Over the last few months, I've had the chance to engage with customers and industry analysts about a range of topics in the field of Artificial Intelligence, and I've been struck by how effective the Sentient Enterprise is in addressing the most common questions and misconceptions about AI. Examining customer case studies is one of the best way to share knowledge and insights around how enterprises are driving business outcomes from AI technology. Case studies are practical, relatable and authentic; and we are fortunate to have some great reference accounts that allow us to publicly share their AI and deep learning success stories. For context, most of our AI case studies start with Rapid Analytic Consulting Engagements (RACE) based on an agile and experimental process to find and test new insights and produce results in weeks, not months. So, the starting point for telling these stories is identifying the business outcome we want to achieve, and then jumping into a range of deep neural net taxonomies, augmenting current platforms with requisite software and GPU enablers, and measuring the final results.