Data Leadership Roundtable: insights on moving from proof of concept to impact

Last week, Rewire welcomed senior data leaders for the second edition of the Data Leadership Roundtable. Held at our Amsterdam office, the event brought together executives from a broad range of organizations – Shell, Rabobank, Heineken, AFM, KPN, Terberg, a.s.r. and many more – for an open exchange on two urgent challenges in data strategy: how to balance investment trade-offs to unlock value, and how to scale AI and data initiatives in environments dominated by legacy systems.

Industry leaders at the table.

The morning began with a plenary perspective from Rewire Partner Helen Rijkes, who reflected on the dramatic evolution of data work—from manual reporting using SPSS over 15 years ago to today's modern data environments, shaped by the shift toward domain-based data ownership and emerging technologies such as GenAI. She emphasized that the challenge isn’t building isolated use cases, but rather scaling them into sustained, organization-wide value. Success, she argued, comes from strategic balancing between consistent capability building and scaling value cases while embedding secure data access policies at scale.

Helen Rijkes, Partner at Rewire, reflecting on the evolution of data management in recent years.

The plenary was followed by a panel discussion featuring Annemarie de Beer (AFM), Gijs Thieme (KPN), Maarten Kramer (a.s.r.), and Sam van Kesteren (Royal Terberg Group). Their conversation explored how organizations balance central and decentralized approaches to data, with many agreeing that centralized data science teams help avoid fragmentation, while local business ownership is critical for adoption. They also examined the ongoing tension between building versus buying solutions, noting that while building is attractive for strategic control and internal talent, buying can accelerate implementation—provided there's clarity on vendor limitations and alignment with business needs. Finally, the panel addressed legacy systems, emphasizing that successful transformation requires not only technical migration but also strong change management and alignment across business and IT.

Annemarie, Gijs, Maarten, and Sam share their perspectives on common data strategy challenges

After the panel, the group split into four breakout discussions, each diving deeper into the practical realities of leading data strategy in complex environments. In one group, participants discussed the evolving role of data scientists, the importance of aligning platform decisions with long-term strategy, and the challenge of ensuring responsible access to both structured and unstructured data. Another group focused on why so many AI initiatives get stuck in the proof-of-concept phase, concluding that without the right infrastructure, skills, and business engagement, even promising pilots can fail to scale. A third group explored how to move from compliance-driven data management to a value-driven model, especially in regulated industries, while also grappling with new ethical questions introduced by AI. And in the fourth group, participants reflected on the importance of embedding AI into day-to-day decision-making, emphasizing that adoption—not just technical readiness—is what ultimately drives success.

Sharing challenges. Shaping solutions. Together.

Throughout the day, one message came through clearly: the real challenge in data and AI transformation isn’t just technical—it's organizational. Success depends on bridging silos, aligning stakeholders, embedding data into business processes, and building the trust and capabilities needed to move from experimentation to impact.

We’re incredibly grateful to all participants for their insights and participation. The conversations reaffirmed the importance of community in tackling the complex challenges when executing your data strategy in the Data & AI landscape of today. We look forward to continuing that dialogue in future roundtables.