I've got a question for retailers who have a fear of the unknown: in reality, do they really know everything when it comes to their core operations, from stock levels and price points to distribution volumes and promotions? Most decisions are still largely made on gut feeling or by an individual who believes their experience will always outperform a computer, which in today's technological environment is nonsensical. There's a lot of hype surrounding artificial intelligence (AI), and Gartner research states that the number of enterprises employing AI has grown 270% over the past four years; and yet, many companies are still sceptical about what it means or how it could benefit their business. There is a clear purpose, however: retailers can use AI to illuminate areas of the business that were previously shrouded in uncertainty. When unhindered by human interference or pride, AI and Machine Learning (ML) can clear the fog surrounding the unknown, ensuring that all decisions are made using accurate statistics and data.

Artificial Intelligence and Machine Learning are quickly becoming a key priority for innovative Retailers looking to secure a competitive advantage in 2017. But what are they exactly? How mature is the technology? The modern customer expects a consistent Customer Experience and demand their products right here, right now, but retailers are struggling to adapt. Current omnichannel concepts makes replenishment and competitive pricing strategies increasingly complex.

But what exactly is machine learning? And how is it different to what we call artificial intelligence and the term deep learning frequently used today? Artificial intelligence is the general term for a series of methods, tools and technologies that imitate human cognition, including recognition, learning, planning, reasoning and the ability to solve problems. One of these methods is machine learning, aimed at making the computer capable of performing these cognitive skills. Machine learning is based on data and expert knowledge, out of which computers extract significant patterns and transfer these patterns to other data in order to generate predictions and recommendations.