This project examines the gap between rising AI activity and measurable business output.

The research shows significant gains in AI-assisted activity across software development, marketing, customer support, and knowledge work. But increased activity does not always translate proportionally into finished output. For example, autonomous coding activity can rise dramatically while actual software releases increase at a much slower rate.

The dashboard was designed to make that gap visible and show where the real operational challenge begins: review, testing, deployment, system integration, governance, and human decision-making.

The central takeaway is simple: AI productivity should not be measured by how much activity it generates, but by how much finished value reaches the business.