Automation has firmly established itself at the forefront of digital transformation across enterprises and industries, and by the looks of it, it is going to be ubiquitous very soon. Every organisation is looking to add automation capabilities to their functions and processes to stay ahead. Robotic process automation (RPA) has been increasingly used to streamline and optimise business processes. It is delivering results across various domains, from logistics and retail to manufacturing and banking. Such is the widespread adoption of RPA that it is predicted to become a $2.9-billion market by 2021, according to a recent study by research firm Forrester.
RPA is still used for most activities which are time-consuming and repetitive. The automation software is programmed to perform tasks based on a set of rules. With the volume of business increasing by the day, there is a rising demand for automation capabilities that can scale up and add more processing power to services, along with computational power, to handle the massive amount of data. But what happens when business processes are complicated, with unstructured and dynamic data?
With artificial intelligence, machine learning, and natural language processing, businesses have more options for automation. This means adding intelligence to automation, and with it comes the ability to learn, analyse, take decisions and even make suggestions to improve processes just like a human would. It helps organisations to navigate through complicated situations without compromising on speed, efficiency and decision making. Intelligent automation, the appellation of next-generation automation and which is also interchangeably called cognitive automation, brings in a degree of savoir-faire to help businesses grow.
What makes automation intelligent?
The building blocks of any cognitive automation RPA platform are as follows:
· Optical character reader – helps capture visual data from multiple inputs such as documents, receipts, emails, images, etc.
· Natural language processor – converts human inputs to machine readable instructions, and processes pattern matching, rule-based computation, information gathering and retrieval, etc.
· Machine learning module – read, understand and recognise images, text, and other forms of inputs.
· Data storage solution – stores massive volumes of data encountered across multiple processes.
· Deep learning module – helps the platform to learn on its own, powered by an artificial neural network.
· Training data interface – to teach the platform how to perform simple and complicated tasks
How does this help intelligent automation?
When dealing with processes that involve a lot of unstructured or dynamic data, an intelligent automation platform will understand the variations since it has learnt from past data and can make decisions based on a situation. It understands the semantic meaning of documents/unstructured data and makes or assists in making more complex decisions and executes actions.
Enterprises can get the best out of a cognitive RPA solution when it is self-learning and can be scaled to handle any type and volume of processes. Such a solution can also automate the most complicated tasks easily and can process and provide insights into unstructured data. This helps enterprises vastly improve the speed, efficiency, and processing power of their processes.
During our conversations with some industry leaders across various forums in recent months, we realised that existing RPA users have already adopted or are going to adopt cognitive RPA or intelligent automation in various processes. Some who are new to RPA are starting out with cognitive RPA rather simple rule-based automation. Usually, enterprises first adopt rule-based automation and then later venture into intelligent automation. This results in a lot of operational and transitional inefficiencies. It is definitely interesting to see how enterprises are still competitive in order to get an early footing in the intelligent automation curve.
Intelligent automation holds a lot of promise for businesses to scale and power through to Industry 4.0. It opens new opportunities as a lot of innovation is taking place in this area and this helps enterprises achieve faster and better results than ever imagined before.
Kris Subramanian and Rajmohan Harindranath are co-founders of Bangalore-based automation and data analytics products company Option3.io.
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