Chatbots and RPA. The waiter and the chef
Until recently, chatbots and RPA were considered two separate and distinct functions. Chatbots generally dealt with customer service interactions, while RPA processed back-office data manipulation. Right? They were performing different tasks, so there was no need for them to play together.
Both technologies have come a long way in recent years. RPA has evolved into teaching robots what to do rather than automating them through code or scripts, a step closer to intelligent automation. Chatbots in the meantime have become smarter, more sensitive to language and emotion, and capable of solving issues rather than just supplying information.
As their capabilities have broadened, the lines between the technologies have blurred. Nowadays they can be used together at different stages of the same process, for example with a chatbot interacting with a customer and handing off tasks to an RPA bot to execute. One bot to take instructions and another one to do the grunt work, like a waiter and a chef…
We’re going to take a brief look at how they can work together to make gourmet, Michelin-star quality, eh, processes.
First of all, what is a chatbot?
Chatbots, also known as digital assistants, can be characterized as AI-based software that simulate human conversations and comprehend human capabilities. Bots decipher and process user requests and offer relevant answers.
Bots can interact through voice or text, and can be deployed across websites, applications and information channels, such as Facebook, Twitter, WhatsApp, Slack etc.
How do chatbots work?
Chatbots work by examining and recognizing the purpose of the user's request to extract relevant data. Once the analysis is done the chatbot delivers an appropriate response to the user.
From a high-level view, this look as follows:
• A user — you, an individual, your auntie — speaks with a computer program. You type, maybe you talk, perhaps you smile and wave.
• The computer program reacts to what you have said depending on how it has been programmed. Maybe you wrote "cost," and the chatbot has been programmed to present data about product costs.
User sends a Request considered and Real-time
request analysed by AI user conversational response
Different types of chatbot framework are available depending on the outcome required. For example, some chatbots use AI (artificial intelligence) and NLP (natural language processing) to simulate human conversation.
Advance Chatbot Concept View --->
AI technology allows these chatbots to better understand, adapt to, and respond to a conversation. Chatbots with advanced technology like NLP are certainly useful, but for the day-to-day use of banking, marketing, and customer service, basic chatbots do the job just fine.
The chatbots work by embracing three characterization methods:
1. Pattern Matching
Bots utilise pattern matches to group the text and produce an appropriate response for the customer. The bots are instructed to recognize the patterns and respond accordingly to anything relates to the correlated patterns. The more patterns the bot understands the more useful it will be.
Artificial Intelligence Markup Language (AIML) is a standard structured model of these patterns.
2. NLU (Natural Language Understanding)
NLU is an artificial intelligence language that uses computer software to recognise text or speech sentences. The NLU provides a direct human-computer interaction and allows human languages to be understood statically by the computer without the use of if / else.
NLU follows three specific concepts.
• Entities – the information in the user input that is relevant to their intention.
• Context – a natural language understanding algorithm must be able to grasp the backdrop of the conversation.
• Expectations – the ability to fulfil the customer expectations when they make a request..
3. Natural language processing (NLP)
Natural Language Processing bots are designed to convert the text or speech inputs of the user into structured data. The data is then used to choose a relevant answer.
Natural Language Processing (NLP) comprises of the below steps:
• Tokenisation – The NLP filters set of words in the form of tokens.
• Sentiment Analysis – The bot interprets the user responses to align with their emotions.
• Normalisation – It checks the typo errors that can alter the meaning of the user query.
• Entity Recognition – The bot looks for different categories of information required.
• Dependency Parsing – The chatbot searches for common phrases that what users want to convey.
Here’s a quick look at how chatbots can be integrated with RPA
The front office bots interact with the customers or employees to transfer information, perform tasks or handle their request. Based on the use case, a bot integrates with different enterprise systems, such as CRM, SharePoint, APIs, help desk etc. The chatbot can navigate through modern systems and APIs, however may not be capable of integrating with legacy system like Windows or CITRIX based.
This is where RPA comes in. The RPA bots integrate with legacy systems or other complex business applications and fetch the information for the chatbot.
By adding deep learning and machine learning functionalities, the RPA bots can harness and analyze large volumes of customer data and provide meaningful, contextual services to the customer.
Chatbots and RPA in harmony
According to last year’s Gartner report, 68% of service leaders indicate that bots and Virtual Customer Assistants will be more important in the next two years. As companies increase the functionality and scope of artificial intelligence within their customer service, chatbots will increasingly rely on back office bots to find information and complete transactions on behalf of the customer.
While there are several trends impacting the chatbot ecosystem, the interaction between RPA and Chatbots is growing fast. The chatbots are already helping the enterprise to solve many challenges in customer service, employee self-service, and scalability. Now, by combining the power of automation from RPA with the cognitive intelligence from chatbots, the entire experience can be taken to the next level, improving productivity and business advantage. The result is a an intelligent and efficient e2e customer and employee experience.
The advantage of RPA and Chatbot Integration include:
• Reduction in business costs
• Increased employee productivity
• Reduction in average handling time to complete tasks
• Improved employee and customer experience
If you have any questions on implementing chatbots, RPA, or a combination of both, feel free to reach out to email@example.com for a no-strings workshop tailored for your company.
Eclair will work with you to understand your ambition and deliver a tailored solution that fits your scale and budget.