Why the future of machine learning will be crunching words

In recent years, enterprise machine learning has revolved around crunching numbers: analyzing datasets or tracking customer behavior. But what organizations will soon realize is that applying machine learning to content--physical documents, images, presentations and even conversational UIs--removes the cap on who machine learning impacts, and how far its value extends across the enterprise. Tracking down lost documents and images, or drafting abstracts and case studies only to realize they've already been written are just a few of the daily frustrations that we typically consider unavoidable. But as it turns out, it's these issues specifically--content discovery, tagging and classification--where machine learning is in a strategic position to make a substantial impact. Numbers-driven algorithms have informed strategy for years now, but applying machine learning to content will likely have a similar, if not greater, impact on the enterprise.