For a decade, the Chief Data Officer had to argue for relevance. AI has ended that argument. The data function is not being displaced by AI, it is being elevated by it, and it now has the seniority and the budget to act on that shift. But the change has not taken the same shape everywhere, and a wider remit does not always mean ownership. In some organizations, an even more interesting development is taking shape: should the CDO role also incorporate AI strategy, or are these two distinct mandates with two different focuses? In this study, we examine both sides of that question, and show an example from the field of how one Dutch organization has chosen to answer it.
A role that is becoming more strategic
Eraneos surveyed 100 senior data and AI leaders in the Netherlands to study how AI is changing their role. The data shows these roles now carry real seniority and real responsibility. Most respondents report directly to the CEO or the board, and act as the primary decision-makers for data and AI. But a wider remit does not always mean ownership. In some organizations, that mandate went to a new seat instead of a widened CDO role. The study shows CDOs now face new priorities and a new set of challenges. That also creates new opportunities for those ready to make the shift.
"There’s no reward for the best data foundation, however important it is. The next eighteen months are about more than architecture. The CDO who takes ownership of integrating AI products into the business is the one who will lead the change."
Important lessons for the CDO in the AI era
Four important lessons come out of our research:
- Treat data trust as an operational control, not a hygiene metric. Trust in data is the most-cited barrier in the study, and it gets harder to rebuild the longer it’s treated as a monthly report metric instead of a gate. Set quality thresholds that block deployment, and make lineage and metadata complete enough to answer “where did this decision come from” after the fact.
- Specify the delegation. CEO ownership of AI strategy does not automatically translate into clarity at the operational level: 69% want clearer ownership and accountability, and 74% want AI governance formalized. Write a one-page decision-rights map that states who approves a high-risk use case, who can stop one, and what the escalation path is.
- Stress-test your own optimism. Every figure in the study is a senior leader’s self-assessment, and C-level executives rate their organization roughly 2.3 times more positively than specialists and frontline staff on guidance, accountability, and trust in AI. Measure trust in AI at the front line, not deployment as reported from the top, since deployment is a number leadership can produce alone but trust is not.
- Own the ground between the foundation and real adoption. Proving business value from data and AI is the most-cited priority in the study, yet scaling AI adoption is a top-three priority for 61% of AI leaders versus only 21% of data leaders. Build a single roadmap that sequences architecture and adoption milestones together, with one person accountable for the handover, so the answer to “what has AI delivered” is a number rather than an anecdote.
Download the report to see how data and AI leaders across the Netherlands are redefining the CDO role.
