Intelligent automation comes in a variety of forms and technologies (also known as intelligent process automation or IPA and hyper-automation). Intelligent automation technologies or IA is a combination of robotic process automation or RPA with a variety of other cognitive technologies.
IA enables businesses to achieve end-to-end process automation as well as make the workflows smoother and more flexible. Hence, here are the top 10 intelligent automation technologies to keep an eye on in 2023.
1. AI and Machine Learning
Perhaps two of the most obvious examples that come to mind when intelligent automation technologies are concerned, are those of AI and Machine Learning. Both use digital data in combination with such elements as remote inputs and algorithms to imitate the way humans learn.
In most cases, AI and Machine Learning are used for making predictions based on past behavior and historical data analysis. However, this field is growing rapidly and these technologies are being applied in more and more areas.
2. Collaborative Robots
Collaborative robots are somewhat of a new invention, but they have already established themselves as an integral part of many industries, allowing them to prevent dangerous situations in the workplace and ensure a safe environment for workers.
Meryl Perlman, an expert from the writing services reviews site Writing Judge, says, “At the moment, there are four major types of collaborative robots: safety monitored stop, speed and separation, power and force limiting, and hand guiding. Each type has been useful in its own way and it’s great to see that such robots are still being developed and upgraded.”
3. Semantic Software Systems
Closely connected to Artificial Intelligence, semantic software systems and semantic technology are used to teach machines to understand data better. Semantics is encoded with the data through technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL).
The reason why semantic technology is so important is that its fusion of semantics and data offers a variety of benefits that significantly improve the way machines process heterogeneous data sources among other things.
4. Augmented Intelligence
As the name suggests, augmented intelligence is also related to Artificial Intelligence. To put it simply, augmented intelligence is a design model where humans work together with AI to improve cognitive performance.
The biggest benefits of augmented intelligence are connected to learning and decision-making, but the tech itself can be used in all kinds of industries. After all, it maximizes the power of human brains and AI by combining the two.
5. Process Intelligence
Process intelligence is mostly used in business to analyze a number of processes to create a particular process model of these operations. In other words, it analyzes a particular business workflow and provides businesses with insights into it.
With the help of process intelligence, you can detect problem areas in your business processes and significantly improve your operational model. Such analysis is particularly useful for businesses that rely on non-linear processes that make it harder to accurately analyze the entire workflow due to the number of dependencies.
6. Process Mining
Process mining is both a part of IA and process management. The primary aim of process mining is to support operational process analysis by using so-called event logs. It’s meant to show what humans, machines, and entire organizations are actually doing.
7. Natural Language Processing (NLP)
Also known as NLP, natural language processing is a branch within the more general fields of AI, computer science, and linguistics that is closely connected to IA. NLP focuses on the interactions and interrelations between computers and the human language.
More specifically, NLP is used to understand how computers should be programmed to process and analyze natural language data. NLP usually works with large amounts of such data which is precisely why it is so valuable.
8. Computer Vision
Computer vision is considered to be a field within IA that is concerned with enabling computers and entire systems to extract useful and relevant information from digital sources. Such digital sources can range from images to videos to other types of visual inputs.
Based on the extracted information, recommendations can be made both for further actions and for general assumptions or conclusions. In this sense, computer vision is essential for interpreting and understanding the visual world as well as making such interpretation possible for machines.
9. Optical Character Recognition (OCR)
Also referred to as OCR, optical character recognition includes either electronic or mechanical conversion of images from typed, handwritten, or printed text into machine-encoded text. Due to this, OCR is sometimes referred to as an optical character reader. The conversion usually involves certain types of materials such as scanned documents, photos of documents or scenes, textual subtitles on images, and so on.
10. Robotic Process Automation (RPA)
Last but not least, robotic process automation (also known as RPA) is a kind of software technology used to operate and manage software robots. These, in turn, are used to mimic human behavior when interacting with digital systems and software.
For example, such robots can simulate repetitive human actions, predict results, classify behaviors, scan documents, identify content and enhance the decision-making process.
Conclusion
All in all, intelligent automation technologies are definitely on the rise now and keep helping businesses innovate their processes. It’s a good idea to pay attention to the latest developments in the field of IA to understand how such technologies work and how you can use them to pursue your own goals.
Whether you are more interested in robotic process automation or you already have some experience in natural language processing, learning about all of the IA technologies will give you a better understanding of the field as a whole.
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