From an AI perspective, RPA and BPA solutions differ greatly in terms of their scope, use cases, and build. But together, they can power highly efficient enterprise solutions and enhance business operations.
Business process automation (BPA) and robotic process automation (RPA) have opened new doors to improve business efficiency and accuracy. With every passing year, automation technologies are getting more sophisticated to perform more complex organizational tasks.
Throughout this journey, artificial intelligence (AI) has played a crucial role in making automation solutions smarter, more accessible, and user-friendly. However, its functionality significantly differs in BPA and RPA technology, making it the key determinant to understand differences in their functionality.
In this blog, we are comparing robotic process automation and business process automation from the lens of AI. It will help you design your future automation strategy with optimum AI utilization.
What’s the difference between RPA and BPA?
Before getting started, here’s a quick refresher on RPA and BPA.
RPA, or robotic process automation, is a software-based automation approach where the system builds, deploys, and regulates virtual “robots” to perform human actions and complete individual tasks. Simply put, these are smart software that performs everyday repetitive business tasks without human intervention. Classic examples of RPA include automated employee onboarding, inventory management, chatbots, etc.
Business process automation refers to the use of technology to automate a variety of complex business operations to streamline workflow and replace manual labor. BPA is the overall digital transformation of an organization where we delegate non-critical business tasks to technology.
BPA can automate various business functions such as HR, marketing, supply chain management, process optimization, etc. Unlike RPA, BPA orchestrates a variety of complex tasks throughout the various departments and nodes within an organization and automates them from start to finish.
To sum it up, RPA uses software robots to perform individual business tasks while being a part of the overall automation system under BPA.
With a quick introduction out of the way, let’s look at how AI enhances BPA and RPA services.
Role of AI in BPA and RPA
According to McKinsey, about 18% of overall business activities can be automated using currently existing automation technology. With the rapid advancements in technology, we’ve witnessed a dynamic shift in business operations. Workflows of modern enterprises are becoming more streamlined and efficient due to AI-enabled automation.
But how does it function in BPA and RPA systems?
AI is designed to mimic human intelligence while RPA isn’t. AI uses smart algorithms to find errors in the system and adapts itself to avoid the same errors in the future. RPA does an excellent job at carrying out repetitive business tasks that need no technical know-how. However, it cannot adapt and learn from the errors and make changes accordingly.
Combining AI and RPA, also called hyperautomation, gives you the best of both worlds. You can leverage both the efficiency of RPA and the smart insights of AI. For instance, let’s say your business is using RPA to screen for job applications in terms of work experience, qualifications, and relevant skills. An AI system can help you optimize the process by notifying potential errors in the RPA system that might lead to losing an ideal candidate.
In both BPA and RPA, AI can augment the core functions of your solution. In RPA, AI appears largely in one or two steps before the RPA elements kick in. For example, during the discovery phase of building a solution, AI can help in process mining to help developers build an accurate process framework. Another example is where AI assists in reading documents or images, and then passes the information to software robots.
How AI capabilities drive BPA
Machine learning makes business process automation solutions smarter and more streamlined. It can help orchestrate, manage, and optimize process in complex industries where multiple levels of decision-making are involved.
Consider manufacturing, for instance, where machine learning can connect various machines in the assembly line as per a complex process, feedback, and correction framework. Here, a command given from a central hub can operate the entire manufacturing system without human inspection of every machinery, or identify gaps in the process and course correct.
With that said, here are a few additional key functions where AI-enabled BPA can help your business:
Retail and e-commerce: Using sales and marketing analytics solutions built with machine learning can aid teams in consumer sentiment analysis, demand forecasting, price optimization, consumer segmentation, providing customer recommendations, customer service, detecting and preventing fraud, and more.
Banking and Finance: In finance and banking, machine learning is used to power complex solutions for credit scoring, risk analysis, fraud detection and prevention, trading exchange forecasting, and other applications.
Healthcare: In healthcare and life sciences, machine learning is applied to increase diagnostic accuracy, identify at-risk patients, optimize costs of insurance products, and more.
Human Resource Management: Apart from the usual applications like automation workflows in every-day HR operations, machine learning can be applied source candidates with very specific skillset combinations, manage, analyze candidate profiles to look for things that are not in the resume, or to improve engagement by analyzing retention and attrition rates, and more to guide decision-making at each step.
To delve deeper into the applications of machine learning, Natural Language Processing (NLP), which is a subset of it, allow businesses to create virtual customer service platforms like chatbots or voice enabled assistants that can address customer queries 356×24/7. Such platforms can be customized and fed with data that allows it to have human-like interactions and find the most relevant answers for customers.
As AI interacts efficiently with other enterprise technology, it can streamline work across the various departments in your company. It improves your BPA system significantly, allowing your human resource to focus on more crucial and mission-critical tasks.
From an AI perspective, RPA and BPA differ greatly in terms of their utilization and dependency. I hope this blog gave you some clarity on the use of AI in the automation process so that you can design a robust AI-enabled automation ecosystem.
Speaking of which, VBeyond Digital designs BPA and RPA solutions keeping in mind the unique business needs of an organization. With our customized BPA and RPA solutions, we help you make your digital transformation more result-centric and future-ready.