CDAO responsibilities are evolving: why AI strategy now starts at the top (www.techradar.com)

AI has moved from experimental moonshot to operational mandate, and the Chief Data & Analytics Officer (CDAO) is becoming a central executive role responsible for turning AI promise into measurable business outcomes. Adoption stats are striking: 73.7% of organizations now have a formal CDO/CDAO (vs. 12% a decade ago), and Gartner expects 75% of organizations to operationalize AI by 2026 (up from 10% in 2020). With predictions that underperforming CDAOs will be pushed out if they can’t show AI’s positive impact, the role has expanded beyond data hygiene to owning strategy, execution, governance, and culture. Technically, CDAOs must build the connective infrastructure that makes generative AI useful—integrating structured and unstructured data, encoding institutional knowledge into models, and orchestrating APIs, data layers and workflows so agentic systems can reason and act responsibly. Modern mandates include “governance as code” for auditable, scalable controls, adopting flexible stacks and no‑code platforms to accelerate experimentation, and measuring impact with business‑aligned KPIs (time‑to‑decision, hours saved, explainability, data literacy, and volume of governed AI processes). The implication for AI/ML teams: success now depends as much on productizing models, embedding them into operations, and earning user trust as on model accuracy.