Introducing Intelligent Automation

For years, businesses have used process automation to make both internal and customer-facing tasks more efficient. Legacy technologies have limited these improvements to basic applications such as creating and modifying records using form-based inputs, and automatically sending documents between business functions. However, this did not substantially improve the customer experience as people generally don’t want to spend their time filling in forms. They want to communicate in a more natural and convenient way using the array of digital options they have available to them.
More recently, techniques such as Robotic Process Automation (RPA) have pushed the envelope by using software that mimics how employees work. This has enabled RPA to automate tasks such as formatting data in a spreadsheet. Still, the scope of RPA and other automation techniques has been narrow because they could only cope with the kinds of structured, digital information found in database tables. The unstructured information found in emails, social media posts, video, recordings of call-center conversations, word processing documents and scanned images such as PDF and TIFF files have been very difficult to process automatically in large volumes.

Figure 2: Using IA to structure data

Figure 2: Using IA to structure data

Taking automation to the next level with Artificial Intelligence

That situation is now changing. By integrating Artificial Intelligence (AI) with RPA – a combination commonly called Intelligent Automation – it’s now possible to process unstructured content at massive scale. AI-based techniques such as machine learning and deep learning have unlocked a valuable set of new capabilities. For example, natural language processing (NLP) is able to ‘read’ unstructured information from a constellation of different sources. Meanwhile, natural language understanding (NLU), can ‘understand’ the content and even the sentiment of the communication and extract the underlying concepts and intents. RPA can then be used to automatically move the information into the correct systems for further processing. IA is a powerful way to digitize and streamline business tasks, as it can complete more complex tasks and requires less oversight than previous approaches to automation.
Take a function like answering high volumes of email inquiries, which is a common challenge for insurers, banks, telecom companies and major retailers. With an IA solution, a company can manage thousands of emails a day across multiple categories and languages. Artificial Intelligence is used to automatically understand and index the emails and then take out the key information, according to a set of established business rules. RPA is then engaged to move the extracted information into the correct back-office system and either automatically reply to the email or forward the message onto the responsible business unit for processing.

Making data an asset

In the digital economy, data is the fuel. It’s used for everything from improving operational processes, to understanding and modelling customer behavior to predictive analytics. But although companies now receive huge volumes of data, much of it is in a raw state. Unless it is structured and made available to the right systems at the right time, it is not useable. IA can take unstructured data and turn it into a format that computers can process. This is also extending what is possible with data analytics. For example, the Artificial Intelligence component in IA is able to gauge customer sentiment or highlight key business areas where a company may be getting lots of requests. IA does not just process the numbers, it can help companies to ‘understand’ the context behind them.


Today's customer experience challenge

Chapter 1

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The benefits of Intelligent Automation

Chapter 3

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Overcoming Implementation Challenges

Chapter 4

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Intelligent Automation in Action

Chapter 5

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Intelligent Automation and SPS

Chapter 6

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