"People think AI is magic, that data goes in and answers come out. From accounts to recruitment, drug discovery to financial products, AI implementation offers the promise to business leaders of automated decisions, innovative products and reduced OpEx through efficiency gains. The reality can be somewhat different. So where does the gulf between expectation and reality emerge? A new report from the Oxford Internet Institute (OII) published this month looks closely at why AI projects often fail. The report, AI @ Work, analyses themes in 400 reports about AI from January 2019-May 2020, focusing on how they covered AI in workplaces. The authors say they discovered a significant "evidence gap in how AI tools used and how people talk about what they are supposed to do." As Co-author Professor Gina Neff puts it: "Time and again, we see organisations making the same mistakes in the integration of AI into their decision-making: Over-reliance on the tech, poor integration into the larger data ecosystems, and lack of transparency about how decisions are made… the one takeaway that rings loud today is that AI systems often make binary choices in complex decision environments." As she told Computer Business Review: "As AI moves from the technology sector to more areas of our economy, it is time to take stock critically and comprehensively of its impact on workplaces and workers.