Intelligent Automation: Acing the Digitization Game
Intelligent Automation is a collection of solutions that includes Intelligent Document Processing (IDP), Robotic Process Automation (RPA), AI/ML, and sophisticated analytics. To solve the issue statement or business requirement at hand, these products can be utilized alone or in combination.
It boosts the processes’ efficiency to new heights. Because of its process-intensive nature, the banking, financial services, and insurance (BFSI) industry are naturally the first to implement the technology. Businesses in other industries, such as manufacturing and logistics, retail, and others, are now moving to Intelligent Automation adoption for renewed inspiration to automate repetitive operations, thanks to its rapid adoption across the US, Europe, APAC, and EMEA.
Businesses can seamlessly improve revenue and savings year over year by utilizing Intelligent Automation.
Highly competent professionals typically undertake swivel chair operations, data input, validations, formatting, and other tasks on a daily basis as part of their work profiles, leaving them less time to focus on their core competency. Intelligent Automation automates the tedious, repetitive operations in this scenario, allowing leaders to focus on boosting business prospects. We now have a set of critical Intelligent Automation and RPA use cases to help medium and small businesses get started with Automation (MSMEs).
Intelligent Automation usage is expected to increase and grow at a CAGR of 40% as a result of this, sources suggest.
Strategies for Intelligent Automation Adoption
The best approach to establishing Intelligent Automation projects is to start with a problem statement and then deploy Lego-like automation components –
IDP allows you to extract unstructured data from paper documents and transform it into a structured representation, which is considerably more effective than OCR. It is capable of continual learning and works without a template. IDP provides more than 99 percent accuracy thanks to its picture pre-processing and data post-processing functionalities.
RPA’s universal recorder can automate medium to complicated processes and perform multi-way data transfers of the resulting structured data between legacy systems and ERP, cloud, Citrix, and other systems.
Through continual machine learning, AI / ML allows us to extend the same use cases further and go where no man has gone before, automating human decision-making and exception management.
The analytics module performs sophisticated analysis of various data points and delivers breakthrough analytics to help you make the most use of your resources.
Let’s now go through some of the major functionalities:
Various modeling is done in order to intelligently generate first-time-right (FTR) output with a high level of certainty. It checks each node’s data generation and transfers against pre-defined rules and master tables. It mostly focuses on exceptions that can’t be handled.
KYC, accounts payable processing, health claim document processing, and banking & treasury operations are some of the most common use cases for end-to-end automation.
Areas that demand a high level of compliance and strict adherence to deadlines are also great candidates.
Another extremely significant use case is trade finance document processing or end-to-end EXIM automation across geographically dispersed players. It takes advantage of the previously mentioned stack of reusable RPA use cases and serves as a springboard for holistically embarking on and thriving on the automation journey.
Originally published on The Tech Trend
