The landscape of enterprise leadership in early 2026 is marked by a paradox that few predicted during the initial excitement of the generative artificial intelligence revolution. On one side, the technological capabilities of large language models and agentic systems have reached levels of sophistication that indicate nearly limitless possibilities for operational efficiency and creative output. On the other hand, a growing body of empirical evidence, including recent longitudinal studies from global research institutions, reveals a harsh reality: most corporate AI initiatives are failing to deliver a measurable return on investment. This issue, increasingly known as the GenAI Divide, highlights the gap between successful experimentation at the individual level and the failure to deliver enterprise-wide value.