In short
Conversational AI covers any system built to take part in a dialogue with a person. It has to understand the input, keep track of the conversation so far, decide what to say or do next, and produce a reply. For text, the reply is written. For voice, speech-to-text and text-to-speech are added at either end. Most modern systems use a large language model for the understanding and the reply, with business rules and data connections around it.
How it has changed
Conversational software is older than most people assume. ELIZA, written by Joseph Weizenbaum at MIT in the mid-1960s, imitated a therapist by matching patterns in what people typed. It understood nothing, but people still confided in it.
For many years business chatbots and voice assistants worked on intents. A designer listed the things a customer might want, such as “check opening hours” or “book an appointment”, trained the system to recognise each one from example phrases, and wrote a scripted reply. They worked when customers stayed on script and failed when they did not.
Large language models changed the understanding step. They can follow a loosely worded request, cope with a message that contains three questions, and write a natural reply. The design work has moved from listing every phrasing to writing clear instructions, supplying the right facts and setting limits.
The main forms
| Website and app chat | Answers questions and captures enquiries in a chat window. |
|---|---|
| Messaging assistants | The same job on WhatsApp, SMS, Facebook Messenger or Instagram. |
| Voice agents | Spoken conversations on the phone, using speech recognition and a synthetic voice. |
| Internal assistants | Help staff find policies, procedures and customer history in plain English. |
| Embodied assistants | Conversation through a physical device or robot, such as a kiosk or a humanoid at an event. |
What makes it work in a business
A narrow job. “Answer questions about our services and book a survey” works far better than “be our customer service”. The narrower the job, the easier it is to test and the less can go wrong.
The right information at the right moment. A language model does not know your prices, service areas or policies. They have to be supplied, usually through retrieval-augmented generation from an approved knowledge base, so answers come from your material and can be traced back to it.
Connections to other systems. A conversation that ends with “someone will be in touch” but writes nothing anywhere has only moved the problem. The useful versions create a CRM record, a booking or a task.
A clear exit. Every conversation should have a route to a person, and the system should take it when it is unsure, when the customer asks, or when the topic is sensitive.
Common mistakes
Letting it answer from general knowledge. A model with no approved source will produce plausible guesses about your business. Give it the facts and tell it what to say when the facts are missing.
Hiding the human option. Customers who cannot reach a person get frustrated quickly, and a frustrated customer reflects on the business, whatever the software did.
Launching without review. Read real conversations every week for the first month. Most improvements come from small changes to instructions and the knowledge base.
Measuring the wrong thing. The number of conversations says little. Track how many ended in a booking, a captured enquiry or a resolved question, and how many were handed to a person.
When this matters
Relevant when customers or staff ask the same questions repeatedly by phone, chat or message. Echo One is New Era AI’s platform for spoken conversations, and Jimmy, our humanoid robot, shows the same conversational engineering in a physical body.
Questions
Is conversational AI the same as a chatbot?
A chatbot is one kind of conversational AI. The term also covers voice agents, messaging assistants and internal helpers.
Does conversational AI understand what people mean?
Modern systems are good at working out intent from loosely worded requests. They can still misread ambiguous or unusual requests, so important actions should be confirmed with the customer.
Can it work in Welsh?
Many modern language models can read and write Welsh, with quality that varies by model and task. Test it with real Welsh-language enquiries and have a fluent speaker review the replies before relying on it.