RPA Use Cases and Chatbots: How the Combination Boosts Experience and the Bottom Line
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
“The technology known as “Robotic Process Automation” (RPA) enables anyone to set up computer software, or a “robot,” to simulate and incorporate a human user’s interactions with digital systems in order to carry out a business process. RPA robots use the user interface in the same way that humans do to modify apps and collect data. They conduct a wide range of repetitive tasks by interpreting, initiating reactions, and interacting with other systems. Only significantly superior: an RPA software robot never sleeps, commits no errors, and is much less expensive 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 analyze data from different content formats before launching a number of incredibly repetitive business processes.
The beauty of both is that the bots never stop working, responding to client requests or doing massive amounts of repetitive labor, reducing or even doing away with the need for pricey and frequently mistake-prone human contacts 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 the help of mobile technology, this process can now be started at the time and location of the incident. The customer can then get in touch with a claims bot via voice, messaging, or the web to request assistance and gather information about the incident. Uploading of images, documents, or videos that provide additional context to aid in the claim’s validation and processing is also now possible.
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.
However, RPA comes into play when it pertains to handling the claim. The bot may directly log claims with accessible details or photographs into the proper systems after they have been begun, negating the need for 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 business chatbots 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 chatbot use cases 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
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 ai Platform for conversations can expand and improve conversational engagement for your business contact us.