What is conversational AI

The business value of conversational AI

Conversational AI improves customer experience

Better customer experience

Direct messaging is the preferred channel for interactions because of its speed and simplicity. This is particularly true for Millennials and younger generations.

Conversational AI is the set of technologies behind automated messaging

Shorter waiting times

Conversational AI frees up capacity for human customer service, by automating high-volume customer interactions and augmenting human support staff.

Conversational AI allows technologies like chatbots to interact with people

Efficient human support

The performance of human customer service agents improves when augmented by AI, with more cases per hour resolved and a more consistent flow of information.

Conversational AI: How it works

“In order to help someone, you must first understand what they need help with.” - ancient proverb (not)

How does conversational AI translate human language into something a machine can understand and respond to in a human-like manner? On the surface, it’s deceptively simple - a customer interacts with a virtual agent and is given an appropriate response. But there are actually a number of different technologies working behind the scenes to make this happen smoothly.

This shows how conversational AI works and what technologies working behind the scenesAn illustration that shows how conversational AI works and what technologies working behind the scenes

The first step involves Natural Language Processing (NLP). It’s the job of NLP to correct spelling, identify synonyms, interpret grammar, recognize sentiment and break down a request into words and sentences that make it easier for the virtual agent to understand.


Once the request has been prepared using NLP, a number of Deep Learning and Machine Learning models take over. Collectively known as Natural Language Understanding (NLU), these allow conversational AI to identify the correct intent (or topic) of a request and extract other important information that can be used to trigger additional actions i.e. context, account preferences, entity extraction, etc.

Proprietary algorithms also play an important role in enhancing the overall NLU capabilities of a virtual agent. In the case of boost.ai’s conversational AI platform, we have developed Automatic Semantic Understanding (ASU), an algorithm that works alongside Deep Learning models as a safety net to further reduce conversational AI’s chance of misunderstanding user intent.


Now that the request is properly understood, it’s time to formulate a response back to the customer. It is key that a virtual agent is able to communicate in a personalized manner which is where conversational AI outshines traditional chatbot solutions. By combining the information gathered using NLU (customer intent, contextual information, etc.) with a structured hierarchy of conversational flows, a virtual agent can respond appropriately - whether it’s answering a simple question or completing a complex transaction - in a conversational manner that is more akin to interacting with a human than a machine.

Over time as the virtual agent answers more questions, and as AI Trainers help to enhance its knowledge, conversational AI grows smarter - learning new variations to each intent and how to improve its responses.

The secret behind conversational AI are the AI trainers

AI Trainers: the secret behind customer service automation

Technology is a crucial part of what makes customer service automation work, but it’s only one piece of the puzzle. Helping customers and solving problems has long been the domain of customer service teams and it’s their expertise and experience that can be leveraged into ensuring that conversational AI achieves its potential.

By up-skilling members of their trusted customer service teams into AI Trainers, and not relying on external consultants or data scientists, companies are able to keep conversational AI on-brand. AI Trainers are a new breed of non-technical, self-service professionals. They are responsible for building, training and working alongside a virtual agent to automate large-scale interactions between brands and consumers, boosting self-service rates, decreasing the workload of their frontline colleagues and delighting customers in the process.

This symbiosis of machine efficiency and human expertise is the secret sauce behind what makes conversational AI such a powerful tool for automating customer interactions.

Learn more about how they design interactions (4 min read)

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