Greater access to AI development tools has kicked off a race to automate all the things. The resulting big gains in cost reduction are in turn driving an increase in new business models with more innovation for the near future.. However, organizations and enterprises looking to meet more than just business goals in the long-term, can aim for bigger returns by deploying solutions that are economical, kind and considerate to humans. Take a common application of machine learning technology: the recommender system. They are arguably more successful from a business standpoint and also very ubiquitous. With top of the line algorithms, query languages, filtering methods, rating matrices, and correlation computations baked in them to automate recommendations, thesey are common AI investment. Yet if the resulting customer experience doesn’t go beyond a transaction of data, there are huge opportunities missed for both customer and enterprise.

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