Robotic process automation (RPA): from routine to revolutionary

Kunal Verma, Co-Founder and CTO at AppZen, discusses how robotic process automation (RPA) can evolve to improve the workplace

There are still many options when it comes to RPA technology.

Robotic Process Automation (RPA) often conjures up images of robots assembling cars, or even droids sitting next to Bob in accounts payable. But in reality, RPA is simply software that automates repetitive, typically manual processes and executes them accurately, efficiently, and inexpensively – which makes it very popular in the business world.

With the RPA market forecast to reach $ 7.1 billion by 2025, this technology is fast becoming an indispensable tool for businesses to keep up with the competition. RPA is particularly beneficial for companies that process large amounts of structured data, which is specific data stored in a predefined format, such as a particular cell in a table, field in a database, or an entry in a Online form.

RPA in business

During the pandemic, RPA was at the center of attention as companies looked for ways to provide goods and services with reduced human contact. Many of these companies are now using RPA to automate supply chain processes such as data entry, billing systems, and customer service. Not only does RPA offer great cost and efficiency savings, it also gives employees time to focus on more creative tasks.

From its beginnings as a software tool programmed solely to work with structured data and automate repeatable business processes, RPA and its definition has evolved. Deloitte recently described RPA as something that “combines artificial intelligence – including natural language processing, machine learning, autonomy, and machine vision – with automation”.

How organizations can overcome the disadvantages of content processing from RPA

This article explores ways businesses can overcome the shortcomings of Robotic Process Automation (RPA) in content processing. Read here

RPA meets AI

While some would argue with this definition, there is no question that RPA and AI together are a powerful tool. With aspects of AI like computer vision that can “read” digital images and extract data from documents, as well as natural language processing and semantic analysis used to understand and interpret text in context, process automation is taking a giant leap forward Forward. This combination enables systems to understand structured and unstructured data and to learn from billions of transactions, data points and user feedback.

When processing expenses and invoices, for example, intelligent automation can not only extract predictable, structured data from form templates, but also read barely readable texts from receipts and apply semantic understanding to invoices. Based on AI, financial tools can make independent decisions by understanding what is being bought, who is buying it, and how it should be classified and accounted for.

AI systems use ingested data to learn and develop a deeper understanding of business scenarios, further enhancing their decision-making and prediction skills. The more information is consumed, the smarter it becomes. While RPA can require human intervention if the system fails to recognize certain types of data, AI can use context to process the situation and provide an intelligent and informed decision. RPA is typically limited to rule-based tasks, while AI comes with tools to understand exceptions to the rules.

What’s next for RPA and AI?

When it comes to business automation, both RPA and AI add value, but there are certain scenarios where each technology makes more sense. For example, many expense management systems use RPA to extract predictable, structured data from templates that have been filled out by employees. This automation eliminates further human intervention by validating and then approving or rejecting reports.

However, when the system cannot process the data and needs to be instructed to classify and process it, organizations with a lot of unstructured data still require human intervention. In such cases, AI-powered solutions may be preferable, as such tools can easily read unstructured data such as handwritten receipts from fare dodgers or contractual terms from contracts.

Despite the proven benefits of RPA and AI, accounting and finance operations still lag behind when it comes to automation. Only 12% of the companies surveyed use RPA tools, while around 11% use AI. As a result, companies have a tremendous opportunity to increase performance, productivity, and efficiency through automation – something that makes good business sense for any business.

Written by Kunal Verma, Co-Founder and CTO of at AppZen

July 11, 2021