on decoupled, modular setups. (For deeper insights, check out this blog post.)
- Connecting defensive with offensive strategies: exploit defensive data management initiatives – like fixing metadata, glossaries – to comply with regulatory compliance. Then use this as an opportunity to create a scalable knowledge foundation that provides a fly-wheel for offensive value creation.
Participants sharing the challenges and lessons learned.
The dialogue doesn't end here
The day’s discussions reaffirmed that navigating today’s Data & AI landscape isn’t just about tools - it’s also a bit about community: Together, we’re better equipped to tackle the complexities and turn challenges into opportunities.
Until the next Roundtable!
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