Why Machine Learning Projects Fail

Start typing'artificial intelligence will change' into a search engine and you will see suggested sentence endings like'the world', 'everything in your lifetime' and'the face of business in the next decade.' Search a little further and it will become clear that AI and machine learning projects are not only driving advancements, but are integral to their success. According to research from Accenture, 85% of executives in capital-intensive industries say they won't achieve their growth objectives unless they scale AI. At the same time, research from MIT Sloan suggests that the gap between organizations successfully gaining value from data science and those struggling to do so is widening. As we know, data science and machine learning are the engine behind AI applications, as it is through processing data that AI learns how to interpret our world and respond as we want it to. If AI is to make a real impact on companies and their customers, companies need a new approach to machine learning.