The Convergence of Robotic Process Automation RPA and Smart AI Bots

By 5 Minute Read

 Robotic Process Automation (RPA) Bots and Conversational AI: Two Winning AI Technologies

rpa bot

Terminology abounds in the area of artificial intelligence (AI) and multiple definitions can lead to confusion and blurring.

In industry sectors like manufacturing, utilities, energy, financial services, healthcare, and telecommunications, routine, rules based, and repeatable business processes have become less manual thanks to the application of automation software throughout the decades. Increased accessibility and advances in the area of Artificial Intelligence (AI) are creating another wave of opportunities to increase the degree of automation as well as to apply greater intelligence and cognitive understanding to business workflows and processes.

One area of technology evolution in automating business processes has been in the area of robotic process automation. But more recent developments in chatbot technology and conversational AI may be set to disrupt how organizations approach chatbot automation.

Maybe the future lies in a mix of the two technologies, in the form of a RPA chatbot, where the power of natural language understanding and machine learning can come together to enable end-to-end automation of customer- or employee-facing processes. 

Firstly, let’s look at how RPA vs Chatbot technologies and approaches differ and how they intersect? 

Chatbot vs RPA: The Characteristics

RPA applies software robots or bots, to automate monotonous processes at scale, eliminating inefficiencies, cutting costs, and improving speed and performance. It is the next stage in the evolution of traditional business process automation, which has been around for decades. The technology can vary from basic rules-based automation to very complex solutions based on machine learning.

At the more basic end of the spectrum, solutions are often an extension of traditional business process and rules management while at the more advanced end, the complexity means that data scientists are required to build highly customized AI solutions that are both expensive and difficult to scale or adapt with shifting demand. 

The key characteristics of RPA technology are:

  • It is applicable to routine, repetitive, rules based, or predictable business processes.
  • It is centered mostly on automating back office repetitive operations and business processes.
  • It focuses on eliminating manual or human involvement in process steps.
  • It is governed by structured data inputs and not human inputs or chat.
  • It is a  process-centric, bottom-up approach
  • It decreases friction across process tasks by limiting human handoff
  • It focuses on back-office processes governed by rules and business logic, for example in the areas of finance, operations, production, HR, and distribution departments
  • It lacks the flexibility to adapt quickly to changes or exception-handling
  • Finally, RPA implementations are generally IT-led with some input from the business

Where the confusion between RPA software and Chatbots comes in is that both are strongly associated with robots or ‘bots’. In the case of RPA bots, these are process automation bots and in the case of Conversational AI bots, these are conversational AI bots (often also referred to as conversational bots, AI bots, chatbots, or digital assistants). RPA bots don’t have a chat element whereas chatbots rely on natural language technologies to emulate human-like conversations. In the early days of chatbot technology, these were often characterized as digital assistants that responded to simple Q&A-type requests. Now they have evolved to be much smarter conversational bots that can automate complex flows and tasks, detect sentiment, respond to context switching,  and handle multi-turn digital journeys.

The key characteristics of Conversational AI are:

  • Any user-centered procedure or journey that is launched or carried out using speech (phone, physical, or voice-activated devices) or message qualifies (text, chat, email, web, etc).
  • It is based mostly on automating client (or employee) engagements across a variety of different digital channels, playing a significant part in the modern business transformation.
  • Leveraging natural language understanding (NLP) technology, conversational AI can simulate and understand human intent and automate tasks needed to fulfill that intent.
  • It is governed by unstructured data in the form of free-form and/or guided conversations but with the ability to handover to a human worker when necessary.
  • It is a data- and conversation-centric, top-down approach
  • It decreases friction across process tasks by limiting human intervention
  • Early adoption for business chatbots was mainly for customer service use cases but they are equally powerful in many other areas such as employee interactions (IT services, HR, facilities management, field service) and other customer interactions and journeys (quotations, onboarding, claims, renewals, collections and more).
  • It is very versatile, adapting quickly to changes and gaining intelligence and capabilities through training
  • And, chatbot implementations are mostly business-led, involving IT when needed.

differences between chatbots and RPA

Based on natural language processing technology, chatbots can engage with a customer on multiple digital channels via either voice or text. RPA, on the other hand, is applied to a discrete business process that does not involve chat.

For example, RPA bots could be used to generate customer invoices by downloading customer order information from existing systems or screen-scraping and extracting the information when it is not available from code.

A chatbot, on the other hand, can be used to respond to a customer’s text requesting details of an invoice, interpret the intent, retrieve the appropriate data required to fulfill the request, and present the information to the customer in the same chat session. Customer satisfaction goes up and cost savings increase as the degree of human interaction in routine engagements and workflows is reduced.

Recommended Reading: The Fundamentals of a Successful Chatbot Strategy

RPA and Chatbot Approaches Differ

Automation has been around for decades with robotic process automation representing more of an evolution in the approach to automate monotonous processes. By implementing RPA, processes are automated by teaching robots what to do rather than automating them through code or scripts. So it’s a step up the curve towards more intelligent AI automation.

As RPA tools and conversational AI technology have evolved,  the two are increasingly intersecting. After all, the ultimate goal of each is simply automation.  However, it’s important to recognize that the RPA approach is different from the conversational AI approach and each requires different skillsets.

Conversational AI represents a revolutionary approach, where conversations and user intent dictate the tasks and processes that are executed in order to fulfill the intent. By its nature, it is more free-flowing and unstructured requiring a high degree of flexibility in orchestrating the right tasks at the right time. This contrasts with RPA where the processes are less fluid and more rigid. Layering conversational AI on top of RPA creates the potential for a more comprehensive end-to-end approach to chatbot automation.

Bot orchestration is probably one thing that makes the conversational approach much easier, faster, and more versatile.  At ServisBOT, by using a clever bot orchestration approach, a Dispatcher can hand off a high-level customer intent to an appropriate task-oriented bot who can either execute on the intents or invoke the help of another bot, if needed. In this way, multiple bots can be assembled to handle different tasks in a customer journey and individual bots can be deployed or removed according to how the conversation guides the execution of tasks. This is a much more flexible and modular approach than traditional RPA.

Bot Orchestration Diagram - ServisBOT

But will Chatbots replace RPA? 

Some business use cases lend themselves more to RPA while others favor more of a conversational Ai approach. But where customer or employee engagement meets automation there may be an argument for Conversational RPA.

Conversational AI is a newer technology that is key to digital transformation. The use cases are increasingly not just focused on simple Q&A type interactions. Rather, chatbots can now understand high-level customer intent and route it to a task-oriented automated chatbot or an RPA bot to execute the necessary tasks. As this evolves and matures,  conversational AI bots are set to enhance some RPA workflows by adding a conversational element to them.

The Use Cases for Chatbots

The broad differences between RPA technology and conversational AI have been mentioned above as have some of the high-level business use cases. Wherever there is a chat element to a process think in terms of conversational AI. And if the business process is highly administrative with no chat element, maybe RPA is the right choice. But the two can also work well together.

Don’t think too narrowly about conversational AI. Many of the current implementations are in the areas of more basic customer service use cases. However, enterprise operations are increasingly extending their horizons beyond customer service, recognizing the potential for bots to go broad as well as deep in their organization and to be used in outbound marketing campaigns, in field sales or business operations, or in employee-facing engagements. The pandemic has especially spurred the interest in Conversational AI solutions for accelerating digital transformation. 

Related Resources:

The Business Use Cases for Conversational RPA

Download our new eBook: A Conversational AI Journey Guide.

 Visit our Conversational AI resource library 

Close this Window