Leaders believe AI is delivering value, yet many organizations still struggle to prove it. The problem may not be a lack of return, but an outdated approach to measuring it. When AI influences productivity, decision-making, learning, and workflows across an open organization, not every benefit can or should be forced into a traditional ROI calculation.

In this session, Kristen B. Hubler challenges the instinct to begin with metrics and makes the case for a different sequence: train first, scale second, and track ROI last. Through real-world examples, she will explore when ROI can be measured clearly, when the pursuit of proof becomes a distraction, and how organizations can identify the outcomes that genuinely matter. Attendees will learn how training, adoption, communication, knowledge-sharing, and employee feedback work together to reveal valuable use cases, spread successful behaviors, and create stronger evidence of impact over time.