Intelligent Applications

The underlying components that make up AI today: machine learning, natural language processing (NLP) and deep learning—have existed in an evolving form for many years. Over the past year or so, software application vendors started experimenting with embedding AI into business software applications. This approach of embedding AI capabilities into the workflow of the applications (better referred to as “intelligent applications”) will continue to grow in 2017 and beyond.

Intelligent applications are developing along two distinct functional use cases:

1. Automating simple routine tasks that take time away from more value add activities.
2. Provide relevant data to the application user (person or team) that needs it, at the appropriate time and with the proper context.

Both of these approaches take advantage of the rapidly growing mass of structured and unstructured data that can be accessed and used by companies to make better business decisions. These use cases open up opportunities to improve productivity and decision accuracy, as well as employee and customer experience.

The first function, automating simple, routine tasks, is straightforward and relieves users from tasks that distract and consume time, allowing them to focus on higher-value tasks. An example would include virtual assistants that manage schedules, providing the capability to coordinate meetings without user intervention. An AI agent could also perform tasks that require coordination of available data into an output, like a project plan, a resupply order or even a bill of materials.

The second use case, providing decision support to users that need to evaluate large data sets, has many applications across a business operation. Perhaps the simplest example is in medical diagnosis. An intelligent app doesn’t make the diagnosis, but it can sort through massive data sets to look for patterns, potential diseases and treatments. It can manage electronic health records, test data, patient and family history, and genetic information: ordering and contextualizing relevant data to make the physician’s job more manageable.  SOURCE:  Learning Hub