{"id":13127,"date":"2020-01-06T07:45:59","date_gmt":"2020-01-06T13:45:59","guid":{"rendered":"http:\/\/taxprocenter.proconnect.intuit.com\/?p=13127"},"modified":"2020-01-06T07:55:47","modified_gmt":"2020-01-06T13:55:47","slug":"how-intuit-uses-ai-to-power-personalized-education-for-accountants","status":"publish","type":"post","link":"https:\/\/accountants.intuit.com\/taxprocenter\/practice-management\/how-intuit-uses-ai-to-power-personalized-education-for-accountants\/","title":{"rendered":"How Intuit Uses AI and Machine Learning Technology to Power Educational Experiences for Accountants"},"content":{"rendered":"

At Intuit, our mission is powering prosperity around the world. One of our strategies for achieving this is around delivering personalized experiences. Our educational programs are one of the ways we can deliver on that strategy.<\/p>\n

\"Intuit<\/p>\n

\"Intuit<\/p>\n

Our Intuit ProConnect\u2122 Lacerte<\/a>\u00ae and ProSeries<\/a>\u00ae customers can enjoy our online training portal within \u201cMy Account,\u201d where they can access nearly 100 different pieces of content, from white papers to bite-sized video content less than 10 minutes long on average.\u00a0We have had more than 30,000 users access these portals and take advantage of the valuable training contained within.<\/p>\n

Machine learning technology allows personalization within this experience. The portal currently will suggest relevant content to users based on a module that they take. While users engage in their suggested content about 15 percent of the time, we feel like the level of personalization to an individual user could be much stronger. The way we can get there is through the continued implementation and evolution of AI and machine learning models.<\/p>\n

The interesting thing about using AI to drive personalized experiences is that it is not a single algorithm or set of code; it leverages a series of models that create a holistic set of criteria by which to feed an engine that suggests the right content to the right person in the right way. It can also continue to be enhanced (or enhance itself) as time goes on, without the need for additional implementation. Simply put, it just gets better over time as it continues to get more data that feeds the precision of the recommendation engine.<\/p>\n

In our case, we will use four models to drive the recommendation engine. Here is an overview of how they will work:<\/p>\n