Intelligent applications are developing along two distinct functional use cases:
- Automating simple routine tasks that take time away from more value add activities.
- Provide relevant data to the application user (person or team) that needs it, at the appropriate time and with the proper context.
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
