Artificial Intelligence and Machine Learning (AI/ML) reign high on the Gartner hype cycle, promising new business models, and capturing the imagination of executives well outside the digital industries of Facebook, Google and Uber. However, despite dozens of POC studies, in demand planning AI/ML is struggling to prove significant increases in accuracy. A limiting factor is that the underlying data is fundamentally different. After a brief introduction into AI/ML in forecasting we will explore the differences in data between image recognition and demand planning, where the dataset size, structure and labels are often sparse. Companies are not drowning in data, but rather sitting in a puddle! As this limits AI/ML algorithms, we present a case study at one of the worlds leading pharmaceutical manufacturers Janssen (a Johnson & Johnson company) where enhanced datasets with increased sample frequency and automatic feature generation promise break-through increases in accuracy by using AI/ML.