Mega. Super. Ultra. I love the effect of superlative prefixes to maximize the meaning of words.
In process engineering, hyper-automation comes from the idea of extending and intensifying the automation of tasks with the help of the most innovative technologies and methodologies, such as Artificial Intelligence, Automatic Process Robotization and others, all with the aim of automating complete processes in a scalable and adaptable way.
It sounds super interesting – and it is.
Hyper-automation has always been considered an aspirational goal, due to the complexity of business processes and the rigidity of traditional technologies. However, generative AI opens up a new way to automate complex tasks that until now were either very costly or practically impossible to digitize.
“The ability of generative AI to emulate human cognitive tasks to analyze and draw relevant conclusions from multiple media, such as emails, documents, images, voice memos and even videos, is mind-boggling.”
Generative AI can understand the context of the business and the specific process, draw on reference materials specific to the business, use previous cases as examples for new answers, and execute tasks automatically with incredible accuracy and speed. I’m not just talking about automating what we do now manually, but doing it better, reinventing processes from the ground up.
Let’s take a practical case: the supplier invoice approval process.
Rather than simply automating the approval flow, generative AI can go much further. Imagine a hyper-automated system that receives invoices in different formats (PDF, images, emails), analyzes them and automatically extracts relevant data such as supplier name, order identifiers, amounts and dates.
That data is then structured and incorporated directly into the company’s accounting software, eliminating the need to enter all that information manually and freeing up people’s time to spend on more valuable tasks.
For those of us involved in process digitization, the opportunity that generative AI represents to achieve hyper-automation is obvious but, as usual with technologies that are so disruptive, it is difficult to identify the right use cases for our business and that generate a real impact on the company’s processes.
Achieving this requires a combination of deep business knowledge and technology capabilities, something that only ambitious companies can achieve with long-standing, strategic technology partners.
The benefit will be for those companies that, understanding which of their current processes are most important in generating business, are able to improve and optimize them through hyper-automation.
And the jackpot and the real competitive advantage will go to those companies that build new hyper-automated business processes from the ground up by maximizing the capabilities of generative AI to drive current lines of business and even create new ones that are more profitable.
Will your company be the first to get it?