This is a shorter version of the full article. The full learnings can be found on the members only DataIQ Hub.
Several themes emerged about how the CDO role is evolving as AI becomes embedded in enterprise strategy. The 2026 DataIQ 100 Europe Number One Laia Collazos, Chief Data and Analytics Officer at Coca-Cola Europacific Partners; Wade Munsie, Chief Data Officer at Heathrow Airport (2026’s DataIQ 100 Number Nine); and Ryan Den Rooijen, MD AI and Monetisation at Currys (listed in the 2026 DataIQ 100), reflected on how the role is shifting as AI becomes embedded in enterprise strategy.
Their discussion did not land on a single definition of the modern CDO but instead pointed to a role that is expanding in multiple directions at once. Today’s CDOs (and equivalent) are responsible for technology as well as governance, organisational learning, and shaping how AI is understood at the executive level.
Many of the insights underscore findings in the latest DataIQ report: The End of AI Theatrics.
Key Insights
- The CDO is moving from builder to orchestrator
- AI has forced CDOs into the role of translator
- Ownership of AI is becoming distributed
- The real challenge is organisational capability
- Credibility still depends on staying close to the technology
From delivery to orchestration
Early iterations of the role were usually grounded in technical delivery. Many CDOs were tasked with building data platforms, establishing governance frameworks and standing up analytics capabilities.
That foundation remains essential, but the centre of gravity has moved. The modern CDO is increasingly responsible for coordinating an ecosystem rather than delivering every component of it.
Internal data teams, software engineers, external vendors, compliance functions, and business units are now all part of the equation for CDOs. Alongside the familiar mandate to create value from data sits a growing expectation around AI oversight, particularly as regulatory and safety considerations become core discussion points at the highest level of organisation leadership.
Board discussions reflect the same shift. Presentations about AI increasingly sit at the intersection of opportunity and risk covering commercial potential, governance, security, and regulatory exposure.
Translating AI for the boardroom
Growing executive attention introduces a new responsibility around shaping how data and AI are understood at senior levels. Technical sophistication alone is rarely enough to influence board conversations and the panellists emphasised the importance of translating complex ideas into narratives that connect with business priorities.
Analogies and stories often carry more weight than model performance metrics and here lies the key to translation. Framing AI initiatives around revenue growth, operational resilience, or risk management makes them more legible to leaders whose focus sits elsewhere.
The difference now is speed. AI is evolving at a pace that compresses decision cycles, forcing organisations to engage with emerging technology before it is fully understood.
That uncertainty means part of the CDO’s role is educational. Some organisations are taking executives on learning journeys by visiting technology companies, running internal demonstrations, or creating forums where leaders can explore emerging tools together. The aim is to build shared understanding as the technology evolves.
The discussion echoed findings in the latest DataIQ report, where translation between technical capability and executive decision-making emerged as a recurring leadership challenge.
AI ownership is spreading across the business
As AI becomes embedded in operational processes, questions about ownership become more complex. Few organisations believe that one executive can realistically control every AI application. Instead, responsibility is becoming distributed across business units and functions.
Several governance models are emerging. One approach is assigning named “AI owners” to individual applications. These individuals are accountable for ongoing performance, ensuring models remain accurate and prompts are appropriately managed.
At the same time, cross-functional decision forums are starting to shape how AI investments are prioritised. One solution is designing an internal mechanism that resembles venture capital, where senior leaders from across the organisation collectively evaluate potential AI initiatives.
Credibility through practice
Despite the increasing strategic scope of the role, it is clear that credibility still depends on maintaining a practical understanding of the technology itself.
AI is evolving too quickly for leaders to rely on second-hand expertise. Experimentation by building prototypes, testing new tools, or deploying small applications is part of the job.
AI-assisted development environments, coding tools, and emerging platforms are lowering the barrier to hands-on experimentation. For CDOs, engaging directly with these tools offers a way to stay grounded in how the technology is developing.
A role still defined by context
The CDO role continues to vary widely between different organisations. In some companies it leans towards strategic vision and ethical oversight, while others have it tightly linked to operational delivery and financial outcomes. Much depends on organisational culture, leadership expectations, and the maturity of the data environment.
What is changing, however, is the level of executive attention the role receives. As AI moves closer to the centre of business strategy, the CDO increasingly sits at the intersection of opportunity, risk and organisational transformation.
The challenge is coordinating the conditions in which the systems being built succeed by aligning technology, governance, and people.
This is a shorter version of the full article. The full learnings can be found on the members only DataIQ Hub.
