As of 2021, intelligent automation (or 'intelligent process automation' - IPA) is one of the biggest trends in the business world. In fact, its market was valued at USD 6.25 billion in 2017 and is expected to grow to USD 13.75 billion by 2023, according to a report by Markets and Markets.
When you think of it, it’s hardly surprising, given that it helps businesses optimize operations and automate numerous processes. It’s goal-oriented, which means it can be used to delegate mundane, repetitive tasks such as data entry or simple product assembly. As a result, it reduces the amount of human labor required to complete work, lowering costs, increasing productivity, improving accuracy, and providing many other benefits.
In this article, we will explain what intelligent automation is, how it works, and how it can be leveraged by companies of all sizes. We’ll also look at examples of IPA from top industries, including healthcare and the world of finance. Let’s begin.
Intelligent automation also includes business process management (BPM), often known as business workflow automation. Business process management (BPM) is the practice of automating workflows in order to increase the agility and consistency of business operations.
When you look around, you’ll see that a business process management system is used in almost every industry to simplify procedures. And while BPM has been around for a few years, it is constantly evolving to accommodate the newest technology, such as bots and Artificial Intelligence. Finally, BPM also allows for tool unification and coordination, which is required for intelligent automation to be effective.
With this short introduction in mind, you might now be wondering:
How is intelligent process automation different from robotic process automation?
To truly understand where the lines between intelligent process automation and robotic process automation are drawn, we need to look at them a little closer.
Robotic Process Automation (RPA)
RPA software serves as the foundational technology for AI and IPA. It automates simple and repetitive activities across a wide range of applications. In particular, it excels at automating basic functions and is ideal for businesses that manage vast amounts of data. As a result, businesses can profit from its speed and reliability. That being said, it also comes with its limitations. Let’s refer to an example:
Imagine RPA as a high-processing digital assistant; one that can reroute papers, sort and file attachments, and execute actions based on keywords. However, this software isn't intelligent (i.e., apt to make its own decisions). For example, if an email with an attachment is received, it will be recorded in Google Drive or Dropbox – every time. RPA will just do what it is taught and will not alter its behavior based on previous experiences.
With that in mind, let’s now look at intelligent process automation.
Intelligent Process Automation
As mentioned above, to conduct and automate digital operations, IPA integrates RPA, Machine Learning, and a number of other AI methods. For example, while RPA mixes web scraping and workflow automation, IPA combines more sophisticated AI disciplines like natural language processing (NLP), or data extraction with process automation. This opens a whole range of possibilities, previously unattainable for business systems.
One of the most critical distinctions here is that, unlike RPA, intelligent automation is perfectly apt to recognize and analyze unstructured or semi-structured data (think of files like PDFs, images, and video). These types of data are generally process-oriented rather than data-oriented, making it challenging (if not impossible) to refer them to predefined models. Intelligent Process Automation can handle such data, without access to large training data sets or complex, rule-based training.
Which Should I Use? A General Rule of Thumb
To summarize, RPA automates repetitive activities using software robots or bots and will do exactly what it’s trained to. Meanwhile, IPA is a more refined solution, which learns from its previous experiences, makes contextual choices, and can handle unstructured data sets.
If you have a project with clear guidelines and can’t see many off-the-script scenarios, go with RPA. On the other hand, if your data is unstructured and needs more complex assessments, intelligent automation may be a better match. Furthermore, unlike RPA and AI, IPA does not need rule-based training nor large data samples, making it ideal for situations when answers are required immediately.
How does intelligent automation work?
In the previous section, we’ve already mentioned that IPA shares some of the features that can be found in RPA software and BPM systems. Below is a brief explanation of some of the key functionalities that stand behind the mechanics of intelligent automation:
We've already mentioned that IPA combines RPA software. RPA helps automate traditionally labor-intensive, rule-based activities that do not need human judgment or intervention with intelligent systems, such as:
- Artificial Intelligence
AI refers to computer systems that mimic human intellect. Artificial Intelligence is the core of intelligent technology, including everything from Intelligent Automation to machine and robot intelligence. AI processes data more quickly than humans and learns from its mistakes. However, whatever AI “learns” comes from the training data sets that it’s given. Intelligent Automation uses AI, but it's usually used to create strong processes or anticipate user and consumer requirements. So, although it has built-in logic, it also learns from past experiences.
- Machine Learning
Machine Learning is a kind of AI software that uses algorithms to detect patterns in structured data and generate accurate predictions utilizing previous data as input to anticipate outcomes. Businesses can build a knowledge base and make predictions based on structured and unstructured data by using Machine Learning and sophisticated algorithms to evaluate structured and unstructured data. Machine Learning is the foundation of IA's decision-making engine.
- Computer Vision
Computer Vision refers to technology tools that transform scanned documents or images into text. It's an important piece to IPA because the internet is text and image-based. Text is simple to read and search. However, images are another story. We have relied on metadata, or human descriptions, to understand what pictures convey. But metadata can't fully convey the substance of those pictures. We need computers that can perceive pictures and comprehend their information to automate processes efficiently. A wide variety of applications, such as invoice processing, insurance claims processing, and know-your-customer (KYC) efforts at financial institutions, are already using this technology in Intelligent Automation processes.
