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Must Know - Hyperautomation Components in 2022

October 13, 2021

In the digital native economy, businesses across all verticals and geographies are constantly trying to recalibrate their process workflows through new technology adoption. Intelligent automation, artificial intelligence is helping organizations to alleviate financial challenges and accelerate digital transformation ambitions.

Running and operating legacy systems, businesses often incur fixed recurring costs which involves plentiful human effort to ensure smooth operations. Building a roadmap for an entire system overhaul can often hit multiple bottlenecks. As a result, digital transformation executives are often skeptical - when considering a complete overhaul. It may affect business continuity, which may have a tangible impact on its goodwill and negatively impact bottom-line growth.

“By 2022, 65% of organizations that deployed Robotic Process Automation will introduce Artificial Intelligence, including Machine Learning and Natural Language Processing algorithms.” – Gartner Research

Hyperautomation is not simply an extension of automation – traditionally driven by Robotic Process Automation (RPA) software initiatives. The primary objective of a Hyperautomation core is business process automation; it accomplishes it by fusing automation with the new cutting-edge technologies - artificial intelligence (AI), machine learning, process mining, data analytics, etc.

Hyperautomation components have provided executives with an integrated suite of technology tools that address most technology adoption challenges packaged as a single product that works seamlessly within the existing IT environment.

Process Automation

Process automation software helps enterprises to automate manual, repetitive, and time-consuming tasks. It mimics human actions to get the job done. As a result, repetitive tasks are completed faster and more efficiently than a human agent. In addition, automation software delivers a lot of advantages through seamless integration into legacy systems. But often, the automation deployed is rule-based and has no judgmental quality of its own. They are initiated in response to a preconfigured set of commands. Automation is core in a Hyperautomation environment, the limitations of native automation are offset by the other technology components that Hyperautomation offers.

Intelligent Business Process Management Systems (iBPMS)

An iBPMS software system focuses on multiple processes within existing lines of business within an organization. It identifies opportunities to increase business process efficiencies by deploying automation. iBPMS systems can often use predefined business rules to model, implement and execute sets of activities across business processes. These systems often integrate into enterprise applications (e.g., ERP, CRM, etc.) and provisions end-to-end support to the entire business process.

Machine Learning (ML)

Using Machine Learning technology, businesses can build models through which systems can learn automatically. Data is often passed through the models, which generates insights. The gains which ML models can deliver are limitless when deployed across business processes. These models can scan large data sets, detect patterns, identify key characteristics, and give decision-making recommendations to a regular business user. All of it can happen in real-time, making ML a powerful technology tool for businesses.

Natural Language Processing (NLP)

Natural language processing (NLP) is a discipline of Artificial Intelligence technology that can comprehend and interpret human language both as speech or text. NLP technology can classify human utterance and understand its tone, intent, and action directive. Based on this, it can perform a wide range of activities. NLP technology has multiple use cases. For example, a conversational interface could help with query resolution, and even a more advanced version can be used to understand extracted data from documents. It can also be used to understand sentiments, language translation, and categorization of textual information.

Process Mining

Process Mining technologies analyze large sets of data mined from business processes. This data is generated when humans and systems interact inside a live business process. The data is often associated with a timestamp based on events for future analysis purposes. Process owners can analyze processes from the event log to identify trends and patterns in the process. The analysis is used to generate actionable insights that can help increase process efficiency and performance.

Advanced Analytics

Advanced Analytics involves the deployment of algorithmic models to analyze data or content with the ambition of deriving meaningful insights. Businesses can do a lot with advanced analytics - Identify meaningful correlations across different data points, understand dependent and independent factors, make forecasts and predictions, and finally provide actionable recommendations to business process owners.

Learn More - Beyond Automation: The Hyperautomation Edge vs Conventional RPA [Whitepaper]

About the Author

Aritro Chatterjee, Product Management, Kanverse.ai

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