2017 Guide for Deep Learning Business ApplicationsDeep learning (a common method for developing AI applications) is exceptionally useful for training on very large and often unstructured historical datasets of inputs and outputs. Then, given a new input, predicting the most likely output. A simple intelligence formula — but one which can be applied across almost every function inside a business.

While applicable to an endless number of use-cases, this method follows some general principles in order to be practical and achievable in the near term:

  1. A company must have lots of historical data to train the deep learning algorithm
  2. A company should have a recurring need for predicting things that either:
  • Cut costs: for example reducing average handling time in a customer service conversation; or reducing the need for in-person insurance assessments
  • Create value: like up-selling the right product to the right customer at the right time; or helping marketers create engaging content which will lead to more sales

Between large technology companies, hyper-focused startups, and massive investment into the space, deep learning and artificial intelligence will certainly become the most important driver for transformation of business functions in 2017. Finally, We will hear less “AI announcements”, and more success stories of companies using AI to win in their respective fields


This article originally appeared in forbes.com  To read the full article, click here