Robotic Process Automation (RPA) and Artificial Intelligence (AI) are highly useful technologies for reducing business operational costs while improving the user experience. They have several similarities, but their distinct differences make it important to distinguish between them.
Leveraging a combination of RPA and AI provides distinct advantages over using them separately. Let’s explore the dynamic synergy of RPA and AI, from enhancing business processes to achieving transformative intelligent automation.
What is RPA?
RPA bots serve as virtual assistants by allowing users to offload simple, repetitive tasks that, when performed manually by humans, require valuable time to complete. Their primary advantages compared to humans are their lightning-fast speed, continuous function (without the need to take breaks), and inability to grow bored with repetitive work.
Many financial institutions employ RPA to automate parts of their fraud detection process. Before RPA bots the detection process usually went like this: an agent analyzed an alert and interacted with the customer. The agent resolved the issue and documented the resolution by filling out a standard form and emailing it to stakeholders according to the fraud department’s service level agreements (SLAs).
By harnessing the power of RPA, however, the organization can deploy a bot to automate the wrap-up phase, made up of a series of time-consuming steps that don’t require human judgment or expertise.
Once they’ve handed this part of the workflow over to the bot, the agent can immediately move to the next customer. In these circumstances, RPA reduces handling time while improving accuracy, SLA compliance, agent performance, and customer satisfaction.
While RPA bots don’t learn, the system’s continuous functionality via automation makes it an extremely beneficial tool for completing repetitive tasks quickly and efficiently. The most significant of those benefits are as follows:
Because RPA operates via automation, it performs the same task ad infinitum, significantly minimizing the risk of human error in any process.
Another benefit of the automation aspect of RPA bots is their ability to generate continuous, chronological event logs that serve as documentation; the documents can then be used as audit trails, providing businesses with no-effort, built-in regulatory compliance.
Non-stop speedy functionality
Bots work 24/7. This gives businesses the option to perform tasks with no human intervention – meaning after business hours.
Coupled with the proven speed of RPA software, a team member could create and run the bot, leave for the day, and arrive the next morning to find a previously arduous week-long project completed and documented.
Improved employee morale
When teams are freed of long, tedious tasks like data entry, they can focus on work that requires thought-based, creative innovation (often the skills employees were hired for). This work is what grows businesses.
Furthermore, the elimination of repetitive tasks frees up employee time for more frequent and meaningful customer and vendor interactions. Great working relationships are a win for everyone.
What is AI?
AI is software designed to simulate human thought and intelligence, including learning, reasoning, and self-correction.
- Learning, in this case, is defined as the acquisition of information and the rules for using it.
- Reasoning refers to the use of rules and contexts to reach conclusions.
- Self-correction is the purposeful changing behavior based on learned information in order to improve success rates.
AI has the ability to translate unstructured data and develop its own logic. RPA, in contrast, is compatible with structured data only. Therefore, AI is designed to replace humans rather than supplement them, which is the purpose of RPA.
Common types of AI applications include:
Machine learning (ML)
It is a foundational aspect of AI, and integrates into each of the AI applications we observe below. Put simply, ML uses algorithms specifically programmed for data – both structured and unstructured – to mimic the ways humans think. This includes the functions we outlined above: learning, reasoning, and self-correction.
Keep reading to better understand how ML partners with other AI applications to interpret and imitate human language and logic.
Natural language processing (NLP)
NLP is a category of AI that couples models of human language (also known as computational linguistics) with machine learning technology to mimic a human interpretation of text and spoken word.
NLP gives AI the ability to interpret and respond to human language, whether typed or spoken aloud. You may be among the millions who use it daily in applications such as digital assistance or GPS systems.
It is one of the most advanced and widely used examples of NLP currently available. Users pose questions to a chatbot, which answers them based on:
- An AI model that uses large amounts of structured and unstructured data from many sources;
- Machine learning, which searches that data for the most relevant answer. Common sources of information used by ChatGPT include encyclopedias, computer-coding libraries and scraped websites.
