As soon as you open your mobile browser and use a search engine, you'll notice that the applied AI processes millions of websites and finds the best results for your search, since Google relies heavily on AI technologies. Even when you decide to watch something on Netflix, the AI will suggest titles based on your preferences and previous shows and movies you've seen.
AI is generally defined as the simulation of human intelligence by machines. Like humans, AI can process the information fed to it, derive patterns and act accordingly. The technology is often used in combination with automation.
Before we explain how AI and automation combine, let's take a quick look at the history of artificial intelligence and how it became one of the biggest buzzwords in the technology industry.
The History of Artificial Intelligence
The concept of artificial intelligence dates back to ancient times, as much of the history of our civilization has revolved around non-human forms (artificial beings) that exhibit intelligence similar to that of humans.
At that time, Alan Turing, the legendary British mathematician and computer scientist, proposed that machines could imitate formal reasoning with zeros and ones, which became known as the Church-Turing thesis. The first formal research in artificial intelligence took place in the 1950s at Dartmouth College, where important breakthroughs were made.Research continued in the following years and was funded extensively by the U.S. Department of Defense, establishing laboratories around the world were.
The popularity of AI peaked in the 1980s, but other technologies came to the fore and serious research continued in the late 1990s, meaning that there was a period of more than a decade during which no major breakthroughs in AI occurred. This was mainly due to the collapse of the Lisp machine market in 1987.
In the last two decades, AI research has been more successful than ever, as researchers and investigators have had much faster computers and access to various data used to train AI.
AI and Automation
Before we discuss how artificial intelligence can be used in automation, it is important to make a brief distinction between the two terms, as these terms are often used as synonyms, which is not correct.
- Artificial Intelligence is a technology programmed to look for patterns in available data, learn from them, and adapt based on the newly discovered information.
- Automation is a "piece" of software that follows pre-programmed rules. It is used to automate monotonous and repetitive operations
But there is a good reason why AI and automation are sometimes mixed. They are often combined in different industries to maximize the scope of automation and make it intelligent. Let's take a look at some of the technologies that have come out of combining automation and AI.
Hyperautomation
Hyperautomation is expected to become one of the leading technologies of the future. Strictly speaking, it is not even a technology, but rather a business initiative that aims to automate as many business and IT processes as possible. Instead of a single tool that enables Hyperautomation, companies are using a toolbox to achieve their goals, which includes individual software components around the following tools:
- Robotic Process Automation (RPA)
- Intelligent Business Process Management Systems (iBPMS)
- Integration Platform as a Service (iPaaS)
- Artificial Intelligence (AI)
In short, Hyperautomation takes automation to a new level by combining AI, ML, and RPA. If you want to learn more about Hyperautomation, you should check out our blog post about it.
Intelligent Process Automation (IPA)
Intelligent process automation (IPA) is sometimes used as a synonym for Hyperautomation, but there is a slight difference. As described above, Hyperautomation is not a single tool, but a set of tools, and IPA is a part of that - a tool that combines technologies such as AI, ML and RPA and focuses on a specific set of tasks. Simply put, IPA picks up where RPA seems unable to automate, which typically involves automating more complex tasks, reducing human error, and providing more detailed and reliable results.
One popular service that offers IPA is SAP Intelligent Robotic Process Automation, which helps companies develop intelligent bodies with a low-code design tool. In addition, many other companies such as IBM, TCS, Tech Mahinda, Cognizant, Wipro and others offer various IPA-related solutions. We have covered IPA in detail in our Intelligent Process Automation (IPA) blog post.
IPA is combined with several other technologies, such as natural language processing and optical character recognition, and is used in many industries.
Natural Language Processing (NLP)
One of the things that makes humans unique is the ability to convey meaning through language. Therefore, the main goal of many linguists and computer scientists is to train AI to recognize and use human language. The interaction between machine and human language has been extensively researched in the context of natural language processing (NLP).
If you use services like Google Translate, you've already experienced NLP at work. It has become an integral part of our everyday lives and helps us overcome language barriers.
NLP in combination with automation is used in many industries. For example, in the banking sector:
Banks often process thousands of documents every day. Every transaction, every loan application, every account statement, every interaction with the customer - all of this contains a lot of text that someone would have to read, study and derive actionable insights from. This is where NLP combined with automation can do the heavy lifting.
In addition to document processing and analysis, banks (and many other industries) can use NLP and automation for customer service chatbots to help customers and clients with their banking needs.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is a tool that recognizes handwritten, typed, or printed text and converts it into machine-readable text. It can convert the text into machine encoded text so that it becomes editable and easier to process/analyze.
OCR is best explained with a practical example: When you travel to another country, one of the border officials puts your passport into a machine that automatically recognizes your passport number and extracts the relevant information to perform a quick check and enter it into a database.
Imagine if border agents had to manually type and enter all the information from the passports of thousands of travelers crossing the country's borders every day. It's definitely an unpleasant sight, but thanks to AI, OCR, and various additional technologies such as pattern recognition and computer vision, this has been automated to some degree. Apart from passports, many other documents are scanned with OCR software (and hardware) in various industries.
Another popular example is automatic license plate recognition (ANPR), which is used to read license plates and facilitate general traffic control, parking, and more.
The Future of AI and Automation
It's no secret that AI and automation are good buddies, working diligently in many areas of our lives, especially technology and process automation.
According to an analysis by McKinsey, which looked at more than 2,000 jobs in 800 occupations, some occupations proved more amenable to automation than others, including various jobs in environments that are highly structured and predictable. As a result, some occupations will be completely replaced by the combination of AI and automation. Currently, only 5% of all occupations can be fully automated, while others can be supplemented to some degree - does this mean that automation and AI will completely replace some human-performed tasks? Will anyone be laid off because of this?
It doesn't have to be that way, because these technologies will also create many jobs, many of which will revolve around AI maintenance, development and testing. When you introduce AI and automation into your business, you're paving the way for economic growth, because whenever the world changes dramatically, so does the need for jobs.
Conclusion
In summary, it's important to train your employees in AI and automation as soon as possible, especially if you want to integrate these technologies into your business processes. This way, you can protect employees, help them learn about new technologies, and even create new jobs in the future.
If you're interested in a world-class education in AI and automation as well as other related technologies, you should check out our courses at our Automation Academy.
.png)