Businesses are constantly searching for ways to drive revenue, cut costs and increase efficiencies. There are many options that companies can leverage to achieve these goals but there is one in particular that is getting a tremendous amount of attention - RPA. Robotic process automation software is the new darling of IT and business leaders alike, but why?
Over the last few years RPA solutions have evolved from niche technologies to critical enterprise platforms. Digital transformation and automation initiatives are fueling this massive growth and the investment community has taken notice. The potential industry growth is significant, with McKinsey & Company projecting the economic impact of RPA technology at $6.7 trillion by 2025. As a result, venture capitalists are pouring hundreds of millions of dollars into RPA-related startups.
Yet, there are still significant misunderstandings around what RPA is and how it can be used to improve existing business processes. Here are a few examples of where this technology is headed over the next several years.
Intelligent Process Automation
In the past, RPA used to automate simpler, more rule-based tasks. However, as business needs become more complex, artificial intelligence is starting to take center stage as a critical tool for allowing RPA to do more.
This cross between RPA and AI, sometimes referred to as IPA, or intelligent process automation, uses machine learning and natural language processing to solve challenging business problems and automate existing manual processes with even greater accuracy and efficiency. This new innovation has allowed businesses to become more profitable off each employee by eliminating mundane and manual labor.
Prior to the advent of computer vision, OCR allowed businesses to automate scanning and data extraction from relevant files. However, the technology falls short because the quality of the OCR is dependent on the scan quality. For instance, while OCR can accurately capture a native PDF, it will struggle to read a document captured from a mobile device or files containing distorted text and images. Alternatively, computer vision technology enables machines to process images and data within its proper context in a similar manner to humans. Through complex algorithms, this technology allows RPA bots to understand and automatically process images, tables, graphs, and other types of content that OCR does not.
Machine learning algorithms allow bots to learn patterns from various data sets and use those patterns to make predictions or decisions about new or incoming data. With machine learning, RPA bots can make more complex decisions and become more efficient as it grows a larger library of reference data.
Natural Language Processing
Natural language processing allows machines to analyze language through speech recognition, semantics, and syntax like a human. The machine learns through listening and reading within the right context to understand complex files such as contracts or tax forms. Through this technology, bots can communicate more directly and fluidly with their human counterparts.
It is impossible to predict how businesses will be affected by the continued advances in RPA technology. However, the space has shown significant potential for growth with several avenues that have yet to be tapped.
Although these advances are not yet fully developed, the future of RPA is bright and leading innovators are already leveraging this technology to enhance their current systems. As a result, businesses can operate more efficiently and have more control over their bottom line.