Automate Complex Business Tasks Through A Modular Approach In The Intelligent Automation Journey

Intelligent Automation market is growing at a CAGR of 40 per cent. It is a natural extension of the Robotic Process Automation (RPA) market through Artificial Intelligence (AI) / Machine Learning (ML) enablement. RPA requires a human in the loop involvement to some extent. With Intelligent Automation, the assembly achieves progressively increasing levels of automation by automating human decision making to achieve higher levels of straight through processing (STP).

It is interesting to note that Intelligent Automation is a congregation of RPA, AI / ML, Intelligent Document Processing (IDP), Cloud, Analytics, and other productivity enhancement tools. These components can be used in isolation or as an intelligent assembly to resolve the problem at hand and improve productivity and efficiency. The technology offers tremendous impetus to business by automating swivel chair operations that eat into the productivity and efficiency of highly skilled executives almost on a day to day basis as a part of their work.

A modular approach towards Intelligent Automation
The Intelligent Automation journey starts with a problem statement. It is driven by business requirement and not by hype. It is a solution to a business requirement. It addresses a business issue in the larger business context. A modular approach best suits here that allows to pick and choose the automation module in any desired sequence towards building an end-to-end automation solution.

• IDP: It is mostly used in a paper-intensive work culture. With its AI / ML enabled template free approach, it allows to extract and ingest unstructured data from documents, hard copies, scanned files, images, etc. The extracted data has high confidence levels and is in a structured format. It improves performance over a period of time through continuous machine learning. It can be easily integrated with the core business systems. As IDP has built in document pre-processing and post processing features, the data extraction is done with more than 99% accuracy.

• RPA: It not only automates repetitive tasks that cut across legacy systems, virtual machines, Citrix, ERPs, Cloud, web, etc., but also be used as intermediate links in a complex automation environment having workflows and other automation elements.

• AI / ML: Algorithmic layers allow to build continuous learning models that learn through the ongoing automation steps and the human in the loop exception management and improve over time. A combination of AI / ML and RPA can build end to end digital automation solutions by themselves.

• Cloud: It can be used as staging for storing the intermediate files created by OCR and IDP and also as data repository, as an automation platform, or for hosting business systems that are in the loop of the end-to-end automation. It offers high scalability to improved compute time to the entire automation effort.

• Analytics: It offers 360 degree analytics of the data generated and provides intelligent and actionable insights about operations at the click of a button. Do It Yourself (DIY) features and visual analytics allow you to drill down into the data at will, anytime anywhere.

Important use cases leveraging Intelligent Automation and RPA combination
It is interesting to note that a vast difference exists between point solution automation in smaller enterprise pockets and automation programs executed through an Intelligent Automation Centre of Excellence (CoE). A CoE led automation allows to reap benefits of scale and generate maximum throughput. It can be implemented seamlessly in a paper-driven ecosystem or a semi to a highly automated business entity.

Some of the important Intelligent Automation and RPA use cases include end to end automation of accounts payables, health claim document processing, operations in banking & treasury, and know your customer procedures.

Following are some of the important use cases in different industry domains that serve as a springboard to jump start the Intelligent Automation journey -
• Banking
• Financial Services
• Manufacturing
• Logistics
• Insurance
• Healthcare
• Telecom
• Government & Public Services
• Retail & E-Commerce
• Real Estate
• Travel & Hospitality
• Education
• Media & Entertainment

Advantages of Intelligent Automation
• Interoperable: Functions with all product platforms and technologies to deliver flexible yet robust automation solutions.

• Modular: Allows you to select the component as per business requirement and in any desired sequence.

• Continuous learning: Offers continuous machine learning through AI / ML algorithms and improved performance through each exception handling.

• Multi-structured data: Processes a wide spectrum of data from unstructured to structured to multi-structured, which is eventually integrated in the core systems.

• Universal recorder: Offers process automation that extends across multiple systems, from mainframes to Citrix to ERPs to Cloud.

• Reusability: Generates reusable components through each automation exercise.

Simply put
Intelligent Automation is a suite of products including RPA, AI / ML, IDP, Cloud, and Analytics. When implemented through a CoE approach to address the problem statement, it offers maximum throughput and allows automation of highly complex business cases. The reusable components generated can be used in other automation cycles throughout the enterprise. It offers high interoperability with different product platforms and technologies to generate robust yet flexible automation solutions.