Hyperautomation: Driving Large-Scale Adoption in the Digital Era

by Daniel Raymond · December 4, 2022

Chaitanya Pathak, a veteran technology leader with over a decade of experience in product innovation, offers an in-depth analysis of hyperautomation as a powerful engine of digital transformation.

Table of Contents

Currently the Chief Product and Technology Officer at LEAPS by Analyttica, Pathak paints a picture of the unprecedented opportunities modern organizations have to integrate multiple advanced technologies, which today span from AI and machine learning to robotic process automation, to automate complex processes at scale, bring data to the democratized age, and drive significant innovation in all levels of business operations.

Introduction

Today, organizations of all sizes look for ways to consolidate operations, boost efficiency, and spark innovation. For Chaitanya, hyperautomation is the magic bullet that does all that and more. It combines several technologies, including workhorse tools like RPA (robotic process automation), AI (artificial intelligence), and machine learning, to do something quite different from what any of the ingredients do on their own: automate multiple complex business processes at scale, at the same time. Chaitanya believes this has vast implications for several industries and that we need to be ready for this dramatic shift.

Understanding Hyperautomation

From RPA to Intelligent Automation

In order to fully grasp the concept of hyperautomation, it’s essential to understand its evolution, as laid out by our expert:

Robotic Process Automation (RPA): In Chaitanya’s view, this is the foundation of modern automation. It focuses on automating repetitive, rule-based tasks that are completed by human workers today.
Intelligent Automation: Using software “bots,” RPA took over these tasks to perform them much faster and far more accurately.
Hyperautomation: Built upon the previous two “eras”, companies engage in hyperautomation to move on to the next frontier, enabled by natural language processing on an unprecedented scale.

Defining Hyperautomation

Chaitanya defines hyperautomation as the synchronized and integrated use of multiple technologies. Among these are Artificial Intelligence, Machine Learning, Robotic Process Automation (RPA), Business Process Management (BPM), Natural Language Processing (NLP), Optical Character Recognition (OCR), and Advanced Analytics.

For example, a hyperautomation solution in the healthcare sector might employ RPA for data entry, AI for diagnosis assistance, ML for predictive analytics, and NLP for processing patient records, all working together seamlessly to improve patient care and operational efficiency.

Key Components of Hyperautomation

Chaitanya pinpoints several crucial components:

Process Discovery: In his framework, these are AI-driven instruments that dissect workflows to identify domains where automation could be applied.
Advanced Analytics: Chaitanya stresses the importance of using big data and machine learning to extract useful insights and guide decisions.
Intelligent Document Processing: The expert calls this “OCR plus NLP”. It’s used to extract and handle the valuable information from documents that don’t have a clear structure.
Low-Code/No-Code Platforms: Chaitanya sees these as a way for the average person to build automation solutions without needing to understand software engineering.
AI-Powered Decision Making: To our expert, this means using smart algorithms to make decisions that are too complex and too important to leave to humans.

The Impact of Hyperautomation

Empowering Citizen Users

Chaitanya believes that hyperautomation brings advanced technologies to the non-technical user, enabling bottom-up innovation. For instance, he cites the AI-powered analytics tools that allow marketing professionals to segment customers with complex criteria. These tools have a familiar, user-friendly interface. They don’t require the user to know Python or R. For many conceivable cases, there is an accessible interface that allows a non-technical employee to achieve extraordinary results for the company.

Intelligent Decision Making

Chaitanya describes hyperautomation as being able to make autonomous decisions, thanks to the combination of natural language processing, advanced analytics, and machine learning. Take, for instance, an example pertaining to that favorite domain of financial services. He suggests that decision systems could analyze a company’s report in conjunction with market behavior and a certain investment trend. Then, the system could analyze the same material and use experience-based reasoning (think deep learning) to determine what the right move is for its investment advisor client. And it could do this in real time.

Adaptive Workflows

Chaitanya points out that, contrary to conventional automation, hyperautomation generates dynamic workflows that can respond to alterations in their operating environment. In anatomical contexts, for instance, he describes hyperautomation this way: an AI system can manage and adjust a production line in real-time, radically changing the arrangement of parts in the line as supply chain disruptions, demand fluctuations, or equipment performance issues require it to do so.

Enhanced Knowledge Management

Hyperautomation is very good at capturing and codifying tacit knowledge within organizations, says Chaitanya. For instance, an AI-powered system can analyze patterns in how human employees solve complex problems. That knowledge can be used to train new employees or to automate future problem-solving.

Scaling Adoption: A Case Study of LEAPS

Chaitanya explains that LEAPS, a SaaS product that centers on data literacy, is harnessing hyperautomation to not only amplify its reach and impact but also to ensure that more and more people get to experience its goodness. Hyperautomation, in a nutshell, is taking automation to the next level, or rather, to the topmost level.

As part of LEAPS, our expert has constructed a coherent hyperautomation architecture that encompasses:

Graph RAG (Retrieval-Augmented Generation), for comprehending user context in real time, using historical data and domain rules.
Natural Language Interfaces that allow users to interact with the data using conversational prompts.

Action-based Recommendation Engines that suggest next steps and analyses for users to consider, rooted in their behavior and data patterns.

Automated ML Pipeline Generation: This means creating and executing machine learning models for mere mortals as specified by them.
Automated Dashboard Creation: Generating visualizations and reports that are, at least in theory, customized to every individual user.

Chaitanya believes that the LEAPS framework permits an end-to-end workflow automation for scenario identification, synthetic data generation for testing and training, ML model construction and deployment with minimal user intervention and automated, context-aware dashboard and report production.

The Future of Hyperautomation

As businesses move toward the digital transformation of their ecosystems, Chaitanya sees hyperautomation taking on an increasingly pivotal role. He identifies key trends:

Integration of Generative AI: This, according to our expert, is all about using really large language models to enhance not just the natural language interactions people have with hyperautomated processes but also the content generation that happens right within the automated workflows themselves.
Edge Computing in Hyperautomation: If hyperautomation is bringing automation ever closer to the places where our data lives for real-time processing, then edge computing is its next logical step.
Cross-Functional Process Optimization: For Chaitanya, this is exactly what it sounds like: taking all the different processes that exist in various departments and stitching them together into a single, functioning whole.

Conclusion

Chaitanya thinks of hyperautomation as a new digital transformation. It can use commonplace processes, like RPA, in combination with AI, ML, and other advanced technologies, to drive efficiency, innovation, and growth. LEAPS seems to think very similarly. RPA powers LEAPS. It also uses AI and ML. But LEAPS is more about all these (and some other) technologies working together (which mostly seems to be what people mean by hyperautomation).

For the future, Chaitanya foresees an emphasis on developing hyperautomation solutions that are centered around humans. These solutions will augment, not replace, human ability and ensure that humans and technology can work together in a digital society that corresponds to the problems of today and those we anticipate in the future.