Artificial Intelligence to Automate Manual Processes for Business
Intelligent automation also includes business processes management (BPM), commonly known as business workflow automation. BPM (business process management) refers to the automation of workflows to improve the efficiency and consistency of business operations.
You’ll find that almost every industry uses a business process management system to streamline their processes. BPM is a well-known system that has been in use for some time, but it is continually evolving to keep up with the latest technology such as artificial intelligence and bots. BPM allows for tool unification, coordination and other features that are necessary for intelligent automation to work.
You might be asking yourself these questions now that you have read this brief introduction.
What is the difference between intelligent process automation and robotic process automation?
We need to examine them more closely in order to understand the differences between intelligent process automation (IPA) and robotic process automation.
Robotic Process Automation (RPA).
RPA software is the foundational technology of AI and IPA. It automates repetitive tasks across many applications. It excels in automating basic functions, which is great for businesses managing large amounts of data. Businesses can benefit from its reliability and speed. However, there are limitations. Let’s take an example:
RPA can be described as a digital assistant that handles high-processing tasks such as sorting and filing attachments, rerouting papers and executing actions based upon keywords. This software is not intelligent, so it’s not able to make its own decisions. If an email is sent with attachments, it will be saved in Google Drive or Dropbox. RPA will only do what it’s taught. It will not change its behavior based upon previous experience.
Let’s look now at intelligent process automation.
Intelligent Process Automation
As we have already mentioned, IPA integrates RPA and Machine Learning to automate digital operations. RPA combines web scraping with workflow automation. IPA, however, combines more advanced AI disciplines such as natural language processing (NLP) or data extraction with process automatization. This opens up a world of possibilities that were previously impossible for business systems.
Intelligent automation, unlike RPA is able to recognize and analyze semi-structured and unstructured data. Think of files such as PDFs, images and video. These data tend to be process-oriented and not data-oriented. It is difficult (if possible) to refer to them to predefined models. Intelligent Process Automation is able to handle these data without having access to large training data sets, or rule-based training.
Which should I use?
RPA automates repetitive tasks using software robots or bots. It will do what it is trained to. IPA, on the other hand, is a more sophisticated solution that learns from previous experiences and makes contextual choices. It can also handle unstructured data.
RPA is best if you have clear guidelines for your project and don’t want to see too many scenarios outside of the script. Intelligent automation might be better if your data needs more complicated assessments and isn’t structured. Unlike AI and RPA, IPA doesn’t require large data samples nor rule-based training. This makes it ideal for situations where immediate answers are needed.
Understanding the differences between RPA in automation and IPA
What is intelligent automation?
We’ve mentioned in the previous section that IPA shares some features with RPA software and BPM system. Here is a quick explanation of some key functions that underlie intelligent automation.
We have already spoken of the fact that IPA combines RPA-based software. RPA automates labor-intensive, rule-based tasks that don’t require human intervention. Intelligent systems such as:
- Artificial Intelligence
Artificial Intelligence refers to computer systems which mimic human intelligence. Artificial Intelligence is at the heart of intelligent technology. It includes everything from Intelligent Automation to robot and machine intelligence. AI processes data faster than humans and learns from its errors. AI learns from its training data. Intelligent Automation uses AI. However, it is often used to anticipate consumer and user needs or create strong processes. Intelligent Automation has logic built in, but it also learns from past experience.
- Machine Learning
Machine Learning is an AI program that detects patterns in structured data. It generates accurate predictions using previous data to predict outcomes. Machine Learning is a combination of sophisticated algorithms and machine learning to analyze structured and unstructured information. This allows businesses to build knowledge and make predictions. Machine Learning is the basis of IA’s decision making engine.
- Computer Vision
Computer Vision is a technology tool that converts scanned images or documents into text. Because the internet is both text- and image-based, it’s an essential part of IPA. Text is easy to search and read. Images are a different story. To understand the content of images, we have relied on metadata (or human descriptions). However, metadata cannot convey the essence of these pictures. To automate tasks efficiently, computers must be able to perceive and understand images. Intelligent Automation is already used by a wide range of applications such as invoicing, claims processing, and know your customer (KYC), efforts at financial institutions.
- Natural Language Processing
Natural Language Processing (NLP) refers to a computer’s ability understand, analyze, and modify spoken or writing language. Text analytics tools allow you to break down sentences into their individual parts. NLP interprets this information and then categorizes it. NLP allows for automated data extraction from structured, semi-structured and unstructured data. NLP can enhance the user experience by adding humanistic touches to automated end-to–end operations.
- Process Mining
Process Mining is an analytical approach to diagnosing business processes in their current form. It then documents and enhances them based on data analysis. Intelligent Process Mining empowers data-driven decisions that have a long-lasting effect on service delivery. Intelligent Process Mining technology will make processes run more smoothly and efficiently with less effort. Process Mining will allow IA technology to be not only planned and implemented efficiently but also monitored and improved continuously based on the gathered data.
Intelligent automation (IPA), the journey from discovery to analysis
Let’s look at some of the key benefits that intelligent automation can bring to the table regardless of industry.
- Lower costs
Intelligent automation can respond quickly to changes in demand. Intelligent automation is able to increase or decrease capacity much faster than conventional models and at a fraction the cost. This possibility is necessary to ensure peak demand doesn’t negatively impact customer experience and employee happiness.
- Increased accuracy and quality
Businesses can reduce the risk of transnational error by using intelligent automation. This includes missing steps in a process, erroneous data inputs, and rule application errors. This can improve overall data accuracy and help with data-driven decision making.
- Improved customer experience and better service
Software robots can be accessed 24 hours a days, seven days per week. This ensures predictability, reliability and continuity of service, even during peak demand periods. This will give the company a competitive edge, and allow them to:
- Provide a better, more enjoyable customer experience
- Quickly bring a better-quality, more reliable product to the market
- Respond to customers’ questions faster
What are the reasons why businesses are turning to intelligent automation?
What is the motivation behind IPA adoption by businesses? These are some of the challenges and goals that IPA helps businesses address.
Scarcity and cost of labor
Modern businesses must address two concurrent challenges: increased competition for services and a shortage of workers which can hinder business development. Many organizations don’t make the most of the huge amount of data they have because there aren’t enough employees. IPA is a tool that automates data processing and analysis.
Lyft’s ex-vice president of science Elizabeth Stone explains how AI helped to find a better algorithm in the report. The new algorithm discovered that Lyft customers who use the Lyft app to order a taxi through the app have strong connections.
AI can also be used to analyze legacy application source codes line by line, to understand their functions. Munindar Singh, a North Carolina State University professor of computer science, said that such insights can be combined with AI analysis to understand the business processes supported by legacy application logic. This could lead to opportunities for modernisation. He adds that AI is needed to help with low-level analysis of business processes.
Any AI-based analysis on the code will impact the business processes that the application powers. This area of research offers the possibility to use AI in a continuous business process improvement strategy. This scenario sees algorithms being constantly tweaked and the source code modified to reflect changes in the business process.
Intelligent business process automation could lead to AI systems optimizing and redeveloping code that underpins the way businesses work.
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