Industry 4.0, a global phenomenon triggered by new-age technologies, needs a unified approach beyond simply RPA. To revitalise, let’s welcome the age of hyper-automation.
By 2024, Gartner predicts that enterprises will cut operational costs by 30 per cent by combining hyper-automation technology with revised operational processes. The pandemic has dramatically hastened the adoption of digitalisation; businesses have already witnessed the fruits of automation, and now the leap towards hyper-automation further fuels digital transformation. Enough beating around the bush; the term hyper-automation needs a clear introduction.
Simply put, it refers to a combination of complementary sets of tools that can integrate process and functional silos to automate and enhance business processes. At its core, it starts with a business-driven approach with RPA (Robotic Process Automation). It combines artificial intelligence, process mining, analytics, machine learning and other cutting-edge tools to broaden the scope of automation. As a result, the approach ultimately helps businesses deliver greater value and impact.
“The shift towards hyper-automation will be a key factor enabling enterprises to achieve operational excellence, and subsequently cost savings, in a digital-first world,” said Ms Cathy Tornbohm, Research VP at Gartner.
Limitations with the existing tech stack
RPA adoption is rising and is expected to reach $5 billion by 2024. Although RPA gained traction and the process is effective, it encompasses specific challenges that hyper-automation can take care of.
First, there is a need to understand the entire business process thoroughly, and simple automation is ineffective as it misses the criticalities at the beginning leading to the under-realisation of automation objectives. Most organisations mistook high ROI as synonymous with higher workforce release. Instead, the focus should be on how to free up the employee time spent on menial and repetitive tasks, allowing them to concentrate more on crucial, high-value deliverables directly related to business objectives.
Second, different teams use different automation tools for different purposes. With this scattered setup, organisations find it difficult to track their automation programme adequately. This difficulty is due to an inconsistent technological stack that often leads to friction between functions. As a result, the firms are unable to focus on areas for improvement due to a lack of a governance system that is effective in assessing and evaluating automation initiatives; hence, low business user satisfaction.
Third, simple business process automation takes away the chance to improve processes by locating inefficiencies. It is crucial to note that process analysis, redesign, and mapping work are vital to successful automation adoption. The current business process is frequently unnecessarily complicated and contains extra processes.
Hyper-automation to rescue
Many businesses have automated monotonous tasks using robotics to adopt more digitised working methods. These organisations are now attempting to scale these solutions with deeptech in an effort to break out of the mould and be more creative. Hyper-automation helps achieve this efficiency with capabilities including;
Simplification of work processes: With the power of AI and ML, the workers are now free from many repetitive and manual tasks with a larger focus on quality work. For instance, customer service representatives can reduce the volume of inquiries when an AI-powered chatbot can resolve the majority of them. In addition, companies can now use recruitment assessment platforms like Aspiring Minds, HireSelect, etc., that can aid in vetting a person’s cognitive capacity, language abilities, and professional expertise. Cut short, from the onboarding process to handling multiple tasks, the organisations will have more time to focus on the quality aspect.
Freedom from complex coding: Developers don’t have to spend much time creating a product from scratch when there are publicly available code libraries like ML. The operating environment can be used to modify the default code. After the no-code and low-code, the time is to move a step further. With Salesforce’s Codegen – a large-scale language model, speaking to the machine and having conversational AI produce code for the user has become possible. These models, which are often referred to as “programme synthesis” or an “AI pair programmer,” translate dialogue (the “natural language”) into codes. This goes far beyond autocomplete, which has long been popular and merely completes statements while minimising mistakes and errors.
Time-saving: Employees can eliminate manual and pointless tasks like data entry with the help of hyper-automation technologies so that they can instead concentrate their efforts on fundamental developmental activities like innovation. For example, AI technology like Optical Character Recognition (OCR) can extract text from images and documents. OCR in RPA helps businesses automate a larger portion of their daily operations, especially those that still heavily rely on scanned documents, like forms that customers fill out.
Further to these capabilities mentioned above, several other benefits exist. Process mining uses knowledge extracted from event logs that are easily accessible in application systems to find, monitor, and enhance real processes. Fusing the BPM (Business Process Management) and RPA platforms enables conformance testing and automated process discovery. Finally, applying advanced analytics to process automation help generate data to unlock business insights to prepare the future roadmap. However, some misconceptions exist that need to be warded off.
Clearing misconceptions
Hyper-automation must not be confused with the technology aspect; it encompasses more than just using tools to manage processes. Instead, it asks for collaboration between decision-makers and their know-how to use technology to evaluate data and apply logic.
Consider a simple situation involving a social media firm and its urge for consumer retention. A company can rely on RPA, AI and ML-based systems to generate reports and gather information from social media sites to comprehend consumers’ tastes and preferences. The tech stack will help produce a report and give a simplified view to the marketing team. However, to retain happy consumers and address the concerns of dissatisfied ones, with these simpler insights, the marketing team will then plan to evaluate what kinds of campaigns, promotions, and incentives should be rolled out in a business strategy.
Summing Up
Organisations have the power to completely transform their core business activities and contribute to Industrial Revolution 4.0 using the technology capabilities already available with hyper-automation.
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