This research paper advocates for actionable interpretability as the primary standard for evaluating how effectively we explain deep learning models. The authors argue that current studies often lack real-world impact because they prioritize theoretical understanding over practical utility and concrete decision-making. To bridge this gap, the text introduces a framework and checklist designed to help researchers move beyond exploratory insights toward measurable interventions. By focusing on five key domains—including surgical interventions and alignment—the paper suggests that interpretability can lead to tangible improvements in model safety and performance. Ultimately, the work calls for a shift in academic incentives to reward findings that enable specific actions by developers and policymakers.