By Vikram Goyal, Technical Architect, Vuram
The pandemic has significantly accelerated digital adoption among small, medium, and large enterprises alike. The technology sector has taken a quantum leap, with numerous technologies gaining immense popularity. Among them, one that has caught all eyes is hyperautomation. Hyperautomation is becoming a rage, gaining prominence, and is fast emerging as the go-to solution for businesses. Industry analysts state that the global hyperautomation market is projected to be valued at $600 billion by the end of 2022. Without a doubt, hyperautomation will be the driver of enterprise efficiency and gain a competitive edge in the times to come.
What is hyperautomation?
Hyperautomation is a business-driven approach that involves the use of multiple technologies like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), business process management (BPM), low-code, and other types of automation tools. Organizations implement hyperautomation for automating business and IT processes.
In simpler terms, hyperautomation uses combined automation tools and technologies to automate business processes and augment human intelligence. Hyperautomation automates knowledge workers’ activities by imitating four main capabilities: hear & speak, observe, perform, and understand. The goal of hyperautomation is to achieve business results via automated processes with very minimal to no human intervention. Hyperautomation helps organizations achieve improved efficiency, cost-savings, and quality, significantly enhancing the bottom line.
Four main capabilities of hyperautomation
Hear & Speak (Ears & Mouth of Digital Workers)
With this capability, machines can read, write, speak and interpret natural human languages. The underlying technology here is natural language processing (NLP), a branch of AI. NLP breaks down a human’s natural sentence into fragments to analyze the syntax and semantics. Further, with ML algorithms, NLP can interpret the meanings from the past “learnings”. NLP is commonly used in speech analytics, sentiment analysis, smart chatbots, and other unstructured information management. Some of the most common applications are machine translation, spell check, text extractions from websites, classifying customers’ reviews or feedback to assess sentiments, and more.
Observe (Vision of Digital Workers)
Let’s take the example of invoice automation. On average, manually processing an invoice can take around ten days and may cost anywhere between 10$ and 15$. How nice would it be if a bot could visually infer words, source, index, extract, validate, approve/reject, follow the configured workflow and pay the suppliers on time? And that is what “observe” is all about. Technologies like optical character recognition (OCR), which scans texts in documents for converting them into electronic files and intelligent character recognition (ICR) that recognizes fonts and different styles of handwriting and images and videos can generate insights.
Perform (Action of Digital Workers)
This capability is about executing specific tasks for which you program the bots, for example, automating data collection, data entry, filing applications and authenticating users, sending emails, preparing reports, data reconciliation, and more. With technologies like RPA and low-code platforms, it is possible to create intelligent, smart workflows.
Understand (Minds of Digital Workers)
Take the above instance of data collection. Once the data is collected, it needs to be analyzed for deriving key insights and decision-making. By leveraging machine learning, data mining and visualization, and big data management, data can be ingested into various databases, after which the required decisions are made.
For example, you want to generate a report on the trend of chocolate sales. With its “observe” capability, a bot can collect the essential data and intelligently derive the correlated parameters like vacation days, festival days, and important holidays. It then provides insights from the data using ML and data visualization to make decisions.
Customer Onboarding
Customer onboarding is a delicate process, which can make or break a business. Customer onboarding is challenging, whether it is for a bank with strict protocols, an organization with a week-long training, or a small company with just a brief instructional manual. According to Wyzowl Customer Onboarding Statistics 2020, more than 90% of customers feel that buying companies could improve their customer onboarding process.
A smooth onboarding process ensures you deliver significant value to customers while strengthening the relationship as the first step. In essence, only through this that your customers will genuinely become a patron of your business. So how hyperautomation can increase retention, ease the process? Let’s explore.
In a traditional approach, the following steps are done manually:
● Collect key documents and feed them into a centralized system
● Verify the documents against reliable or government databases
● Perform risk assessment for fraudulence
● Guide the prospects and answer their queries
Though the steps look simple, from a bigger picture perspective, the process is lengthy and complex; it involves coordination from multiple stakeholders from front-office, finance, compliance, legal, and more, each with its own rules.
The hyperautomation solution: Customer onboarding process leveraging hyperautomation
Typically, it takes at least 1-3 weeks for organizations to fully onboard a client. For corporate banking customers, the whole process takes 90-120 days, according to Oliver Wyman. According to industry statistics, automation can reduce the entire process time by 30-40%, potentially reducing costs by 25-40% over 18-24 months.
With hyperautomation at play, chatbots can welcome new customers and answer their questions 24×7. Application Programming Interface (API) facilitates communication between the applications in the workflow. In this case, API allows sharing data between the government regulator and the team involved in onboarding and collecting client information from other sources like social media channels.
To customize the services, analytics, and machine learning estimate and continuously update the risk levels and segment the client pool to customize services. Computer vision and natural language processing help intelligently process unstructured documents such as bills, contracts, and identity documents. Robotic process automation can support the execution of the overall process, performing all rule-based activities such as reconciling data, checking data, and sending emails. An example of such a redesigned process is shown in Figure 2.
Hyperautomation is gradually entering our lives and is transforming the way we live and operate our businesses. Reducing human intervention, this technology is no less than a boon. However, hyperautomation is not a magic wand to solve all complex problems. A myriad of criteria should be met to ensure successful implementation to reap the maximum benefits.
