How RPA differs from conversational AI, and the benefits of both
Because of their differences, both technologies can be beneficial to businesses.
While RPA is more geared towards backend automation, conversational AI lends itself to automating human and personalized interactions. In some use cases, the two technologies overlap. But where does backend process automation meet call-driven automation?
Know the difference
RPA involves managing rigid, predictable processes where there is no ambiguity about the request, as opposed to conversational AI, where user intent dictates what tasks are performed in response to a request. Chatbots or digital assistants use messaging interfaces or voice control to conduct a conversation. The fluidity of the conversation requires that natural language processing (NLP) automation be very flexible so that the intent is correctly interpreted and the right tasks are performed to resolve the requirement.
Support of customer trips
Imagine the scenario of buying a new insurance policy. Typically, insurance agents go back and forth with a customer to collect several sample documents that need to be validated and attached to the customer record in order for the policy to be taken out. Dealing with large numbers of new customers and managing their onboarding is time consuming and costly for insurance companies.
Use a chatbot or digital assistant to ask, “Please upload a picture of your driver’s license here” or “Can you upload a copy of your latest bank statement?” Offloads the burden on the human agent to follow up on these documents and also provides a more seamless and convenient customer experience as they progress through the onboarding journey. This is the essence of conversational AI. It improves digital customer loyalty and service outcomes while reducing the cost of managing routine steps on a user journey.
In the backend, RPA can come into play in the onboarding scenario by automating repeated compliance checks for collected documents and updating recording systems with customer information. Both together can be very powerful in order to make complete customer journeys seamless, faster and more efficient.
Gartner: 5 Measures to Support Customers in the Covid-19 Crisis
Kristin Moyer, respected VP Research Analyst at Gartner, examines five actions that are required to help customers cope with the Covid-19 crisis. Read here
With Conversational AI, companies can now automate important interactions with customers and / or employees. This enables a completely new wave of automation potential, which in combination with RPA can significantly reduce the need for human intervention in end-to-end business processes.
RPA and conversational AI can work hand in hand. For example, when a customer applies for a mortgage, they have to go through many steps, creating frictions and inefficiencies that can lead to the bank losing the customer.
A mortgage bot that can get in touch with the customer right at the beginning of the application, request their ID, earnings, and current utility bills, and then forward them to back office processes for validation, removes much of this friction. RPA can be used for the validation processes while the digital assistant manages questions from the customer, understands the intentions, collects the relevant documents, and keeps the customer informed of any issues. The property valuation can be provided automatically via RPA using property market data so that the customer can be proactively informed of the status of their mortgage application via the digital assistant.
Who is responsible?
RPA initiatives are typically led by IT with input from business departments such as finance, production, or sales and are primarily aimed at lowering costs and increasing efficiency by reducing manual processes and minimizing employee involvement.
Conversational AI is designed to improve and automate engagement and reduce costs. At the same time, bots can be handed over to humans if necessary. As such, it is usually run by business departments such as Customer Service, Human Resources, and Sales, with limited input from IT.
How UK retailers are using AI to improve security and operations
Michael Affronti, Dataminr’s senior vice president of products, explains the role AI plays in improving security and operations for UK retailers. Read here
AI without a data scientist?
Many companies believe that artificial intelligence is complex, costly, and requires large budgets and teams of data scientists to build the natural language models and algorithms for machine learning. Creating conversational AI bots doesn’t necessarily require that level of investment, however. Natural Language Processing (NLP) engines like Google DialogFlow, Amazon Lex, and Microsoft LUIS are widely used and make it easy enough to fill a bot with intentions and utterances that are at the heart of a conversational experience that can be used to automate interactions.
In addition, conversational AI platforms have emerged that offer a low-code approach to creating chatbots, creating workflows, and securely integrating with popular business systems so that business people can design and deploy their own digital assistants without coding or AI – Knowledge to be required. With out-of-the-box bot blueprints and tools, businesses can get chatbots to market faster, rely on less IT resources or data scientists, and simply click, drag and drop.
How little code can help overcome the lack of AI skills
Richard Billington, Technical Director at Netcall, explains how low-code can improve the playing field for AI skills. Read here
Companies are working to digitally transform their core business processes to enable greater automation of the backend processes and promote more seamless customer experiences and self-service in the frontend. We see banks, insurers, retailers, utilities, and telecommunications companies working to develop their own digital assistants with a growing number of skills while delivering a consistent branded experience.
Developing bots doesn’t have to be complex. It’s more important to carefully identify the right use cases where these technologies can deliver a clear ROI with the least amount of effort.
Regardless of whether a company is using RPA or conversational AI or both, it is important to first understand the business problem that needs to be solved and then determine where bots make an immediate difference. Then consider the investments required, barriers to successful implementation, and expected business outcomes. It is better to start small with a tightly focused use case and achievable KPIs than to do too much at once.
Conversational AI and RPA are very powerful automation technologies. If well designed, a chatbot can automate up to 80% of the routine inquiries sent to a customer service center or IT help desk. This saves a company time and money and allows them to scale their operations. However, outliers or special cases are still better handled by human agents. The Pareto principle also applies to RPA. Automation at its best does the majority of the routine and repetitive tasks, leaving behind the more unique, valuable, and rewarding work for humans.