The term'Artificial Intelligence' was originally coined in the year 1955 by John McCarthy, who is known as the Father of Artificial Intelligence. Artificial Intelligence is abbreviated as AI. Although AI was coined in the mid 1950's, many people assume that it is a recent development. Work on AI started way back and continues to be one of the top researched areas even today. AI is predicted to even perform surgeries on its own by the year 2048. So, what exactly is AI? Artificial Intelligence is the effort to simulate human intelligence by the use of machines. Looking at the history of computers and its associated technologies, the initial concept was to make machines that perform a specified work so as to enhance accuracy, increase efficiency and cut down on human-prone errors.

Explainable AI (XAI) research has been booming, but the question "$\textbf{To whom}$ are we making AI explainable?" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered "explained" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.