Intelligent Automation vs. RPA: Learn the key differences between these two automation technologies & how they can benefit you. Contact BP3 for more info.
Intelligent automation and robot process automation (RPA) are often considered similar. They both play a crucial role in automating business processes and improving accuracy while saving time and money. The two are linked, but while RPA is part of intelligent automation, they are not the same thing. We will observe how intelligent automation plays a broader role in process automation and compare intelligent automation and RPA by uses.
Intelligent automation combines several technologies that allow businesses to efficiently implement process automation for repetitive tasks that a human previously would have done. These solutions use AI and machine learning to optimize processes, which is also called intelligent process automation.
There are several components and technologies for creating intelligent automation solutions. Each has its unique purpose and, when combined, generates powerful tools to help businesses save time and lower operational costs.
This is the broadest tool we will describe, as AI has many meanings and applications. We will focus on how AI applies machine learning and complex algorithms to analyze structured data through predictive AI and unstructured data through generative AI.
Predictive AI analyzes historical data to make predictions about future trends and activities. Generative AI identifies patterns in a dataset and then makes new versions of the data that should be indistinguishable from the real inputs. Common examples of this are AI images and AI voices. Generative AI allows businesses to automate content production for marketing content efficiently.
Natural Language Processing: Natural language processing (NLP) allows machines to understand human speech or text and respond in a human-like manner. NLP can be applied to chatbots or voice assistants to improve the customer experience. These features are always available, and you never have to wait for human workers to become available.
Predictive AI automates complex tasks by analyzing enormous historical datasets. This is used in financial forecasting, sales forecasting, and stock reordering systems.
Generative AI produces marketing content through images, voice-overs, and videos. It is excellent for creative tasks and inspiration. Combining AI and automation to streamline content production leads to substantial cost reductions.
OCR can consolidate data into a singular, usable format. You can transfer physical documents into digitally readable ones quickly. For example, an accountant can input physical receipts to do tax returns, or an employee at a law firm can transfer hard documents onto their system to distribute easily.
Computer vision is at the heart of autonomous vehicles. Without recognizing the potential dangers that lie ahead, self-driving cars would be impossible. There are several other practical applications for computer vision, too. It can be used to monitor crops in agriculture, analyze medical images in healthcare, and spot defects for quality control in manufacturing.
NLP is integrated in intelligent process automation in several different ways. It can improve customer satisfaction through customer service chatbots that answer immediately. It can also be used to read and summarize large documents that would have been time-consuming.
RPA is software that automates repetitive tasks that had been performed by humans using a rule-based system. It uses robots or bots to interact with other software to manipulate data like humans.
The bot is trained to perform a task by a person who records the necessary steps to complete the task. Once trained, the bot can be deployed to complete the task on a one-off basis or a schedule. The bot is then monitored to ensure no errors occur and is retrained if any problems arise.
There are several parts to setting up a successful RPA. Here are the four core components and best practices to follow.
Bot Runner: The bot runner instructs the bot to start performing the task. When you activate the bot, check closely that it is performing the task thoroughly and correctly using actual data. There may be edge cases that pop up on which you have not trained the bot yet. You may need to update or retrain the bot if there are any issues at this stage.
Control Center: The control center is used to monitor and manage RPA workflows. You can set up a specific time or schedule from the control center for the bot to execute the task. This is also where you can scale up the activities of the bot if your requirements or requests change.
RPA can be practically applied to benefit several industries. Customer service tickets can be automatically routed to the right person or department. RPA can automate financial tasks such as payroll, processing invoices, and reporting. It can be used in Human Resources to onboard staff and assist with performance management.
Industries that have many independent entities that need to communicate with each other often find it difficult to centralize data. This is especially true within the healthcare industry. RPA can help to collect, curate, and aggregate data, improving interoperability between healthcare professionals and patients.
Regardless of the task to which RPA gets assigned, the automation technology can increase efficiency and cost savings. These systems can also improve accuracy by removing human error from the equation, and they are highly scalable and can deal with large datasets quickly.
Both types of automation technology help streamline business processes to save time and money. As we have covered, they do this in different ways and have different applications. Let’s look closely to parse out the specific differences between each of these.
The difference between how intelligent automation and RPA work can be compared to the difference between company leaders and managers. A company leader is creative and has a view of the big picture, just as intelligent automation uses AI and machine learning to analyze large datasets and make recommendations.
A manager's role is to ensure day-to-day tasks are performed efficiently without deviating from the plan. This is similar to RPA, which is efficient and reliable at carrying out tasks once it is told what to do. It does not add creativity and cannot handle complex business processes; on the flip side, it usually gets simple jobs done.
Automated intelligence having a host of components and technologies gives it powerful cognitive capabilities. Combining generative AI, predictive AI, machine learning, OCR, computer vision, and NLP allows you to perform complicated, creative tasks that not only exceed RPA but also surpass the abilities of most humans.
Due to the focus of RPA, it is often less adaptable than intelligent automation. You can adapt the steps you record for a process, but you may be limited to relatively simple tasks. The advantage of the RPA system is that it is highly scalable. Because it takes less computing power, it can deal with large datasets efficiently and grow as your business requires more out of it.
Intelligent automation is less scalable than RPA due to its reliance on machine learning. The computational power needed to run machine learning models can get expensive. Scaling using cloud computing platforms and spreading the workload across multiple servers is still possible. The upside is that machine learning brings genuine automation intelligence, allowing for a higher adaptability. The intelligent automation toolbox has many different technologies that can be adapted to almost any situation. As your needs shift, you can integrate a new technology into your existing process without having to build your whole system from scratch.
RPA is one of the tools that you can use as part of your intelligent workflow automation. These are not competing components; instead, combining the value of RPA with other intelligent automation technologies can make for powerful business process automation.
It is all about choosing the right tool for the job and creating a clear strategy of how the technologies will work together.
Not every task is suitable to be fully automated, and if you are new to intelligent automation, choose one task to help you get accustomed to the process.
Choose a task that is repetitive or performed at a high volume. Automating these tasks can often be very time-consuming, freeing you up to focus on more strategic tasks. You can also choose jobs prone to errors, as automating them may improve accuracy and consistency.
These could include processing customer orders, producing financial reports, or conducting inventory management.
As we discussed, developing a clear automation strategy involves selecting which tasks should be automated. The second step is to set your goals. Define whether you intend to improve efficiency, reduce costs, or improve customer satisfaction. Next, you can set targets against these goals to measure the success of your automation implementation as accurately as possible.
The tasks you choose and the targets you set will define which technologies you choose to use. As we have talked about, there are many tools from which you can choose, and you may find the best results by selecting a combination of these tools.
You can develop a custom solution that fits your needs by combining technologies. An example of this is an insurance company that wants to automate claims processing. Your intelligent workflow automation can start by using OCR to digitize paper claims forms that have been submitted. NLP will then extract the relevant data from the client claim form. Next, machine learning will judge whether or not to approve the claim. Finally, RPA can automate the process of issuing the payment if the claim is approved or of contacting the claimant if the claim gets rejected.
Typically, this would be a time-consuming, labor-intensive operation for people. By giving the process a digital transformation, you can save time and money and be able to devote resources to other aspects of your business.
If you have business processes that you want to automate, contact us today to check out what combination of intelligent automation solutions may be just right for you and your team. We have already developed custom automation solutions for hundreds of top companies and have a 99% success rate.
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