The Attribution Gap: Why AI Value Goes Unseen and Unrewarded

Women are already generating measurable AI value, but weak attribution and informal access rules keep that value invisible and unrewarded.

Senior operators in this breakout described a recognition gap, not a capability gap. Women are using AI to drive day-to-day efficiencies, championing early adoption, and building skills through professional networks. Yet their contributions get buried in collective team delivery, so individual impact disappears by review time. Advisors pointed to a structural cause: male-dominated leadership and opaque assignment processes control access to high-visibility AI task forces, and some women choose to stay out of the spotlight. Governance bodies that set AI policy and risk rules were said to be only about 30% women, meaning the people writing the rules do not reflect those affected by them. The group's proposed fixes were concrete and practical. Bake AI use into role descriptions, annual goals, and performance reviews. Name contributors in project charters and postmortems. Maintain an ongoing "AI impact registry" that ties outcomes to individuals. Publish transparent criteria for task force selection and monitor representation targets. Assign executive sponsors to under-recognized AI efforts, and coach staff on claiming credit. For executives, the message is that AI value measurement and talent recognition are the same problem. If you cannot see who drives AI gains, you cannot reward, retain, or scale that talent. The organizations that formalize attribution and widen access will build a deeper, more visible pool of AI-capable people than those relying on informal allocation.