‘Hyperautomation’ ranks #1 in Gartner’s list of top 10 strategic technology trends for 2020.*
According to Gartner, “Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)”
To simplify, Hyperautomation extends the automation journey that you start with robotic process automation (RPA) by adding complementary technologies, including cognitivetools. The combination of these powerful technologies allows you to automate a higher percentage of end to end processes with less need for human labor to handle exceptions.
Analysts are projecting that the adoption of RPA and AI technologies will continue to surge, which we believe is fueling the Hyperautomation technology trend. According to Gartner’s recent report Competitive Landscape: Robotic Process Automation Software [Gartner subscription required] published on 13th May 2020,**
“By 2022, 80% of organizations that deployed RPA will introduce artificial intelligence (AI), including machine learning and natural language processing algorithms for improving business processing activities.”
“By year-end 2023, 90% of large and very large organizations will have deployed some form of robotic process automation (RPA), up from 55% in 2019”.
Getting started with complicated new technologies like this can be difficult for many businesses due to the complexity and variety of options available.There may be a tendency to hold back, believing that Hyperautomation is only suitable for early adopters. However, it can be a great starting point for any organization that is looking to start its automation journey.
Let us talk about a prevalent business challenge of managing data-intensive processes manually and understand how Hyperautomation can be used to overcome that.
Estimates indicate that 80% of enterprise data is unstructured and that number is predicted to grow by 55-65 percent each year. Unstructured information can be textual or non-textual, human- or machine-generated (e.g., emails and information from other communication channels, business application data, media files such as images, videos, audios, etc.).
For most businesses, much of this unstructured data gets generated from field employees and partners (e.g., customer on-boarding, know-your-customer (KYC), vendor registration, vendor commissions, etc.). Many organizations have deployed cognitive tools to convert the unstructured data into structured data, but it still requires time and effort from their valued resources to check and cleanse the structured data. This adds to their infrastructure requirements and human capital cost.
Extending Hyperautomation to field employees and partners can help organizations across verticals to save huge back-office costs. Let us look at a few specific use cases.
Many financial institutions have established large compliance departments to fulfill their Know-Your-Customer (KYC) and Client Lifecycle Management (CLM) obligations. Thousands of employees are engaged in manual and repetitive customer due-diligence and onboarding tasksin order to meet this need.
Many insurance companies employ field agents to onboard customers using handheld devices. The agents, across different regions, capture customer details and documents and send them to their central office for further processing. The back-office staff gets hundreds of customer records daily for manual data entry and verification against multiple supporting documents. This manual process takes several days and can be very frustrating for a new customer that is going through the process.
Hyperautomation can be used to eliminate the back-office manual tasks of data entry and document verification. Once the agents upload/capture documents in a mobile application, data is intelligently extracted in real-time and entered in a predefined form. The data can be reviewed and corrected upfront. After the data is submitted, the subsequent process can be triggered in the backend. Such intelligent and real-time automation significantly reduces the chances of fraudulent cases.
Loan processing involves multiple steps, including pre-qualification, application, estimation, document validation, credit reports, appraisals, underwriting, and closure. It is usually a paper-intensive, time-consuming, and error-prone process. A mortgage company receives numerous loan applications daily through many sources. Each application can have hundreds of documents to process. Manual mortgage loan processing has huge risks related to lost documentation, costly data entry errors, and compliance violations. Additionally, a high cost of loan origination and an accelerated average time to closure cause a significant reduction in profit per loan.
Automated and well-integrated workflow can help in closing loans in record time. With Hyperautomation, businesses can automate data extraction from various sources, streamline data entry into multiple discrete systems, and get enhanced visibility into the end-to-end process.
There are several benefits of Hyperautomation for automating data-centric processes.
If you are interested to learn more about how to utilize Hyperautomation involving field force and save back-office costs, join our upcoming live webinar. Alan Hester, President at Nividous, will talk about the unique capabilities of the Nividous RPA platform and its business use cases.
*Smarter With Gartner, “Gartner Top 10 Strategic Technology Trends for 2020,” October 21, 2019. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020.
**Gartner, “Competitive Landscape: Robotic Process Automation Software”, Fabrizio Biscotti, Cathy Tornbohm, Bindi Bhullar, Arthur Villa, 13 May 2020.