Artificial intelligence (AI) and machine learning (ML) are going to have a huge impact on manufacturing. With these technologies, manufacturers will gain the computational power needed to solve problems that humans can’t possibly solve. They will ultimately be able to provide prescriptive answers to production issues manufacturers have been asking for centuries. Namely, how do we make our product as efficiently as possible, with zero waste and the least amount of downtime.

As with most reports about groundbreaking technology, this discussion of the ‘holy-grail’ is way ahead of industry practices. The vision serves a useful purpose in suggesting what’s possible. But with many manufacturers lacking the data infrastructure necessary to obtain real AI and ML capabilities, the journey towards perfect production can also be so abstract that it confuses the very people looking to achieve it. I’m often asked by corporate leadership, “Where and how do we adopt AI technology?”

Begin with data

While the sci-fi-sounding AI scenarios highlight the technology’s incredible computational power, the practical, effective applications begin with data. Indeed, data is both the most underutilized asset of manufacturers and the foundational element that makes AI so powerful. Think of Maslow’s Hierarchy of Needs, a theory of motivation that is depicted as a pyramid, with the most basic, most important needs at the bottom, and the most complex needs at the top.