Robotic Process Automation (RPA) is driving digital transformation by providing businesses with a solution to business process automation.
What is RPA?
UiPath, our technology partner in the RPA space, defines Robotic Process Automation as “the technology that allows computer software to mimic actions typically performed by humans interacting with digital systems to execute business processes.” Repetitive processes can be automated to achieve higher levels of employee satisfaction by offloading the mundane tasks through RPA.
RPA robots can capture data, run applications, trigger responses, take decisions based on predefined rules and communicate with other systems. Robots target processes that are highly manual, repetitive, rule-based, with low exceptions rate and standard electronic readable input. RPA can complete tasks that don’t require in-depth thinking or decision making. For example, robots can check emails, send emails, upload documents, read Excel spreadsheets, open browsers, and search Google.
RPA is non-invasive, easily scalable, and future proof. It doesn’t require any major IT architecture changes or deep integration and can be used with any pre-existing systems. RPA is a fast, reliable, and cost-efficient solution for a light-weight integration into processes and IT assets. The work involved by a process can vary, as changes are likely to occur in most businesses. Companies can easily adapt their RPA by scaling the solution up or down depending on the requirements. RPA robots work with today’s technology, yet the automations are extensible, able to handle and grow with tomorrow’s technology.
RPA in Business
Businesses can use RPA in ways ranging from payroll processing to customer complaint processing. Payroll processing needs manual intervention month after month. RPA can extract the details from handwritten timesheets and calculate the pay from employees’ contracts. Then, it can pay employees by ordering the necessary bank transactions.
Any organization that has implemented a CRM faces related issues. For example, the client-base is spread across many geographies. Or, there are frequent calls to the back-end databases, and updates and changes are coming from all sources. RPA solutions can process these requests in batches instead of one after the other. This reduces the load on the back-end systems. It ensures better performance and data quality across the whole application.
Regardless of industry, the contract renewal process is generally a complex process, but not necessarily due to exceptions and complications. Rather, it comes down to the number of operations and the synchronization between different departments and systems. Robots can take over the process starting with the standardized communication with the client, processing the changes, drafting the documents, and updating the internal systems accordingly.
Financial statement reconciliation covers all the operations (done mostly by the accounting teams) of matching orders, payments, losses, margins, and so on, with accounts and financial statements. Organizations typically need to ensure clean records and reliable financial documents. This process is well-handled by RPA software robots.
As organizations grow, it becomes increasingly difficult to closely monitor the compliance requirements that each department has to follow: reporting to authorities, following the internal procedures, audit requirements, and so on. Robots can be set up to cover all these needs, with a low error rate and minimal human intervention. Customer complaints are always on the radar of businesses that revolve around customer interactions. Their number and substance is an important indicator of the business’ health and good predictor of the future of the company. Through RPA, customer complaints can be categorized based on keywords and other criteria, and possible solutions can be suggested to the customers right away. By doing so, customer complaints can be answered 24 x 7 instead of only during business hours.
To amplify the ability of organizations to automate work, RPA can be expanded with advanced capabilities, such as artificial intelligence (AI), process mining, machine learning, and advanced analytics. Hyperautomation allows organizations to rapidly identify and automate as many business processes as possible, including knowledge work.
Artificial intelligence capabilities such as Natural Language Processing (NLP), Intelligent Optical Character Recognition (OCR), and AI computer vision enable robots to take over more advanced tasks. Machine learning capabilities allow your robots to learn from the automation work they do, so that they gradually improve. This is valuable in the Document Understanding field, but applications can be found in other areas as well.
AI and machine learning (ML) are used specifically for process automation scenarios performing continuous learning with data collected in automation processes to update models dynamically and make the necessary adjustments. To realize full business potential, it’s crucial to deploy AI technologies that deliver specific, measurable business outcomes for targeted use cases.
UiPath’s Document Understanding uses machine learning models to enhance bots with AI, models get more accurate over time, so the entire process will get more efficient. UiPath robots can provide automation support for a variety of document types including pdf, jpg, doc, png, bmp, and gif. A common problem in document understanding is document ‘noise’ from rotated, skewed, unrelated, or low-resolution documents. UiPath robots, however, can cancel any ‘noise’ that other bots falter on.
UiPath has many engine options for OCR with UiPath’s native screen scraping capabilities. For automated document understanding. Available OCR engines include Google Cloud vision, Microsoft Azure computer vision, Tesseract, Microsoft Project Oxford Online, and UiPath’s native document and screen OCR. UiPath’s screen scraping makes it easy and accessible to implement document automation.
Although the potential benefits of AI and ML are vast, their approaches have very low explainability. If you need to be able to justify your decisions within a process, then using AI and ML may not be a good approach and you may need to implement some human oversight. Common use cases include many industry-specific instances, such as claims handling in insurance, anti-money-laundering efforts in banking, and product data matching in retail. Other uses of AI automation can be seen in contract management, legal processes, clinical trials, and healthcare.
Process mining is a powerful tool in the hyperautomation toolkit. Process mining discovers, monitors and improves processes by extracting knowledge from the event logs readily available in application systems. By using historical data, process inefficiencies can be identified at a granular level supplying businesses recommendations for improvement. UiPath employs the use of the recently acquired ProcessGold company for their process mining.
Hyperautomation allows business processes most suited for automation to be rapidly and scientifically identified. By performing the expected activities, robots learn new skills and gradually improve their work. Hyperautomation gives companies a thorough and unbiased view on the impact of automation, their next possible move, and what can further be improved.
The RPA industry has rapidly become a rising star, the market size growing from $250 million in 2016 to $2.9 billion in 2021 and is expected to reach USD 10.7 billion by 2027. It helps organizations drive efficiency, increase productivity and cut costs by automating various repetitive and manual tasks. RPA has the ability of helping any organization reach its full potential.