3 reasons why machine learning projects fail - and how to avoid them

There's no denying the competitive edge and the value promise that AI has to offer: confident prediction of future demand, faster analysis and insight generation from vast amounts of data, surfacing inherent business process efficiencies, and more. But when push comes to shove, many AI projects either fail to scale, are put on hold or simply never materialize. Gaurav, our VP of Business Development, spends a lot of his time speaking to senior business leaders about their artificial intelligence dreams and how they can best achieve them. It's his job to ensure the project runs smoothly and the foundations are defined around a value generating future. "Typically a business leader will approach us with a complex decision challenge they want to overcome; maybe it's forecasting demand for their products or raw material, maybe it's engine tuning. We'll then go over that problem with a fine-tooth comb, looking over their data and processes to define what the best AI solution could be. "The most important part of my job is to ensure that the right foundations are in place that help our customers generate long term value.