- Natural Language Processing (NLP)
A computer's ability to comprehend, analyze, and modify spoken or written language is referred to as Natural Language Processing (NLP). Text analytics tools break down sentences into individual elements. As a result of this, NLP deciphers the message, evaluates the sentiment, and categorizes it. IPA benefits from the automated data extraction of structured, unstructured, and semi-structured data using NLP. NLP can improve the user experience by adding a humanistic touch to automated end-to-end operations.
- Process Mining
Process Mining is an analytical method to diagnosing business processes as they exist, then documenting and enhancing processes based on data analysis. Intelligent Process Mining empowers data-driven choices that have a long-term effect on service delivery. Using Intelligent Process Mining technology, processes will run smoothly and efficiently in less time. With Process Mining, IA technology will not only be planned and implemented efficiently, but also monitored and enhanced constantly based on gathered data.
Let’s briefly discuss a few key benefits intelligent automation brings to the table, regardless of industry:
- Reduced costs
Intelligent automation is particularly adept at responding to fluctuations in demand. In contrast to conventional models, it makes it possible to expand or reduce capacity very quickly and at a fraction of its expense. Having this possibility may be necessary to ensure that peak demand does not adversely impact the customer experience or employees' happiness.
- Improved accuracy & quality
Using intelligent automation, businesses can lower the risk of transactional errors, such as erroneous data inputs or missing stages in a process and faults in rule application. This can help enhance overall data accuracy and data-driven decision making.
- Better customer experience and service
Software robots are available 24 hours a day, seven days a week, ensuring predictability, reliability, and continuity of service—even during periods of high demand. This will provide the business a competitive edge and enable them to:
- provide a richer, more pleasant customer experience
- bring a higher-quality, more dependable product to market, quicker
- respond to customer questions more instantly
Why are businesses turning to intelligent automation?
Now, how about the motivation that makes businesses turn to IPA? Here are a few goals and challenges the solution helps them address.
Cost and scarcity of labor
Modern businesses need to tackle two simultaneous challenges – increased competition for services and scarcity of workers, which hinders business development. Due to a lack of available employees, many organizations fail to use the enormous quantity of data they have to their full advantage. Here’s where IPA comes into play, relieving humans off manual data processing and analysis.
To be more innovative
Deloitte questioned 523 executives in 26 countries spanning Africa, the Americas, Asia, and Europe in May 2019 about their intelligent automation initiatives and workforce effect. The research found that these businesses are using RPA and expanding the usage of intelligent automation. Of those questioned, 58% of CEOs said they've begun automating. Specifically, more than a third are piloting, 13% are deploying, and 8% are automating at scale.
Organizations think they can now change their business processes by automating choices based on structured and unstructured data, resulting in increased speed and accuracy, while decreasing costs. They anticipate an average payback time of 15 months — and a payback period of just nine months during the scaling phase.
To accelerate digital transformation
World organizations are reconsidering how to speed up digital change while avoiding disruption. Automation opens up new ways to rethink company operations. When done properly, technology enables the creation of a human-centric workplace where employees are empowered to perform more meaningful work. Intelligent automation decreases overall operational costs, improves business performance, and helps the company generate maximum value.
What are some applications of intelligent automation?
Let’s look at a few examples below.
Predicting and adjusting production in the automotive industry
Thanks to Intelligent Automation, manufacturers can better anticipate and adapt output to changes in supply and demand. They can improve efficiency and decrease errors in manufacturing, support, procurement, and other sectors. They achieve a better quality product for consumers at a cheaper cost by using robots to decrease human labor and enhance fault detection.
The automotive giant has decided to invest in collaborative robots, as a way of addressing the drop in the number of employees. The decision came at a time when baby boomers, who used to work at their assembly facilities, started retiring, with no prospective workers to replace them.
At Volkswagen, robots engage in potentially hazardous activities, aid with manufacturing and procurement, and, overall, minimize the likelihood of human mistakes.
Analyzing and answering complex financial queries in finance & banking
IA-powered computer systems can address complicated financial questions given in plain English and in real-time, enabling speed, time, and automation of formerly human-intensive knowledge labor.
Kensho, a real-time computing and analytics platform, received a $15 million investment from Goldman Sachs.
Using parallel statistical computing, user-friendly visual interfaces, and breakthroughs in unstructured data engineering, Kensho is a next-generation analytics tool for investment professionals. It quickly and simply answers millions of complex financial queries, automating (traditionally human) intensive knowledge work.
Predictive analytics and disease risk assessment in healthcare
Respectively, the healthcare sector uses intelligent automation to gather, analyze, diagnose, and treat patients. Remote healthcare providers also leverage NLP-powered chatbots that need less human involvement and, often, offer faster diagnoses.
Example: Kawasaki disease diagnostics
A common example of NLP in healthcare is the diagnosis of Kawasaki disease, a time-sensitive illness where a late diagnosis may lead to serious consequences. The algorithm has a 93.6% sensitivity and a 77.5% specificity when compared to doctors' manual examination of patient records. Similarly, NLP may evaluate unstructured input to identify patient complaints or unsatisfactory results.
Looking towards the future
With the benefits of lower costs of operation, better quality, and better customer service, it’s no surprise that so many businesses are turning to intelligent automation. Companies that decide to introduce it in their workflows are better equipped to become innovative, competitive, and go through digital transformation.
We can expect to see more and more businesses turn to automation in the coming years – especially, as it’s now also become more attainable for small-to-medium businesses. What's more is you don't even need a software development team to harness the power of your own custom IPA systems.