Business leaders recognize that machine learning and artificial intelligence (AI) will soon be ubiquitous and the basis of competitive advantage in their industry. McKinsey suggests that by 2030 70 per cent of businesses will have adopted at least one form of AI and Gartner predicts that by 2022 90% of corporate strategies will explicitly mention analytics as an essential competency. As a consequence, investment in machine learning and AI technologies has increased rapidly and is predicted to grow even more strongly. Despite these investments, hopes and expectations, many businesses are struggling to see returns from machine learning and AI projects. What lies behind this discrepancy between the promise and the delivery of AI and machine learning? The answer lies in the ability to deploy analytics at speed and scale.