Hyperautomation: The Power of Blending AI, Blockchain, and RPA

Industry InsightsJuly 30, 2024

Yes, you’ve probably heard about automation and how automating processes can positively impact a company’s workflows, and even increase revenue by high percentages. But what about hyperautomation? Lately, this term has been thrown a lot across the internet, with promises of high revenue increases and ease of processes. Keep scrolling and find out what hyperautomation means and how businesses across different industries can benefit from it.

What is Hyperautomation?
“Automation” means the process of automatizing something; “hyperautomation” is the same but with different, recently developed tools, such as Artificial Intelligence (AI), blockchain, and Robotic Process Automation (RPA). Companies have been automating processes and workflows for decades now — at first, through time-consuming processes where the staff was key. After that, with big data and other techniques — but hyperautomation takes all this to the next level.

A Markets and Markets report predicts that the hyperautomation market is set to increase from 9.2 billion USD in 2022 to 26 billion USD in 2027, growing at an annual rate of 23%. The same document states that most of this growth will happen thanks to the technological advancements in China, Japan, and India.

Logistics

This is probably the most important area of supply chain management. Logistics staff and processes are responsible for ensuring that the supply and demand ratio is being met, that the goods are being sent and delivered to customers, and so on. Having someone — in this case, something — not only ease these processes through automatization but even finding new, better ones, would be amazing. That’s where hyperautomation comes in: it can increase productivity and customer experience, and help reduce costs. According to a PreScouter article from last year, the logistics sector is expected to “fully adopt” hyperautomation within three years.

Basically, most of the tiresome and time-consuming tasks in a company’s logistics department can be enhanced with these new techniques. A meat processing company can use artificial intelligence and NLP (Natural Language Processing) to gather useful information about consumer trends and come up with solutions focused on increasing efficiency, and meeting the consumers’ needs. Blockchain can then be used to set certain tasks automatically, therefore decreasing the number of steps needed to finish said tasks.

The traditional job of a company’s claims officer entails collecting data from customers, cataloging it, analyzing the claims one by one, determining a course of action, and making a decision. Using hyperautomation could allow this employer to focus on other things. How? Digitally received claims could be analyzed by trained chatbots via NLP, which would merge with AI to find proper ways to reply and solutions to the customers’ issues. Human intervention would only be needed to set up the process. After that, everything would be automatized and autonomous, allowing employees to focus on other things.

Logistics isn’t exclusive to supermarkets or meat processing companies; it’s essential to many industries, from healthcare to the automotive sector, which means hyperautomation can have a major impact a bit everywhere. Inter Aduaneira is an interesting case study. The Brazilian international trade consultancy was able to save around 800 hours per analyst. Read the article about it here.

Finance

Finance is an important and relevant area for every business and industry as well. Who wouldn’t want finance processes to be bettered and more functional? Hyperautomation can help CFOs and CEOs achieve that. Tasks like closing books and running models can be done by machines. Setting things to happen automatically and autonomously saves time, and money, and decreases the margin for (human) error. Using blockchain and AI to automatize processes, enables leaders to better allocate their staff’s time and expertise but also helps avoid future mistakes by setting processes to run with algorithms.

Gartner says the cost of finance will drop 40% in the coming years: “Autonomous is really that predictive and prescriptive nature where decisions are being made based on a constant stream of data — not just you picking up some technology to save time and make something more efficient”, said Alexander Bant, the company’s Chief of Research for Finance. Bant believes mindset change plays a big role in accepting and even embracing these new tools — he argues organizations will have to “rethink the way work is done”.

And Beyond

Logistics and finance are vital areas for almost every business, so it might be fair to say that hyperautomation can — and will — be useful for several industries, from healthcare (pharmaceutical companies and pharmacies can establish automatized processes so chronic patients — whose medical histories are stored in the blockchain — receive their prescriptions at home) to automotive, energy to banking, telecommunications, IT, manufacturing, insurance, legal, and so on.

Challenges in Hyperautomation Adoption
Cybersecurity — Hyperautomation requires digitizing countless processes, and storing them somewhere — usually shared servers or an online cloud. If data isn’t properly safeguarded, it can be exposed to potential cyber-attacks.

Legacy systems — Some systems currently in use might not always be compatible with newer ones, therefore creating the need to adapt, and find ways to transport all the data and necessary software to the future systems. It might be time-consuming and costly to implement.

Privacy — Technology that fosters automation runs on data, which in turn has to be harvested from the real world. This means using people’s — customers, workers, etc — personal information. Privacy issues can arise and should be addressed. Companies should have strict compliance policies to adhere to these new technologies and correctly implement hyperautomation.

Error & Bias — Machine learning systems and AI go a long way when it comes to hard data (quantitative) like analyzing numbers, developing useful algorithms, and crafting reports, but they can also be wrong when it comes to soft (qualitative) data. Chatbots can retrieve information from a chat room or a complaints page on a website, analyze, and provide you with the number of the same complaints, and other useful data, but they might not be able to find different solutions for issues that might sound the same, or even contextualize. They’ll need humans to help with that, so supervision, audits, and detection of errors are paramount to make this work.

In conclusion, hyperautomation can — and will probably — become a leading trend across several industries, especially with the rising use of blockchain, AI, and other technologies. Companies and brand should be aware of the risks, but also start preparing for implementation as a strategy to be ahead of their competitors and not left behind.

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