Evolution is a continuous process. And it is no different, when it comes to technology. As technology continues to evolve, companies have begun to understand its contribution towards increasing business efficiency and employee productivity. Automation has become an important factor to help businesses thrive in a highly-competitive market. Especially Robotic Process Automation (RPA), which has become the sure shot success formula for many first-time players in the sphere of automation, as a more viable and accessible option.
Having pervaded extensively into businesses in the past two decades, RPA has been successfully improving business performance and cost efficiency. And now, it is time to look beyond RPA. In this dynamic environment, automation also needs to go through certain evolution to continue to be fruitful to organizations. While automating rule-based processes is the first step, taking intelligent decisions is the next inevitable step in the automation journey.
Making Robotic Process Automation (RPA) Intelligent
To stay relevant in the market and be able to cater to the demands of the customers, enterprises are becoming increasingly aware of the need to align their strategy and investments to future-based technologies. To be able to do so, their automation requires cognitive abilities to comprehend the vast amount of structured and unstructured data, continue to learn, and intelligently automate processes.
What is Intelligent automation? An advanced version of RPA, essentially a software that mirrors the behavioral pattern of an end user to evaluate, calculate, transform and enter data into existing application fields as per the business rules can be termed as intelligent automation. It broadly constitutes of Machine Learning, Autonomics, Computer Vision and Natural Language Processing (NLP).
Machine learning is the ability of a system to automatically discover patterns in data and carry out predictions. This capability helps improve performance through systems that generate a lot of data over time. An everyday example of this would be Alexa, which is constantly learning from its environment and improving its responses.
Autonomics on the other hand, refers to systems, which perform routine tasks processed by humans. Today, it is becoming increasingly common in back-office operations performing high volume and rule-based tasks. It is predicted that autonomics will completely transform the Business Process Outsourcing (BPO) industry.
Computer vision is nothing but the ability of systems to identify objects, scenes, and activities as images. For instance, the face recognition software that are extensively used on mobile phones and social media platforms is based on this technology. Computer vision is widely used in security and fraud detection activities.
Natural Language Processing (NLP) helps interpret human language in the proper context to take appropriate actions. The most popular application of this technology would be Siri, available on iPhone and Mac devices. NLP is also being widely used for translation.
With these added layers of ML and other AI capabilities, intelligent automation functions differently from regular automation. For example, if you missed filling a section or entered wrong data in an electronic form, automated system would either reject the form or raise it for human intervention. Whereas, an intelligent system will identify and rectify the issue without human intervention. This self-learning ability and application of intelligence helps businesses function with much more efficiency and accuracy in terms of effort and duration, thereby enhancing the overall customer experience. Such advances in artificial intelligence, robotics and automation are becoming important for companies in all sectors to understand the impact and adopt intelligent automation or risk lagging behind.
How is Intelligent Process Automation driving improvements across value chain?
In today’s highly competitive and dynamic environment, businesses focus on customer experience. Intelligent process automation helps businesses in multiple ways to achieve their customer centric goals. Here are a few everyday examples how IA can unleash significant labor capacity while minimizing operational risk across the customer-facing facets through multiple capabilities.
Documentation: Most businesses have a lot of customer details. It gets difficult to segregate data as per the need and correspond with particular customers. Thanks to ML capabilities, an intelligent system helps understand the different requirements of customers, extract insights of the data and generate information accordingly.
Electronic mails: In an email, NLP helps comprehend the context and enables the system to carry out follow up action. ML helps collect data from past events and make faster and informed decisions. Also, the system is also enabled to draft ‘thank you’ mails to prospective customers after the concerned department has contacted them.
Raising Invoices: In case of delayed or forgotten payments, intelligent systems are enabled to send reminder mails to clients, customers, vendors and business partners.
Event invites: Invitations are sent to prospective attendees by tracking their location and analyzing their chances of attending the same.
Recruitment & Retention: An intelligent system makes recruiting easy for HR, by tracing incoming mails from candidates, reading and comprehending the same, scanning the resume and reverting to the right candidate. While the intelligent system does this, the HR can focus on higher-level tasks, which involve employee satisfaction and retention.
The main objective of Intelligent automation is to make machine more human-like. When organizations are automating non-deterministic tasks, they are able to make more informed decisions, without depending on language- or vision-based analysis, gaining a significant competitive edge. Intelligence in machines can extensively expand the scope of automation into newer areas, which are otherwise considered too complex.
While intelligent automation is still in its nascent stage, considered more like a theoretical concept, it is finally here, and will stay for good. The time has come to move from a mere deterministic and predictive automation to becoming more cognitive to assure inclusion of intelligence in your automation journey. With AI fast becoming the norm across businesses, leaders across business sectors would have to adapt AI sooner or later to remain competitive in the global market. So, is your enterprise ready to experience intelligent automation?