As defined by Gartner, hyper-automation “deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyper-automation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)”
It also says that “Hyper automation requires selection of the right tools and technologies for the challenge at hand. Understanding the range of automation mechanisms, how they relate to one another, and how they are combined and coordinated, is a major focus for hyper automation.”
Components of Hyper-automation
Robotic Process Automation (RPA) – RPA helps tackle high-volume, repeatable tasks by using software robots. RPA systems develop an action list by watching a user perform the workflow and then repeating the workflow.
Machine Learning (ML) – ML use statistics to find patterns in large data sets. It can then apply what it has learned to make work-flow decisions. Data extraction, image/voice recognition and clas-sification all benefit from machine learning.
Artificial Intelligence (AI) – AI tools are designed to emulate human decision making. Current AI technology is goal-oriented and narrowly focused on tasks like speech/facial recognition, and extracting or searching for data.
Application Program Interface (API) – APIs create connections between different parts of a system. When used in an automated workflow, APIs can help streamline complex tasks across multiple programs and automate information retrieval.
Solutions supported by hyper automation include
Automated Information Workflows
Data Redaction and Extraction
Reporting & Analytics
Business Process Engineering (BPE)
Benefits of Hyper-automation
Instant and accurate insights
Increased employee satisfaction and motivation
Increased team collaboration
Greater compliance and reduced risk
Educated and productive work force