Intelligent Automation, in general terms, is about leveraging AI in combination with RPA for achieving end-to-end automation. This blog will help you understand the concept of intelligent automation better and give some real-world use cases of intelligent process automation.
Some of the most frequently asked questions around Cognitive/ Intelligent Automation are:
- What does ‘cognitive automation’ consist of that enables better use of RPA?
- What are the different technologies of AI that can elevate the usage of RPA?
- What are the use-cases where these AI-powered technologies can be used?
Artificial Intelligence is a vast topic. But there are certain areas of AI, while used in combination with RPA, can make automation more intelligent.
“By using a combination of Artificial intelligence (AI) and Robotic process automation (RPA), an organization can automate processes end to end. A typical end to end process involves both structured and unstructured data. e.g. take in a document using AI, parse, classify and understand meaning or sentiment, and pass on the required action to RPA.”
— Sarah Burnett, Vice President, Everest Group.
Let us delve into a few of each of these, understanding them in detail and their use case in RPA.
RPA with Computer Vision
Computer vision is the capacity of the computer to be able to understand from digital data like images, documents, or any computer screen, etc.
For instance, while RPA has the property to be able to read data from webpages or desktop applications, traditional RPA lacks the functionality to be able to read from Virtual Desktop Interface. This proves hindrance and processes that need to invoke VDI fall out of the RPA radar.
But with computer vision, this issue can be overcome as RPA can read from any screen on the desktop with its AI capabilities.
Similarly, reading text is possible with traditional RPA, but when it comes to reading handwritten documents or inferring information from images, Computer vision overcomes the short-coming of the traditional RPA and can achieve better results.
Example – Insurance Industry
One major industry where image recognition and document extraction proves worthy is the insurance industry. The Insurance industry has been a common use case for RPA. Using traditional RPA, some of the processes can be automated. But using cognitive automation, lot more processes in insurance can be fast-tracked.
Auto Insurance, for instance, depends heavily on images of the cars or vehicles that are damaged using which the claim is assessed. When using image recognition, RPA can access the claims and process it with minimal human intervention.
Similarly, Insurance also has a lot of paperwork. Using AI-powered document extraction, for both structured and semi-structured data, and processing handwritten documents brings many more processes in the Insurance industry into the RPA radar.
How Intelligent Process Automation is Transforming the Insurance Industry – Download White Paper
RPA with Natural Language Processing(NLP)
Natural language processing is all about deriving information from free flow text like a human conversation, emails, chats, etc. Some of the key features of NLP are
- Sentiment analysis
- Intent classification
- Entity classification
- Speech to Text
All the above types of analysis enable processing text and infer meaningful information which in turn enables end-to-end automation. While processing documents for any given use-case, OCR will help to derive the information from documents but NLP enables processing the information and making decisions.
Example – Customer Support
The customer support process involves understanding and replying to emails, chats, voice assistants, etc. Bringing in RPA + NLP for the above use cases will minimize human intervention in these processes.
For instance, considering a use-case where email streamlining is automated. Based on the content of the email, the email needs to be either sent an automated reply or further escalated to the concerned department. This process can be made end-to-end, where NLP will be able to gauge the purpose of the email and reply where-ever possible or forward to the respective department.
This minimizes the effort to a great extent where all the emails that would be auto-replied are completely taken off the human effort.
RPA with Predictive Analysis
According to Wikipedia “Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.”
What this means is, by combining process automation with predictive analysis, we will not only be able to automate a process but also be able to use the data collected from the process to make decisions that make the process end-to-end automated.
This particularly proves beneficial in finance and compliance where Anomaly detection, Fraud detection, etc play a major role.
Examples – Financial Transactions
With RPA + Fraud detection, financial institutions will be able to gather information on user’s transactions from different sources, process the information, and feed into analysis systems and do predictive analysis. This enables to track any fraudulent transaction and red flags which can be notified to the relevant authorities.
This can be used in Debit/Credit card transactions, online shopping, insurance claims processing, and a wide variety of industries.
RPA with Chatbots
Chatbots and RPA bots are two different things, but when combined, they can prove to be more powerful. Chatbots interact with users to answer simple questions and provide relevant information.
But the use of Chatbots can be elevated by combining with RPA. Based on the customer queries and requests, chatbots will be able to perform simple tasks. This can be proved beneficial in many customer services based industries and be able to enhance the customer experience.
Example – Customer support in Retail
Considering an online shopping portal with integrated chatbots, customers will have different types of product queries, order queries, etc. While the bot will be able to provide the relevant data, it will be better when the bot is also able to perform a task.
If a certain customer needs to cancel an order or increase the order quantity or change the delivery date, chatbots can feed this information to an RPA bot that completes the intended task. This provides instant gratification to customers, making them happy, and brings down a lot of burden on the otherwise overloaded customer service executives as well.
As explained, RPA when combined with AI technologies has a broad spectrum of use-cases and if leveraged properly, can bring in huge savings in terms of cost and time. The success of RPA depends on being able to choose the right tools and processes for automation. If you are looking to start your RPA journey afresh, use our Automated business process discovery tool to understand which processes can give you maximum ROI. If you are looking to take your RPA journey to the next level and make end-to-end automation possible, talk to our experts and understand how RPA + AI can help you scale.