In 1936, Alan Turing birthed computer science when he presented a hypothetical machine that could compute any problem that could be described by encoded instructions on a paper tape.
Before this breakthrough, computers were people. They spent hours compiling tables, crunching calculations, and postulating probabilities. The first computer could perform the same calculations in seconds, which some say helped turn the tide of World War II.
When we compare this automation to processes today, we still look for tasks that robots and computers simply do better than humans, whether that’s crunching data, testing substances or chemicals, and lifting heavy objects. And by automating these processes and embedding capabilities like business process management, analytics, artificial intelligence (AI), and machine learning (ML), you take things to the next level with what is known as hyperautomation.
An emerging, explosive capability
Many assume hyperautomation is the next major evolution of automation technologies. Think of it as a Turing Machine cranked up to 11. In fact, Fabrizio Biscotti, research vice president at Gartner, said hyperautomation is a “condition of survival” for organizations. Gartner also named hyperautomation the top strategic technology trend of 2021. Needless to say, this is a significant mover and shaker for the future of digital transformation.
Hyperautomation is a methodology that enables organizations to rapidly automate as many processes as possible using robotic process automation (RPA), AI, and other emerging technologies. One of the technologies we use specifically at SAIC to enable hyperautomation is called MetaSift. MetaSift processes and manages customer data, from capture through to archiving, by using AI and ML capabilities, thus accelerating data analysi[node:title]s. This enables companies to get real-time intelligence and analysis of their data. MetaSift is one tool in SAIC’s Hyperautomation domain. My colleague, Jim Tuson leads SAIC’s Hyperautomation domain to utilize RPA, Business Intelligence (BI), Business Process Modeling and Notation (BPMN), and User Experience (UX) methodologies with DevOps teams to rapidly deliver solution to our customers.
It’s common for skeptics of RPA or hyperautomation to assume these technologies will result in scaling back the workforce. There may not be as many number-crunchers as there were back in Turing’s day, but today, there are problem-solvers and decision-makers who use data to move their organizations forward.
Analysts shouldn’t be wasting their valuable time and energy on processing data that a machine can. They should be studying those data outputs to discover trends and nuances that machines can’t identify.