10 Use Cases for Superior Automation: Improving Business Productivity

Digital-native companies that adopt hyper-automation can use it to automate their entire end-to-end workflow rather than just separate separate efforts.

It turns out that hyper-automation is the real technology hype, cutting across various industries and business requirements. By leveraging hyper-automation, businesses can process a wide variety of documents, including invoices, bills of lading, orders, receipts, payment receipts, medical records, and prescriptions. Other than these basic execution and process improvisations, hyper-automation can be the real game-changers for more complex business processes as well. Let’s check out how!

Hyperautomation: what is it?

Hyperautomation combines several automation tools and technology breakthroughs to improve process output, whereby robots can take over all the time-consuming, error-prone, and repetitive tasks. This is the key element that determines business success now as well as in the future. A company’s growth is largely determined by the productivity of its employees, and with hyper-automation, the team can have more time to focus on strategizing and enabling growth.

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The use of hyper-automation has increased more rapidly than ever in the post-COVID environment as businesses across industries strive to achieve sustainable digitalisation. In a survey conducted by McKinsey, respondents said their organizations were able to respond to digital developments at least 25 times faster than they would have expected in a pre-pandemic situation. In the field of remote work solutions, respondents said solutions were deployed 40 times faster.

No single automation tool can replace all aspects of human work. In order to do this, Super Automation plans to integrate a number of technologies, including business process management, robotic process automation, OCR, and artificial intelligence. Hyper-automation may help companies visualize their operational efficiency, analyze critical signs of execution, and understand how their processes interact to produce anything of value with the help of AI-powered decision-making capabilities. This will undoubtedly lead to new innovations in business processes and reveal previously untapped opportunities.

See also: Get more out of your data with HyperAutomation

RPA vs. Hyperautomation vs. Intelligent Automation

It is essential to understand the different forms of automation and how they differ before placing a bet on excessive automation. Robotic Process Automation (RPA) is the use of bots to automate a large number of highly repetitive processes that can be performed according to rules and often do not require human skills. Payroll processing is a great example of this. It entails a number of straightforward but crucial procedures, including checking schedule entries, attendance, earnings, reimbursement, taxes, and resignations, among others. RPA bots may reliably and quickly manage payroll data, check timesheet entries, manage employee leave for pay, etc., when setting distinct rules for each.

On the other hand, intelligent automation uses technologies like RPA, machine learning, natural language processing, business process management (BPM), artificial intelligence, and BPM to execute business processes without the need for human intervention. Intelligent automation, unlike RPA bots, uses AI and ML algorithms to analyze the process and make judgments about it. For example, consider how the customer service department manages emails. Each email can be classified, its contents summarized, personal correspondence distributed, and communications addressed to the appropriate parties. On the other hand, RPA bots may read emails, reply to them, and download attachments according to predefined criteria.

See also: How forward-looking organizations can get started with excessive automation

Industries subject to digitization with excessive automation?

There are plenty of opportunities for innovation. Many industries, including banking, insurance, healthcare, manufacturing, retail, and education, could use excessive automation.

Here are the top use cases for hyper-automation across industries:

Customer service: Looking at the customer service desk, which receives hundreds of emails a day, imagine how long it would take for an individual to manually respond to each email. Natural Language Processing (NLP) can be used to recognize language, ascertain tone, understand context, and automatically route emails to the appropriate individual.

file processing: For every industry, manual document processing is a challenge. Documents such as invoices, bills of lading, purchase orders, receipts, payment slips, medical records, and prescriptions must be handled differently depending on the industry. Companies can use hyperautomation to automate the processing of these sheets.

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Companies handle a wide range of documents, including referral pages, purchase orders, invoices, medical records, and receipts. Hyper Automation can help automate end-to-end document management, including data extraction, validation, and sorting/labeling in the appropriate format, using OCR capability.

Data reveal: The organization frequently manages large amounts of data. Companies may rely on excessive automation to perform these tasks accurately, whether they are collecting customer data from emails, order requests, etc., into a central application or evaluating data using machine learning models to detect questionable accounts.

KYC and fraud detection: Superior automation presents a great opportunity for the banking sector to succeed. Regulatory reporting, marketing, sales, distribution, banking, payments and lending, back-office operations, and corporate support are a few of the industries that would benefit the most from hyper-automation. Intelligent character recognition technology, for example, allows electronic data to be entered into the appropriate fields of KYC portals from manually set-up multifunctional KYC forms. Additional copies of this data are uploaded to the linked systems. Al-powered intelligent automation systems can effectively check exchanges and proactively identify fraud and criminal activities. Advanced modeling methods were used to create an AI-based machine learning model that can predict the possibility of malicious transactions and, as a result, reduce or eliminate risks. Anti-Money Laundering (AML) technologies are widely used nowadays, and they have an impact on the suite of hyper-predictive and prevention automation innovations.

Billing management: RPA bots collect incoming documents or emails. Semi-structured or unstructured data may be included in this document or email. With a machine learning model, a document or email is processed to extract data that machines can read. Rules or ML models are used to validate machine-readable data. For example, invoices can be checked to ensure they comply with VAT regulations or are not fake. Receiving, processing, and disbursing invoices from vendors who provide goods or services is involved in the onerous accounts payable process. This is usually a very lengthy process with a high probability of errors. Companies may automate key AP operations by combining the dual powers of RPA with machine learning and document extraction technologies, which include optical character recognition (OCR).

Travel and Expense Management: Hyper-automation can be leveraged to automate the tedious paperwork and repetitive processes involved in Travel and Expense (T&E) processes. Collecting paper travel expense receipts from employees, collecting data from receipts, determining whether receipts meet company expense standards, making payments, or requesting clearance for items that don’t comply with expense policies – it can all be simplified with hyper-automation.

Document data extraction: Claim evaluation involves understanding and evaluating claims to see if they are in compliance with customer policies. Claims settlement includes: Automating legitimate claims processes. Occupational claims or workplace claims, also known as HR claims, are requests and claims that employees submit to a company’s Human Resources department for processing. These requests and claims include requests for leave, reimbursement of expenses, complaints, and more.

Order management: Order management entails actions such as collecting relevant emails and attachments to obtain data about customer preferences. Some of the options include placing a new order, updating an existing order, canceling an existing order, changing internal systems in response to the new order, making changes to previous orders, and responding to consumer inquiries.

future in the future

With excessive automation, the world will see huge digital targets being met. First of all, digital-native companies will start learning how to automate their entire end-to-end workflow rather than just separate standalone digitization efforts. HR is a good example. The entire process, including candidate selection, hiring, employee training and development, mentoring, attrition prevention, and more, may be digital to standardize best practices, increase productivity, and remove bottlenecks.

These are benefits that digital-native organizations that keep a laser focus on their hyper-automation plan will start to experience in the future, even though they won’t be experienced at scale. But it’s safe to claim that the age of hyper-automation is here. Therefore, companies need to prepare for the upcoming revolution and step up their digital game!

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