Although the term “hyperautomation” is only a few years old—it was coined in 2020—it has quickly become one of the biggest subjects across many industries. Automation was already a hot topic. Investments in new technologies made by companies worldwide stretch well into the billions of dollars. According to Gartner, the firm that originated the phrase “hyperautomation,” the term refers to what they see as an “inevitable” future for global marketplaces.
It’s a future where automation is no longer a modernization process, but rather an ongoing evolution that never stops.
In this post, we’ll look at the key facts about this evolution, including:
- What is hyperautomation?
- What forms the backbone of hyperautomation technology?
- Eight hyperautomation use cases
What is Hyperautomation?
How do we define hyperautomation more specifically? Sometimes meaning “automating automation,” hyperautomation comprises a wide range of technologies working together in a unified environment to eliminate wasted time, avoid costly errors, save money, and ultimately deliver superior outcomes with less human intervention. Hyperautomation examples combine tools and platforms, from AI to BPM software, to enable organizations to identify candidates for automation—and to transform those candidate processes into an overhauled and largely automated solution.
Contrary to what some may think, the value of human capital within an organization increases with hyperautomation. Instead of dedicating many hours of work to processes that consume vast amounts of time, end-to-end automation enables those employees to devote their attention to much more valuable work. When robots and automated systems handle everything from simple tasks to more complex, data-driven decision-making, humans working in concert with them have far broader capabilities than alone.
What Forms the Backbone of Hyperautomation Technology?
Before we dive into some concrete examples of how hyperautomation for business and IT processes works in the real world, it’s important to lay out the technologies that underpin these efforts. Remember, hyperautomation arises from an intelligent, structured combination of multiple technologies to bring whole business processes into the future. What are the technologies driving today’s digital transformation?
- Robotic process automation
- Business process management software
- Low or no-code platforms for citizen developers
- Machine learning and artificial intelligence for intelligent automation
- AI/ML-enabled technologies, including optical character recognition (OCR), natural language processing (NLP), image recognition algorithms, and more.
At the intersection of all these technologies lies the power for automating complex tasks.
8 Hyperautomation Use Cases to Consider Today
How is hyperautomation embraced in the marketplace today? Not every company aggressively pursuing automation recognizes what they do as hyperautomation, but their actions often fit the definition. Some major companies, such as CVS, have already adopted advanced systems to manage an increasingly complex, multi-faceted business. What are some other hyperautomation examples seen throughout the business world today?
1. Transforming the Accounts Payable Workflow
The accounts payable process is fundamental to a company's ongoing operations. If you don't pay your vendors on time, your business suffers in more ways than one. Although AP has been a candidate for automation for some years, today’s rich set of available tools is ideal for hyperautomation.
Consider the many steps you can overhaul. Upon receipt of purchase orders, invoices, and other paperwork, OCR extracts the information and provides it with structure in a digital system. Machine learning adjusts and adapts to understand different paper formats, while humans can still influence the process when necessary. ML/AI tools pass AP information to the relevant systems, and rules-based RPA can automate approvals and payments.
2. Overhauling Insurance Claims Handling
Claims administration is the most important work an insurer does because it constitutes the major way customers interact with the business. Streamlining this process using hyperautomation can yield better business and consumer outcomes. Again, OCR plays a key role in data extraction, while NLP and AI chatbots can guide the insured through filing a claim.
Behind the scenes, you can automate checking a policy against a claim. Use machine intelligence to identify when a policy can pay a particular claim. Once validated, robots can make the payments, too. Hyperautomation makes claims administration much faster and more hands-off.
3. Streamlining HR Claims and Processes
Human resources is a department with processes exceedingly ripe for automation on an end-to-end basis. Many essential elements, such as time tracking through digital punch clocks, have already undergone some aspect of automation. Using multiple technologies to stitch these processes together can give an HR team more time to deal with pressing issues.
For example, does HR need to dedicate hours to checking everyone's schedule and ensuring that a vacation request is OK to approve? What about sorting business expense receipts and entering them into a computer system for approval and reimbursement? OCR, NLP, RPA and AI can all combine to automate those processes almost entirely.
4. Rethink Document Processing and Classification
Handling documents can be challenging in any business, especially in the healthcare, law, and government sectors. This is particularly true when those documents might contain sensitive information that requires handling with care and due diligence. For example, consider an organization that frequently needs to ensure compliance with document storage or sharing requirements.
Usually, a human would need to handle the whole process—creating a digital copy, securing it, or redacting sensitive information as required. With hyperautomation, virtually the entire document processing system can improve. With ML and AI, these systems can learn what information qualifies as sensitive, automatically redacting or encrypting documents based on complex rules.
5. Customer-Facing Hyperautomation Leads to Better Outcomes
We've mentioned the potential for hyper-automating customer-facing applications in the insurance industry, but the potential can extend even into spaces such as retail. Chatbots powered by natural language processing provide a human-like experience. AI and ML use training data and knowledge to use the conclusions drawn by NLP to trigger the appropriate processes or to make smart suggestions.
Consider how a chatbot might guide a user through a support issue. It could understand the problem, provide solution options, and troubleshoot when one solution doesn't work. The bot could pass the user to a human for additional support when necessary. In this way, many customer service applications are perfect hyperautomation candidates.
6. Onboard Customers More Quickly
Some industries, such as banking, can't simply open new customer accounts randomly. Instead, banks and other financial institutions face extensive "know your customer" requirements. Without KYC compliance, it would be possible for money launderers, criminals, and others to subvert financial regulations more easily. KYC is typically a burdensome process that, in the past, has required substantial human intervention.
Today, a hyperautomation strategy means gathering customer data, extracting it to proper systems, and identifying potentially suspicious information can all happen with very little human interaction. When a human does need to come back into the loop, the system can automatically send alerts to the relevant parties and then continue the process after receiving feedback.
7. Stop Money Laundering at the Source
Related to the importance of KYC compliance, adhering to anti-money laundering (AML) laws is also critical for organizations that handle the finances of others. Money laundering can be very challenging to detect and traditionally requires skilled forensic accounting—but today, we can teach algorithms how to find suspicious or unusual patterns that could indicate bad behavior. When such activity occurs, the system could automatically halt or freeze that account until a human can review the facts. You could also streamline the creation of reliable audit trails and reporting with such tools.
8. Automate Forecasting and Ordering for Retail or Manufacturing
How much stock will you need to meet demand during the next holiday rush? When do you need to place an order to ensure that the turnaround time won’t be too long? In the world of retail and manufacturing, forecasting is a mission-critical process that underlies success. However, it shouldn’t be mere guesswork—but an intelligent process that provides human workers with the key facts they need.
A hyper-automated forecasting system would analyze reams of past and present sales data while also bringing in outside factors to consider. It could automate the ordering process or generate numbers for approval. Once approved, such a system might interface with the AP department to automatically issue orders and arrange for payment. The possibility of automating entire supply chains exists right now.
Embracing Today’s Opportunities for Hyperautomation
Hyperautomation opens the door to many pathways to impact processes across an entire business. As automation technology becomes more cost-effective and widely available, staying competitive may soon demand a focused approach to this emergent strategy.
The right tools and technology, backed by best-in-class support, will be critical for companies looking to step into the future. With an all-in-one platform from Kofax containing tools for RPA, applying AI, and more, embracing hyperautomation is more accessible than you may think. Learn more about these opportunities and explore what’s possible for your business to achieve today.