The difference between Intelligent Automation and RPA

How is Intelligent Automation different from traditional RPA?

Robotic process automation (RPA) follows rules to automate work that has no variation.

This is why AI-driven Intelligent Automation is superior to rules-driven RPA. Intelligent Automation integrates all the capabilities found in RPA, plus adds capabilities to process automation only possible through bots that learn and adapt to data in real-time. 

Machine learning, computer vision, text analytics and NLP process unstructured data, automate tasks that require judgment, and detect and adapt to constant change.

At the automation stage, intelligent automation applies AI to access the unstructured information, including human chat conversation, audio, and video, which is crucial in making end-to-end automation possible. Unstructured data represents 80% of all business data.

Analysing automation relies on AI to identify patterns in the process data and predict future productivity gains. Predictive and prescriptive analytics help leaders plan resources and set achievable KPIs, plus help ensure optimal operational results.

Artificial intelligence

It is the capability to automate content-centric processes that makes AI an ideal complementary technology to RPA. Using a combination of the two, organizations can automate processes end-to-end e.g. take in documents using AI, parse, classify, and understand meaning or sentiment and pass on the required action to RPA. Finish by composing an acknowledgement letter/text or email to the client using AI.

AI is the ability of machines to exhibit human-like intelligence. With advances in technology, computational power, and machine learning techniques such as deep learning, significant progress has been made in the field of AI. We have been able to create systems that can simulate human-like capacities and even outperform human-expertise in specific domains or tasks such as playing board games, and answering trivia questions. Such expert systems are referred to as Narrow AI.

NLP is understanding the structure of sentences, their meaning and intention through statistical methods and machine learning. NLP converts text into data and vice versa and allows meaningful interactions between humans and computers.

Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images, including scanned documents. It involves the development of theoretical and algorithmic basis to achieve automatic visual understanding.

ML refers to a set of algorithms that aims to enable machines to “learn” and improve their performance over time without being explicitly programmed, by passing large numbers of data points through the system.

Traditional automation benefits :

Cost reduction

Significantly bringing down the cost of many support functions across a company. 

Higher accuracy

Usually, automated tasks involve processing a lot of data, which people often find challenging, and thus are prone to many human errors.

Increased focus on core competencies

Automating tedious, time-consuming manual tasks allows employees to focus more on their core competencies. This also leads to considerably higher customer satisfaction ratings.

Improved productivity

Minimizing human errors means less time spent correcting them, which leads to higher productivity.

Better compliance

Actions performed by the bots are easier to audit, which makes it easier to track and correct shortcomings in processes.

Benefits of intelligent process automation :

.The compliance functions of enterprises from regulation-heavy verticals such as banking, insurance, and healthcare go through multi-step repetitive processes and need to navigate through complex IT architecture. Navigating this complexity and providing the depth and detail of reporting that regulators mandate is beyond the capability of standalone RPA products. Intelligent Automation helps complex organizations ensure adherence to quality and compliance.

Intelligent process automation combines the capabilities of robotic process automation and artificial intelligence, which allows it to deliver value to the company faster and provide additional benefits versus traditional automation approaches.

Organise and process complex data

Set up process to run successfully no matter how big and unwieldy the data inputs look. Intelligent automation tools capture, store, manipulate and retrieve records from unstructured sources.

Enhance customer experience

Improve customer satisfaction by delivering faster response times, greater accuracy, and more consistent results.

Reduced employee turnover

By freeing employees from monotonous manual tasks like copy-pasting data, automation can allow them to focus on more challenging tasks which in-turn leads to in turn, leads to better morale, higher employee satisfaction and better employee retention in the company.

Reduced employee turnover also means the company needs to spend fewer resources on hiring and on-boarding new employees and can allocate these resources to other important initiatives. It also ensures that important knowledge and skills are kept within the company and passed on to new employees.

Better insights into the business:

Intelligent Automation tools rely heavily on workforce analysis and analytics. Their main purpose is to analyze the automated processes to tune them and adapt to changing circumstances, thus maximizing ROI. This is one of the most important benefits of intelligent process automation as it allows the businesses to be proactive in their automation journey, analyze results and identify additional automation opportunities faster.

Applications of AI in the front office

A combination of computer vision and NLP is used to read and understand the meaning of information in unstructured content, such as text, emails, letters, and images, to identify, classify, and structure the information into data for further processing.

Use cases include handling incoming insurance claim forms and supplementary information such as photos, or collecting information in support of any application process such as a mortgage or a bank loan.

First line of support in the form of virtual agents and chat bots that handle client enquiries online. Use cases include handling IT or HR first line of support or answering questions from visitors to e-commerce websites.

Applications of AI in the back office

Any process that includes both structured and unstructured data e.g. invoice processing that often requires scanning of documents and capture of data from the scanned images. Much of the data capture can be automated by using software based on AI technologies such as computer vision, ML, and NLP.

Loan servicing requires a great deal of cross referencing of data between forms and multitudes of supplementary documents to ensure data quality and accuracy, compliance with regulatory requirements, and fraud and error prevention.

Collecting information from thousands of documents, held on-premises or on the web, and identifying key data, e.g. company financial data from returns and announcements in the financial services industry and legal discovery processes.

References:

https://www.workfusion.com/blog/benefits-of-intelligent-automation-ia-vs-rpa

https://www.auraportal.com/what-is-intelligent-automation/

https://www.automationanywhere.com/intelligent-automation

https://www.semantia.com.au/articles/intelligent-automation-industry-disruption/?

Automating Content-Centric Process with Artificial Intelligence by Sarah Burnett, Vice President, Everest Group