Bots, Chatbots, RPA

Chatbots and RPA Use Cases: How their Intersection Boosts Experience and the Bottom Line

By 6 Minute Read

Chatbot and RPA use cases are set to revolutionize many industries, both in terms of experience and efficiency. How and where do we see Conversational AI and Robotic Process Automation come together?

This blog explores some example use cases where the two technologies can overlap to marry intelligent conversational engagement with intelligent business automation, powered by AI rather than humans, and breaking down organizational silos that hamper convenience, efficiency, and customer experience.

AI Fuels Red-hot Growth and Innovation in Intelligent Engagement & Automation

Hardly a week goes by when there isn’t a funding announcement by a conversational AI (or chatbot) startup company. Many of these are recent entrants to an emerging and growing market based on digital engagement, enabled by conversational interfaces and AI.

And then there are the RPA players, some of whom have taken a natural next step from process automation, adding AI technologies to pivot towards RPA. Another hot and vibrant market! In the past year alone, three large RPA players raised almost $700 million: Automation Anywhere added an additional $300 million in funding, Blue Prism issued stock to create $130 million in fresh funding, and UIPath raised a further $265 million (with rumors circulating now that a $400m Series D round could mean a valuation of $7bn for UiPath)

RPA and chatbot technologies are being adopted at accelerated rates by a variety of industries and for a wide range of bot use cases. And while the focus of chatbots is on digital engagement and RPA’s value proposition is on automation there are many ways in which the two technologies can work together in game-changing ways.

Conversational AI and RPA: Differences and Similarities

In a previous blog, I highlighted RPA versus Chatbots. But let me summarize quickly before I go on to explain where and why I think Conversational AI and RPA can work together.

To quote UIPath’s definition of RPA

Robotic Process Automation is the technology that allows anyone today to configure computer software, or a “robot” to emulate and integrate the actions of a human interacting within digital systems to execute a business process. RPA robots utilize the user interface to capture data and manipulate applications just like humans do. They interpret, trigger responses and communicate with other systems in order to perform on a vast variety of repetitive tasks. Only substantially better: an RPA software robot never sleeps, makes zero mistakes and costs a lot less than an employee.”

At ServisBOT we define Conversational AI as follows:

Conversational AI  is a form of artificial intelligence that understands and simulates human conversation through the use of bots powered by natural language processing (NLP). It allows users (customers or employees) to express intent, via voice or messaging, whereby the bots then execute on that intent and automate the required tasks to fulfill the customer need. Conversational AI represents one of the most significant shifts towards using natural language to do things like transact, book things, search items, interact, and access services, without the need for human intervention. It goes beyond just the conversation to orchestrate and automate underlying processes and tasks needed to execute on the customer’s need

So a chatbot understands and simulates human conversation while a RPA robot emulates the actions of a human. While natural language processing (NLP) plays a role for both technologies, chatbots interpret conversations, from voice or text channels, while RPA bots extract language and data from documents, files, forms, browsers etc.

The former engages purely through a conversational interface whereas the latter can scrape information from user interfaces that are not conversational. But both technologies rely on automating underlying tasks or business processes. Chatbots take their cue from a user’s desired intent expressed through chat and execute the required business tasks in order to fulfill their need. RPA bots interpret data from various content forms and then trigger a number of highly repetitive business tasks.

The beauty of both is that the bots never sleep, working 24/7 to fulfill customer’s needs or process large volumes of repetitive tasks, limiting or eliminating the need for costly and often error-prone human interactions and handoffs.

Chatbot and RPA Use Cases provide Opportunities for  Multiple Industries! 

So let’s look at some of the interaction-intensive industries where customers are increasingly engaging via conversational interfaces – SMS, messaging apps, email, web browsers, live chat and voice assistants such as Apple Siri, Amazon Alexa, or Google Assistant. These include companies in the insurance, banking, travel, entertainment, logistics, consumer goods, energy, telecommunications, and healthcare sectors.

Now consider the transaction- or processing-intensive industries where intelligent automation is driving new levels of efficiency in automating highly repetitive processes at greater speed and levels of accuracy and you’ll discover that the same sectors are being impacted by these two technologies.  

But even more interesting is when you look at the use cases for conversational AI and those for RPA. You can see startling similarities, even sometimes in the language used to describe the benefits and the challenges.

Here are some use cases in insurance and banking that show how the two technologies working together can create an even more frictionless, speedy and superior experience for customers while bringing bottom-line benefits to the business.

