By Boopathy Rajendran, Senior Vice President, Delivery and Services, Vuram
Hyperautomation has pushed the boundaries of traditional automation capabilities. Rather than relying on a single technology or task automation, enterprises now can access a bundle of advanced technologies that enable process mining, intelligent business process management (iBPM), artificial intelligence (AI), robotic process automation (RPA), and machine learning (ML). It’s not only about combining technologies. It is about achieving the ideal technology process design to automate and provide a seamless customer experience.
According to Coherent Market Insights, the global Hyperautomation market is set to exhibit a CAGR of nearly 18.9% during 2019-2027. This is understandable as organisations are adopting automation technologies to go beyond the routine and be innovative. Let’s take a look at how Hyperautomation technologies change the way enterprises work?
Hyperautomation Toolkit Transforming Enterprises
Hyperautomation adds an orchestration layer to the typical automation technology. With human intelligence and technology going hand in hand, Hyperautomation has the potential to revolutionise the future of industries. Let’s explore some tools that comprise Hyperautomation:
1. Intelligent BPM
Traditional BPM platforms optimize business process workflows and improve outcome and efficiency. Low-code Intelligent Business Process Management (iBPM) augments the traditional BPM tool with intelligence and analytics at its core, making it the backbone of Hyperautomation. Combining AI, cloud capability, analytics, and complex events processing, iBPMS transforms business operations.
2. RPA
Robotic Process Automation (RPA) has taken out the necessity of spending countless hours doing mundane, clerical tasks that could be easily passed on to software bots. RPA bots operate processes like a human would, but faster, sparing time for humans for more effective work and brainstorming. This technology has transformed the way organisations function—be it invoice processing, account reconciliation, web scraping or auto-updating CRM records. The software bots reduce cycle times and improve throughput and accuracy, flexibility, scalability and effective utilisation of resources.
With the help of RPA, to process insurance claims—a complex process—all repetitive tasks can be automated: customer data for the claims can be processed by programming the bot on how to pull up data from particular locations and compile the report.
3. Doc AI
According to IBM’s estimates, around 80% of the world’s data is dark and unstructured. Doc AI can be used to interpret large volumes of dark data and pull valuable insights out of it. AI is a sequence of intelligent processes that help identify technologies like intelligent optical character recognition (OCR), natural language processing (NLP), and machine learning (ML). It helps organisations to become Insight-Driven Organisations (IDO). Some of its applications are conversational FAQ, form processing, contract management, reading bank statements and invoice processing.
4. Analytics
Analytics translates data to information across industries. It helps predict outcomes based on past trends and create suggestions to maximize ROI. Analytics platforms come with built-in dashboards where users can see the actionable insights that support decision-making in the business domains of the supply chain, customer, workforce, finance and risk.
5. Natural Language Processing (NLP)
NLP analyses semi-structured, structured and unstructured documents to identify, extract and use data within them for further analysis. Some applications of NLP include insurance claim handling, invoice processing, text classification, spam detection, contract analysis, contextual spell checker & slang detector, slang clearer, emotion detector, summarization, etc. Integration of NLP into RPA helps organisations enhance customer experience by analysing the sentiment in the text.
Hyperautomation: A Revolutionary Disruptor
In the retail industry, besides easing up the most basic tasks such as product scanning and inventory management, RPA takes care of the administrative processes as well. This results in better customer relationship management, cost reduction, advanced audit ability, better productivity, and much more. RPA can help companies predict the fluctuation in the market and prepare in advance to meet the evolving market requirements.
Further, in a traditionally automated business approach, there are multiple teams involved. Let’s look at this with an example: in a traditional order-to-cash process, communication through email can hinder transparency in order processing time and provide no visibility on bottlenecks. On the other hand, by integrating technologies like RPA and intelligent document processing, enterprises can gain end-to-end visibility of the process including bottlenecks and missed SLAs. Humans intervene very minimally compared to the traditional process.
Hyperautomation is the current priority and every enterprise has to get on to this journey to achieve digital transformation and continuous improvements for better outcomes. Organisations across various sectors are moving beyond their dependence on manpower to achieve digital transformation and enhance human capabilities for better outcomes. Hyperautomation is the key driver of this change.