Soon (and it can’t come too fast), all the confusing automation acronyms and jargon that people are fond of bandying about—RPA, IA, AI, LC/NC, ML, IDP, IPA, OCR, iOCR, BPA, iPaaS, RPA-plus, hyperautomation—are going to fade away.
That’s because the great convergence is coming. Artificial intelligence (AI) is being embedded in everything related to automation. Unstructured data is being welcomed into enterprise business processes and systems. Citizen developers are coming out of the woodwork. Everyone with a consumer-facing website is building personable and intelligent chatbots.
In short: everything about automation is merging and moving closer to a holistic vision that takes on the toughest business and operational process challenges from end to end.
And it’s looking quite likely that Robotic Process Automation (RPA) is going to win out as the foundational technology. As all these various sub-automation technologies are integrated, RPA is going to be the common denominator holding everything together.
All to improve automation
Here is a smattering of the technologies coming together on top of RPA:
Artificial intelligence (AI)—Soon, intelligence will be in everything. RPA will be more integrated with AI by the end of 2022. AI that mimics human behavior will become common and able to do the most complex tasks.
Machine learning (ML)—Technically, machine learning is a subset of AI, but it is worth calling out. It is the use of algorithms and statistical models to see and learn from patterns in data. The primary requirement of a machine learning system is that it can learn and adapt without following explicit instructions by a human.
Natural language processing (NLP)—Natural language processing is a combination of linguistics, computer science, and AI that enables computers to process and analyze large amounts of natural language. Why this is attractive for RPA: as a user interface, NLP allows people to interact with and automate systems using the spoken (or written) word.
Computer vision—Computer vision is a technology that allows computers to “see” and comprehend the contents of photos, PDFs, diagrams, drawings, videos, and other digital images. Computer vision is an increasingly popular technology to incorporate into automation and digital transformation initiatives. Many people combine computer vision with older optical character reader (OCR) technology. OCR can only do text recognition. It extracts and digitizes printed, types, and some handwritten texts. But computer vision uses machine and deep learning to look at an image and understand all the content that it contains.
Intelligent document processing (IDP)—Intelligent Document Processing (IDP) solutions transform unstructured and semi-structured information into usable data. Business data is at the heart of digital transformation; unfortunately, 80% of all business data is embedded in unstructured formats like business documents, emails, images, and PDF documents.
Intelligent document processing is the next generation of automation, able to capture, extract, and process data from a variety of document formats. It uses AI technologies such as natural language processing (NLP), Computer Vision, deep learning and machine learning (ML) to classify, categorize, and extract relevant information and validate the extracted data.
Intelligent data extraction—Robotic Process Automation is one of the facilitators of hyperautomation in an organization. Hyperautomation is the enablement of digital systems to take care of all routine tasks without requiring any human intervention. When such automation is implemented with the use of software robots, it is known as Robotic Process Automation. With Robotic Process Automation, any repetitive task which is governed by a set of rules can be automated completely.
Low code/no code (LC/NC)—The appeal of LC/NC coupled with the RPA world is that it allows non-technical business users to automate low-level RPA and other simple workflow automation tasks using graphical user interfaces (GUIs). The software may also have a conversational or search interface. Few, if any, programming skills are required.
The Internet of Things (IoT)—The IoT is the network of connected devices with sensors that collect and exchange data. They can gather information in real-time to help organizations make better decisions. When joined with RPA, this stream of data can be captured and put to use as part of automated business processes.
What will we call the future version of RPA?
Maybe it will be “hyperautomation,” as Gartner has boldly declared. (Gartner says that 85% of its clients report that they will increase or sustain their “hyperautomation investment strategies” over the coming year.) Forrester calls it “digital process automation,” whereas IDC refers to it as “intelligent process automation.” Perhaps the dust will settle around the simplest, most straightforward definition: “intelligent automation.” Whatever happens, it’s clear that RPA technology will continue to play an important role in the business world.