Optical Character Recognition (OCR)
OCR is a technology that converts handwritten text to typed text. It’s been in use for decades, but the best traditional OCR systems are still only about 80% accurate. Applying a machine learning model to OCR, however, dramatically improves this rate with no additional human interaction.
How? First, a bot programmed to monitor an email inbox for attachments with images of handwritten text passes found attachments along to an ML model to convert the handwritten text into typed text. That text, in turn, could generate an invoice, which could then be uploaded to a business’ Enterprise Resource Planning (ERP) system.
The benefits of using AI in business process management are essentially limitless. AI technology can build and modify processes with just a few simple instructions.
Its fast, accurate data analysis can reveal sales and marketing trends, and interpret their significance in real time. Below you’ll find a few of the most common benefits of AI:
Better customer management
AI-based chatbots allow companies to scale their engagement with customers while freeing up employees for other tasks.
Chatbots also help businesses better understand their customers’ needs, and suggest products and services that better support those needs. This capability improves customer satisfaction, which is increasingly important to remaining competitive.
AI technologies can also support sales teams, which is crucial to generating revenue. These tools can record sales calls and transcribe them into text, which they can then analyze. Sales staff can use these results to shape strategies that increase sales.
Organizations are increasingly likely to rely on AI to protect their data. AI tools can identify security vulnerabilities and take appropriate actions to prevent cyber attacks without direct human intervention.
How RPA and AI complement one another
RPA and AI have a lot in common, but the two technologies are distinct. An RPA solution is highly efficient, but it only does what the programmer tells it to. On the other hand, a programmer only tells an AI solution what its end goal is, without telling it how to get there.
RPA automates rule-based tasks, but AI can bridge gaps in its capabilities. For example, RPA requires structured data, which isn’t always available. AI can derive useful insights from unstructured data and provide it with a structure that RPA can understand.
Use cases of RPA combined with AI
Intelligent automation (IA) is an approach to problem-solving that combines RPA’s speed and efficiency with AI’s adaptability and decision-making capabilities.
It uses algorithms to analyze data and make decisions, allowing IA systems to adapt in response to changing circumstances. Use cases of IA include intelligent data extraction, cognitive automation (CA) for decision-making and adaptive process automation (APA).
Learn more: RPA vs. IPA: What Is the Difference?
Intelligent data extraction
Bots can exceed their traditional limits in extracting data by leveraging AI techniques like ML and NLP. These intelligent bots are often used to obtain unstructured data from sources like images, handwritten text and PDF files. They also use AI to contextualize this data, reduce the noise in the source material and improve the accuracy of data.
Cognitive automation for decision-making
CA is an extension of IA that specializes in processing large amounts of data. It uses techniques like deep learning, image recognition, NLP and neural networks to perform diagnostics and predictive analytics that emulate human behavior. In addition to reasoning, CA can imitate emotions and other attributes. This form of CA is now being used to supplement patient care, especially in nursing homes.
Adaptive process automation (APA)
APA automates digital processes by combining RPA with AI techniques like ML and Digital Process Automation (DPA). Unlike pure RPA bots, APA bots are programmed with self-remediating and autonomous decision-making capabilities that allow them to modify tasks for the purpose of optimizing processes.
They can also reason and retain information, allowing them to obtain new insights into data. In addition, APA uses AI techniques like ML and NLP to interact with humans. Other capabilities of APA include the ability to record actions, decisions and transactions, making these solutions highly accountable and auditable.
Transform your business with Pipefy AI
RPA and AI provide great ways to relieve employees from performing simple repetitive tasks, especially when they’re used in combination. This approach is best for straightforward processes with a well-defined beginning and end. It can also cut operating costs by allowing employees to focus on tasks requiring human creativity.
Pipefy AI is the first true no-code solution designed to automate and optimize process management. Sign up now to access Pipefy AI, use it to optimize your workflows, and experience the future of process automation.