Intelligent automation combines robotic process automation (RPA) and artificial intelligence (AI) to create fully automated processes which can adapt over time based on detected change variables. In contrast to classical automation, intelligent automation does not require precise instruction to carry out its tasks. Intelligent automation instead, learns from incoming data and adjusts accordingly. Entire workflows and business processes can be automated in this way.
Rapid development of new intelligent automation workflows and processes is possible through deep learning algorithms during the discovery phase. Artificial intelligence tools first observe how work is done. Deep learning and natural language processing algorithms are used to analyze and then recommend an optimal workflow structure and automation path forward.
When it’s time to begin setting up process automation tasks, unstructured data that includes human communications like chat conversations, video, or audio, is analyzed by artificial intelligence to find patterns in existing processes. AI algorithms are then used to predict productivity gains for process improvements. Intelligent automation improves over time as more data is generated and taken in by artificial intelligence analysis tools.
Intelligent automation can be used to ingest large volumes of data and information for developing complex automation routines for industries that both utilize a large number of human workers and data. Common goals of intelligent automation include freeing human workers from repetitive tasks and decision making processes. Streamlining repetitive manual tasks or processes is one way to ensure the human workforce can focus on other, more business critical tasks not suitable for machine intervention.
Enterprise use of intelligent automation is common, but consumer device makers have also capitalized on its advantages. Consumer applications of intelligent automation include the Roomba robot vacuum which learns the layout of a home and the Nest Learning Thermostat which learns heating and cooling preferences to provide optimal home temperatures. These are just two of the many examples of intelligent automation for the home.
Intelligent automation is already being used in many sectors to make decisions based on sensor data (including sensor data from self-driving cars), governmental applications, inventory management, equipment maintenance, patient medical information, and much more.
How does intelligent automation benefit business operations?
- Reduce labor costs. Intelligent automation can help reduce labor costs by providing more efficient execution of previously manual tasks. Workers can be reassigned to higher-value working tasks.
- Improve quality. In manufacturing, for example, intelligent automation can help speed warehouse inventory maintenance, as well as invoice and payment processing tasks to name a few.
- Limit downtime. Intelligent automation can help determine appropriate predictive maintenance routines, as well as schedule installation and maintenance tasks. A proactive approach to maintenance can help businesses avoid costly downtime.
- Improve patient outcomes. In healthcare, intelligent automation can be applied to help improve patient outcomes by ensuring patients are appropriately scheduled for follow up appointments after surgery and by reducing manual tasks typically associated with pre and post operation processes.