While Conversational AI chatbots have revolutionized text-based conversations, voice assistants have long promised to redefine the way we engage with technology, offering an even more natural and intuitive experience.
Conversational AI (CAI) voice assistants are software applications powered by AI designed to talk with people using spoken human language. In other words, it’s how to make AI speak.
These voice assistants rely on great conversation design and cutting-edge technologies to understand spoken commands, answer questions, and perform tasks.
However, to deliver great experiences, the challenge is steeper than with text-based Conversational AI applications. This is because humans communicate in a less organized way when speaking than when writing. The AI has to work harder to properly understand the meaning behind the sounds we are making when we speak.
AI voice assistants employ sophisticated algorithms to analyze and interpret human speech. By leveraging machine learning and natural language processing (NLP) techniques, these assistants can be continuously improved over time. And now, with the advent of LLM’s and Generative AI, the flexibility of voice assistants in both understanding and delivery is expected to increase dramatically.
Voice assistants, also known as voice bots or virtual assistants, can be deployed via various channels, including smartphones, smart speakers, call centers and integrated products. CDI experts have gained experience in products ranging from Google Home units, talking coffee machines, telephony IVR for health insurers and much more.
There are two main types of voice assistant:
Much like chatbots, these voice assistants follow predefined rules and patterns to address user queries. They are ideal for managing structured interactions and delivering consistently correct information. This means that they can only react to unanticipated requests by going into some form of error handling (which might include handing over to a human agent).
Generative voice assistants can generate speech, translate languages and answer questions in an open-ended manner, even using humor or sarcasm. This makes them a great option for complex interactions and less predictable situations.
However, the practical application of generative voice assistants is somewhat limited by their propensity to making up answers (‘hallucinating’) or simply making mistakes. This risk can be offset to some extent by using Retrieval Augmented generation (RAG) solutions: grounding the answers in a predefined data set, such as a corporate knowledgebase. Even then though, it’s necessary to create coded guardrails - in short: there’s no ‘easy fix’.
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Voice assistants can be used in many different ways. Practically any application where one can imagine either picking up the phone and calling with a query, or simply asking a real person for help. Let’s look at some examples:
Voice assistants can handle customer inquiries, provide real-time support, and assist with issue resolution, improving customer satisfaction and reducing support costs.
Voice assistants can augment call centers, handling routine inquiries and providing initial support, reducing wait times and improving agent productivity.
They can offer product recommendations, assist with order processing, and facilitate personalized marketing campaigns, driving sales and revenue growth. Consider also the possibilities of pro-active voice assistants.
Voice assistants automate HR tasks, provide onboarding and training materials, and connect employees to internal resources.
Voice assistants provide medical information, schedule appointments, and manage patient records, improving patient engagement and satisfaction.
Voice assistants can be integrated into products, such as smart speakers and home appliances, to provide hands-free control and guidance.
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