An Introduction to Conversational AI
Conversational artificial intelligence (CAI) is a form of technology that is enabled by machine learning to imitate natural conversations with humans.
Conversational AI uses large volumes of data, Natural Language Processing (NLP), and deep learning to recognise speech and text inputs, detect cues, and give the correct response. Conversational AI can be found in many forms from voice assistants to chatbots and intelligent self-servicing systems, all of which can be programmed and designed to be intelligent and interactive.
How does Conversational AI work?
As mentioned, conversational AI relies heavily upon Natural Language Processing (NLP) and machine learning. These processes generate a constant feedback loop to continuously improve its AI algorithms. This is why Conversational AI is distinctive from other branches of AI. Its algorithms are able to process, and generate responses, or translate them in a human-like manner.
Let’s take a more in-depth look at how conversational AI is enabled, step-by-step.
Machine learning applications, using statistics, allow CAI algorithms to find patterns in massive volumes of data, which consists of numbers, words, images, or anything that can be digitally stored. As CAI is “fed” with more inputs, it becomes much more adept at identifying patterns and making predictions.
As a subset of machine learning, deep learning algorithms are also embedded in CAI platforms to learn and imitate how human brains generate responses. With its artificial neural network, deep learning requires even larger sets of data, along with multiple layers of calculations to constantly improve the outcomes, and enhance the human-like interactions.
After CAI algorithms can successfully identify patterns, NLP is then embedded to transform unstructured data into machine-readable formats, which are analysed to generate appropriate responses. This process encompasses four principal steps:
- Input generation: Users enter voice or text inputs through a website or an app.
- Input analysis: Natural Language Understanding (NLU) is used to analyse the text-based input, whereas a combination of Automatic Speech Recognition (ASR) and NLU is then used to analyze the speech-based data.
- Dialogue management: Natural Language Generation (NLG) develops a response.
- Reinforcement learning: Along with machine learning, NLP perfects the responses over time to ensure conciseness and accuracy.
How can conversational AI be used?
Delivers human-like experience- Conversational AI aims to solve numerous tasks and problems in a human-like manner. Although this seems to be a far-fetched goal, the current conversational AI applications are benefiting businesses more than ever before. Evidently, conversational AI applications are being used on a mainstream scale as a replacement or augmentation of human agents in the customer journey.
Support 24/7- A digital agent that never sleeps, conversational AI delivers quick and accurate answers for frequently asked questions (FAQs) such as shipping, cross-selling, or providing personalised answers regarding sizing using customers’ data. The most common forms of CAI used in these circumstances are chatbots and voice assistants as virtual agents on e-commerce sites and messaging apps.
Improved customer service- When used in contact centres, these AI-powered platforms can work all-day round, rotating live agents to assist customers with complex problems, or handling enquiries outside office hours. This seems to be the ultimate resolution to the age-old problem in customer service: how to not lose customers during shortage of staff, or long handoffs?
Benefits of conversational AI
Saves you money- Another common challenge for businesses is reducing operational costs. Embedding one central source of conversational AI has the ability to replace the need for human agents for mundane tasks, thus reducing salaries and training costs, which is a highly cost-efficient benefit for medium to small-sized businesses.
Improved Customer Service- The costs saved will allow more room for customer service improvement, particularly in the quality of support and first contact resolution. Virtual agents allow consistency in responses both in store and online. This is especially important as interactions with customers tend to be information-seeking and repetitive.
Improved Customer Satisfaction- Besides saving costs in terms of headcount, this form of AI can be more accessible and less intimidating to customers than employees. 86% of consumers prefer chatting with AI applications than with human agents, thus avoiding long waiting times, and handoffs. As customer satisfaction grows, companies are more likely to see the customer retention rate grow, reflecting in customer loyalty, increased sales, and revenue from referrals. Customer satisfaction can also stem from personalisation, enabling businesses to upsell products and services.
