Many organizations deploy AI across teams with the expectation that value will emerge over time. While experimentation increases activity, it does not always translate into measurable business impact. Leaders struggle to distinguish between promising initiatives and AI usage that delivers little return.Without a clear value framework, AI investments remain difficult to prioritize or defend.

AI usage data lives in AI tools, while productivity, quality, and financial outcomes are tracked elsewhere. Teams may report success anecdotally, but leaders lack consistent evidence of impact. As a result, decisions about scaling or stopping AI initiatives are often based on perception rather than performance.This disconnect makes value realization inconsistent and hard to manage.