In an increasingly digital world, conversational AI enables humans to engage in conversations with machines.
By 2025, the global conversational AI market is expected to reach almost $14 billion, as per a 2020 Markets and Markets report, as they offer immense potential for automating customer conversations.
To do so, AI-based conversational systems combine a variety of technologies including automatic speech recognition (ASR), natural language processing (NLP), machine learning (ML), and more to understand, react to, and learn from each interaction.
What Is Conversational AI? Definition
Conversational AI is a set of technologies that work together to automate human-like communications – via both speech and text – between a person and a machine.
At first, these systems were script-based, harnessing only Natural Language Understanding (NLU) AI to comprehend what the customer was asking and locate helpful information from a knowledge system. Yet, over time, they have evolved significantly.
Now, they even learn from previous interactions, various knowledge sources, and customer data to inform their responses. Nevertheless, the design of bots is generally still short and deep, meaning that they are only trained to handle one transactional query but to do so well.
If the contact center wishes to use a bot to handle more than one query, they will likely require a master bot upfront, understanding customer intent. It then filters the contact through to another bot, which resolves the query. Although, such a systems architecture is invisible to the customer.
In the future, fully autonomous virtual agents with significant advancements could manage a wide range of conversations without human intervention.
Components of Conversational AI
Advanced conversational AI systems have four key components:
1. Machine Learning
Machine learning consists of algorithms, features, and data sets that systematically improve over time. The AI recognizes patterns as the input increases and can respond to queries with greater accuracy.
2. Natural Language Processing
Conversational AI uses NLP to analyze language with the aid of machine learning. Language processing methodologies have evolved from linguistics to computational linguistics to statistical natural language processing. Combining this with machine learning is set to significantly improve the NLP capabilities of conversational AI in the future.
3. Data
The success of conversational AI depends on training data from similar conversations and contextual information about each user. Using demographics, user preferences, or transaction history, the AI can decipher when and how to communicate.
4. Conversation Design
Organizations must develop the content that the AI will share during the course of a conversation. Using the best data from the conversational AI application, developers can select the responses that suit the parameters of the AI. Human writers or natural language generation techniques can then fill in the gaps.
How Does Conversational AI Work?
Conversational AI allows humans to interact with machines through four steps:
Step 1: It starts with receiving information from the human, which may be a written text or spoken words. As the input is spoken, voice recognition converts it into text that is in a machine-readable format.
Step 2: The second step is for the application to grasp what the text means. To understand the intent behind the text, conversational AI uses natural language understanding (NLU) – a part of NLP algorithms.
Step 3: Utilizing dialog management, the application determines the response based on its understanding of the text’s intent. By orchestrating responses and converting them into a human-readable format through natural language generation (NLG), dialog management forms the other aspect of NLP.
Step 4: Depending on the platform, either the application delivers the response in text or uses speech synthesis (artificial generation of human speech) so that the user receives it over a voice channel.
Discover more about how chatbots – a common use case of AI – function by reading our article: How to Bots and Chatbots Work
Benefits of Conversational AI
Using conversational AI, organizations can:
- Automate Customer Interactions – Conversational AI shares answers to simple, transactional queries. It also provides personalized advice – with a CRM integration – quicker than the contact center is likely to do so.
- Replace the IVR – Siri, Cortana, and Alexa are all examples of conversational AI that we use every day. The use of voicebots enables organizations to follow this trend, personify a brand, and meet the needs of each customer using their preferred channel. Such a process helps contact centers overcome clunky IVR processes.
- Increase Sales – Conversational AI can facilitate a consistent and convincing selling strategy. For example, a chatbot that tracks how a customer uses the website can offer support when they take a long time to check out. Also, it can proactively reach out to a customer with a discount on a product that they revisit but never purchase to drive sales. Subscription offers may also work well.
- Conduct Sentiment Analysis – With advanced conversational AI, businesses can analyze customer sentiment and fine-tune processes. For example, many conversational AI systems categorize interactions as positive, negative, or neutral based on the customer’s use of language. Through this process, a chatbot can respond accordingly and provide a more personal experience.
- Reduce Costs – Conversational AI lowers staffing requirements, handling tasks such as answering customer queries at no extra charge. It also requires no time off and is not prone to human error.
Conversational AI is still in its infancy, and commercial adoption has only recently begun. As a result, organizations may have challenges transitioning to conversational AI applications, just as they do with any new technology. Yet, while the technology is far from plug-and-play, advancements in each of the central components of conversational AI are driving up adoption rates.
Discover the market leaders in conversational AI by reading our rundown of the Gartner Magic Quadrant for Enterprise Conversational AI Platforms 2022
