CDAOs Must Prove GenAI Value to Keep Executive Role

Stakes High for Data Leaders in AI Era

Gartner predicts that by 2027, 75% of CDAOs not seen as essential to their organization's AI success will lose their C-level position. The warning came as data leaders gathered at the Gartner CDAO Summit in New York last week, where AI fatigue was palpable yet the urgency for strategic action was clear. While CDAOs have spent 10 to 15 years building predictive models and machine learning systems, the stakes for GenAI leadership are fundamentally different. The opportunity for data chiefs is substantial. According to the HBR article Why Your Company Needs a Chief Data, Analytics, and AI Officer, 53% of organizations are appointing a C-level officer for AI (many by expanding the CDAO role), and 93% say AI is leading to a greater focus and investment in data. More CDAOs are gaining CEO access, with CDAOs reporting to the CEO increased to 36% in 2025, up from 21% in 2024.

Three Critical Execution Strategies

Anaconda's analysis identifies three key approaches for CDAOs to secure their strategic position. First, organizations must make data AI-ready by providing models with both structured and unstructured sources containing business processes and institutional knowledge. Second, security and governance frameworks need evolution to combat shadow AI while enabling legitimate innovation. Third, strategic build-versus-buy decisions should favor commercial solutions for standard use cases while investing in custom builds for competitive differentiation.

Organizations with AI-ready data and analytics foundations see a 20% improvement in AI-related business outcomes compared to organizations that don't. Yet most organizations remain stuck in pilot mode. Research indicates that most companies are still piloting their GenAI initiatives, and most of those initiatives are failing to provide ROI.

The challenges holding back GenAI adoption (governance, security, and complex change management) are precisely the problems CDAOs have been solving for years. According to Anaconda's 8th annual State of Data Science report, 92% of respondents are using open source AI tools and models, while 76% indicate that open source will become an even higher priority in the next year, reflecting the industry's preference for flexible, adaptable solutions over expensive commercial offerings that quickly become obsolete.