For most companies that are interested in using AI, there isn’t a clear model to follow. The approach to building AI used by massive internet companies like Amazon and Google just doesn’t translate — most companies don’t have overflowing troves of data they can use to train models. So, industries such as manufacturing, agriculture, and healthcare need to take a different approach: programming with data, not code. Companies in these industries typically have relatively small data sets, face high costs for customizing a system, and are scared off by long gaps between pilot and product. But, given advances in AI technology, these organizations should shift their focus from building the right model — a software-focused approach — to focusing getting good data, which clearly illustrates the concepts we need the AI to learn, and using new machine learning operations (MLOps) tools. These tools that are geared to help…

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