Insurance Claims: Resolving Claims Faster with AI-powered bots

Think of insurance claims as an end-to-end customer journey involving multiple interactions and processes that are handled by multiple systems and employees. Without speed, accuracy, and efficiency in back-office processing, no matter how great and responsive the customer engagement piece is, the time to resolve a claim is dependent on the complete chain of events from a customer filing a claim through validation, approval, and payment of that claim.

A claim is initiated when a customer reports an accident, loss or other incidents. With mobile technology this process can now be initiated at the time and scene of the incident, enabling the customer to reach out, via messaging, web or voice, so that a claims bot can assist and gather documentation and details related to the incident, even enabling the uploading of images, documents, or video that provide more context to help in subsequent validation and processing of the claim.

By integrating securely with back-office claims management systems the bot can access the customer’s policy information so as to update them on eligible services (such as towing or car rental), their deductible, relevant third party information, or other policy details pertinent to their claim. These conversational interactions and access to customer information (e.g. connecting the customer to a repair shop near their location) are enabled by conversational AI. 

But when it comes to processing the claim, here’s where RPA comes in. Once a claim with available details or images has been initiated it can be directly logged into appropriate systems by the bot, eliminating any manual entry.  The claim then goes through validation checks, reviewing policy status, entitlements, and eligibility.  Some of this data can be used by the conversational bot to inform the customer of their eligibility for certain services or their policy limits, for example.

Depending on the type of insurance claim there are then rules and workflows around adjudicating the claim, approving the damage and reimbursement amounts, and making a payment to the customer. This can involve multiple business rules management, workflow, and accounting systems, many of which are legacy systems. RPA is instrumental in integrating with all these applications in highly automated, seamless and scalable ways so that the claim can be processed with much higher speed and accuracy.

If you think about how an insurance provider can use conversation to engage with a customer throughout the journey, you can see how the combination of conversational AI and RPA makes total sense in reducing resolution times and greatly improving customer experience.

Loan Applications: A Smoother Journey with Chatbots and RPA

When it comes to banking bots, the process of applying for a mortgage or a credit card account and getting approval is another area where RPA and conversational AI can play a combined role.

Take the example of a mortgage application that a customer has to go through before being approved. The customer journey and approval process are complex, requiring multiple steps, systems, and handoffs that are fraught with friction and inefficiencies. This can result in losing loan customers during long and frustrating cycle times. Even in online applications, it can be difficult to guide customers efficiently through the process, respond to their queries immediately, and bring them through to completion without them dropping or interrupting the web session.

Now consider a mortgage bot that can engage with the customer immediately when they initiate a mortgage application request via their mobile app or web portal. The bot gathers all the necessary proof documentation and details before passing this on to the back office processes that can validate the information and/or seek alternative proof docs from the customer. RPA can automate the validation steps while the chatbot can manage the conversation that gathers the docs and informs the customer of any issues.

Then comes the appraisal process. Traditionally, an appraiser is tasked with determining the current market value of a home, a step that causes delays and costs. Now with RPA, automated appraisals can determine a valuation in seconds by running analyses with comparable home sales. The RPA bot eliminates the need for the appraiser but it also greatly accelerates the appraisal process so that the customer can be proactively updated via the chatbot with the status of their loan application.

A Conversation-driven, Automation-First Approach

The use cases for a combination of conversational engagement and RPA are not limited to insurance and banking but span multiple industries and use cases, such as energy and utility use cases, employee engagement bots, customer service bots and more. Wherever there is a customer or employee interaction in a business process that involves bulk, repetitive and/or time-intensive processing of transactions, documents, or other records you can think of how the two technologies could be leveraged to improve the customer experience at a lower operational cost. Here are some additional examples but the list goes on:

Sample Use Cases where Conversational AI and RPA Intersect

Use cases for RPA and Chabots

So ask yourself, is there a customer- or employee- engagement that can be improved with smart conversations and is there an associated and underlying time-intensive process that could benefit from RPA?

If you can marry the two together in ways that are transformative, the enormous benefits of both technologies can be reaped. Of course, like with any new technology, it is not about just applying bots to existing engagement models and business processes. Rather, it requires rethinking existing models and processes with a conversation-driven and automation-first mindset that is at the heart of chatbot implementation strategy.

For more information about how a Conversational AI Platform can expand and improve conversational engagement for your business contact us.

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