The modern machine learning program, however, needs no such precision. With the increases in computing power paired alongside incredibly massive data sets upon which machine learning relies, programmers can prepare algorithms that allow for a much greater range of capability requiring far fewer coding hours.  Instead of telling the machines what to do in every instance, you need only to program a task or desired outcome and then provide the materials needed for the computer to be able to achieve that outcome.

The potential benefits of strong machine learning models are massive.  Consider how this can work on customer service calls: signals derived through NLP and ML can reveal trends and produce insights that can be used to identify customer disengagement or delinquency risk, call-handling issues and skills gaps among agents with high precision. It enables computers to work in ways that previously only a human could, but at exponentially greater speeds.

These insights can be coupled with other data sources — such as customer transaction history, website and mobile app data, and other digital touch points — to provide a holistic view of the customer journey, identify cross-sell and up-sell opportunities and beyond.  Those materials are data, which is necessary in enormous quantities to build accurate machine learning models; the more data you have, the better the program will be at its job.  Image recognition to natural language processing to text tonal indicators are just a fraction of the techniques possible when committing to a machine learning platform.