The following are the key components of conversational AI:
1. An user value proposition that is clearly defined.
Now consumers and employees connect with your company via the web, mobile, social media, email, and other platforms. Consider the scenarios where there is friction or annoyance if the engagement is already conversational. For example, where people may have to wait a long time for a response, switch between apps, or frequently input data.
If the conversations are mostly informational, they may be suitable candidates for conversational AI automation or partial automation. However, they may be appropriate candidates for conversational augmentation if they are more intricate.
This includes creating an appealing character, selecting the correct messaging platform and channel, polishing the dialogue flow, and ensuring that a conversational interface is well-suited to the work at hand. For conversational upgrades, you’ll need to figure out when the system should provide ideas to the human agents or users and then design the interactions to make them seamless and natural without being obtrusive.
Since both conversational agents and conversational improvements allow people to communicate with you, you’ll need to figure out how to generate the material they provide. If you already have conversational data, you may curate the best of it and utilize it as the foundation for your best conversational AI application’s responses. To fill in the gaps where conversational data is unavailable, you’ll need to use human authors or natural language generating tools.
4. Language Technology.
When dealing with voice interfaces, you’ll almost certainly need to employ speech-to-text transcription to generate text from a user’s input and text-to-speech to convert your responses back to audio. Language understanding techniques such as sentiment analysis, question classification, intent identification, and entity and subject extraction are likely to be relevant for both speech and text interfaces to grasp what the user is saying.
5. Other capabilities of machine learning.
You might wish to apply machine learning models in addition to language technology to help set the stage for a successful encounter and give value to the user.
6. Loops of feedback.
Each discussion should increase your ability to design a successful conversation while also updating your understanding of the user. You might directly ask the user for feedback after the chat, or you could look at downstream behaviour (such as if they re-engage or if the conversation leads to conversion) and utilise that information to optimise the next conversation.
7. Confidentiality and safety.
As conversational contact between bot and customer can be casual and natural, and the data can often contain sensitive information, so careful technical and policy treatment is necessary. At the same time, you’ll want to make sure you can use the data you’re gathering in the future to improve the user experience.