What Is Intelligent Automation (IA)?
Intelligent Automation (IA) refers to the application of advanced technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and machine learning (ML) to automate business processes in a way that mimics human-like decision making and reasoning. IA aims to automate tasks and processes not just by following predefined rules but by using advanced algorithms and machine learning models to analyze data and make decisions.
IA combines the efficiency and speed of traditional automation with the cognitive abilities of AI, allowing organizations to achieve new levels of operational efficiency, improved accuracy, and reduced costs.
While there isn’t a standard definition yet for intelligent automation, the term is similar to hyper automation, which is defined by Gartner. You can refer to the below sources to learn more:
- "Intelligent Automation: A New Era of Automation for the Digital Age" by Mark van Rijmenam and Jerry Luftman
- "Intelligent Automation for Testing" by Sarika Hegde
- "Intelligent Automation: Driving Business Transformation" by Ritu Jyoti
- "Intelligent Automation for Supply Chain Management" by Nada R. Sanders
What is Robotic Process Automation?
Robotic process automation (RPA) is a software technology that makes it easy to build, deploy, and manage software robots that emulate the actions of humans interacting with digital systems and software. Robots can do things such as understanding what’s on screen, completing keystrokes, navigating systems, identifying and extracting data, and performing a wide range of defined actions.
What is Artificial Intelligence?
Artificial Intelligence (AI) is human-like intelligence exhibited by the use of multiple machine learning models. AI science is the science of designing computer systems to perform tasks that require human-like intelligence, including Vision, Text, Audio, Tabular, or combinations thereof.
What is Machine Learning?
Machine Learning (ML) is an approach to achieve artificial intelligence utilizing Vision, Text, Audio, and Tabular models, or any combination thereof. Machine Learning includes supervised, unsupervised and reinforcement learning models. ML provides computers with the ability to learn without being programmed. Using a set of algorithms, the computer reviews large sets of data, looks for patterns, and makes predictions that improve with increased exposure to data.
Intelligent automation (IA) combines all of these technologies together to form smarter, and increasingly, more intelligent automation systems.
Intelligent Automation Drivers
Connection to USDA IT Strategic Plan
Our IT strategic plan includes a goal to “Drive innovation in support of USDA mission”, with an objective to “be an innovation incubator” by FY 2025. One of the strategies to achieve this objective is to develop mission-specific use cases for emerging or advanced technologies, such as Edge Computing, AI, and Robotic Process Automation (RPA), which can be leveraged across USDA (USDA IT Strategic Plan (PDF, 18.9 MB), page 11).