AI technology can expand the scope of what companies can automate. Let’s look at a few real-world examples to understand what we can do with Intelligent Automation. We’ll get started with a common use case: Invoice Processing.

Intelligent Automation Use Case Examples

Invoice processing

As we know, in a manual invoicing process, customers receive an invoice from the supplier. The invoice is input into the accounting software generally using copy-paste. Then the invoice is sent for approval, and once approved, the payment is made.

Automation tools like RPA can automate a significant part of this process. RPA can extract the invoice data and verify the invoices. It can then input the data into the accounting system for approvals and payment.

Since RPA is rule-based, its data extraction capability is quite limited. The computer must be told to look for a text anchor or a position and extract the required values. This is okay as long as we only have a few Invoice formats to process. In the real world, though, there are many formats that each customer receives. For example, one of my customers has got 4000 plus formats. So, it becomes tedious to do this rule-based mapping for each invoice.

Instead, what if the machine could learn the mapping by itself? That’s what artificial intelligence for AI enables. It uses a combination of AI techniques to map the fields. The AI techniques include computer vision (CV) and Machine learning (ML).

The AI extracts the data and presents its mapping to us. We can then correct the mappings it could not get right. It learns in the process and improves over time. AI thus does a bulk of the work rather than you mapping each template one by one.

Once AI does the bulk of the work, we can combine that AI output with RPA for higher data extraction accuracy. RPA can also input this data into your accounting systems or ERP.

So, as you can see, RPA, with added AI intelligence, improves automation quite a lot. AI can do more than this, as we’ll see in our next Automation use case – Claims Processing.

Claims Processing

Next, we’ll see how we can automate Vehicle claims with automation.

Usually, after a vehicle accident, customers have to file a claim with damage details. This is a manual process that takes days to process. We can automate this process using Chatbots, RPA, and Google damage assessment API.

A chatbot collects the claim details, including the damaged vehicle photographs. An RPA bot then matches the submission to policy details. It invokes a damage assessment API to assess the damage. Finally, it provides a claim number to the user via chat.

We can see that with more AI capabilities, you can do more! We can string more AI and Automation tools to create more complex automation. We will look at one such use case next: Customer onboarding.

Customer Onboarding

Let’s see how software-based automation can transform an Onboarding process. This is an example from the Intelligent automation book.

When a customer is acquired, we can use Machine learning to segment them. We can then use a Chatbot to have customers provide their information. They usually submit a few documents which can be digitized with AI.

RPA can help us check for any missing information and reach out to the customer to get that information. Once we have all the info, RPA can do background checks and use AI to detect fraud. Finally, we can monitor customer data for any churn risk.

So we can see how Intelligent Automation (IA) can transform your customer onboarding process. This is just another example. You can use IA to change multiple functions in your organization.