We often say that customer success should be part of an organization's DNA. But, what if it was also in the instruments of our daily lives--our phones, computers, and devices? Machine learning--applications that not only respond to inputs without the help of human intervention, but learn, or adapt, from each interaction--is transforming the ways that we communicate with customers, thus allowing us to know customers better and anticipate their needs before even they are aware of those needs. A Gartner report found that 89% of organizations expect that customer experience will be a top priority for maintaining a competitive advantage from 2016 into the foreseeable future. A key aspect of machine learning is already changing the way we do business.

At OpenWorld, Oracle jumped into the artificial intelligence and machine learning space for its customer experience products (aka customer relationship management) and other applications (like human capital management) with an interesting difference -- a huge data store to help educate the algorithms that work for you. We're waiting for products to be delivered this year. Machine learning depends on data about prior situations that the learning algorithms can use to get smart about a situation. Ten examples are good, 100 are better. Generally, the more samples there are the more refined a recommendation can be.

There aren't enough data scientists in the world to go around, so Salesforce is counting on automation and an apps-centric approach to bring its Einstein artificial intelligence capabilities to the masses. At last week's Salesforce Analyst Summit in San Francisco, company executives shared details of the company's two-plus-year effort to build a highly automated data management and machine learning pipeline to deliver predictions and recommendations at scale. The work started with Exact Target predictive customer journeys, and many (though not all) Salesforce AI acquisitions are being plugged into the same automated pipeline. The system can scale, said company executives, because all data collection, data prep, feature selection, model building, hyper-parameter tuning and scoring is handled automatically. Salesforce says it spent more than two years developing an automated data management and machine learning pipeline to drive customer-specific predictions at scale. It's the engine behind Sales Cloud Einstein, Service Cloud Einstein and Marketing Cloud Einstein apps that are either already available or due out this year (see chart below).