As AI becomes increasingly embedded in organizational decision processes, the need for effective explanations of algorithmic outputs grows critical. Existing explainable AI (XAI) approaches emphasize technical transparency through visual feature attribution, yet these methods often fail to support meaningful human interpretation. We introduce GenXAI, a conversational explanation layer that translates model outputs into interactive natural-language explanations. Drawing on Cognitive Fit Theory and Construal Level Theory, we argue that explanation effectiveness depends on alignment between linguistic explanation style and the cognitive demands of decision tasks. Across two randomized experiments using a loan default prediction task, we show that conversational GenAI explanations significantly improve users' understanding of algorithmic predictions and decision confidence relative to static XAI visualizations. A second experiment isolates the mechanism by manipulating explanation style: concrete explanations—characterized by numerical specificity, benchmark comparisons, and structured reasoning—produce stronger improvements than abstract narrative explanations. Behavioral telemetry from chatbot interactions reveals that users exposed to concrete explanations process information more efficiently while spending more time deliberating, a pattern consistent with active reasoning rather than passive AI reliance. These findings advance XAI research by demonstrating that explanation effectiveness depends not only on technical transparency but on representational alignment between explanation style and task cognition. More broadly, the study highlights linguistic explanation design as a critical lever in conversational AI systems. For practice, organizations can improve human oversight of AI by complementing static transparency dashboards with conversational explanations that enable users to interrogate model reasoning.