Types of Conversational AI stacks

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Conversational AI is the collection of tools and techniques behind artificially intelligent speech-enable software which provides human-like interactive experiences between humans and computers. In a broad sense, this technology can be described as combining artificial intelligence with human creativity and experience in order to give users access to personalized experience while conversing over the phone. In practice, this means that the phone becomes an extension of the user’s own consciousness, allowing him or her to engage in highly sophisticated forms of conversation. This emerging field is fast becoming a mainstay in corporate communications and has the potential to revolutionize the way companies interact with their customers.

What makes conversational ai allow bots to achieve this level of engagement? One major component of the agent’s learning process is reinforcement. It presents the user with a series of opportunities to create and test new conversations. The Bot feels as if it is a real person with the right skills and knowledge, so it is very likely to pick up on new skills or learn new things that it would otherwise not have been able to do on its own. As it learns, the Bot becomes more intelligent, picking up on patterns from the conversations it engages in and even mimicking its human handlers in some cases.

Another important ingredient of conversational AI is natural language processing (NLP). Natural language processing (NLP) refers to the ability to extract meaning from natural conversations and leverage it to provide meaningful responses. By using carefully selected natural conversations, conversational AI software can make inferences and even pro-actively attempt to solve problems based on previously observed patterns. Such solutions will generally be more robust and accurate than those provided by automated systems, as they are constructed using carefully selected, previously studied material. Such tools therefore create much higher quality intelligence.

The value of such an approach lies in the fact that conversational AI allows for a much deeper and more detailed response than any purely robotic assistance could ever provide. Humans are social animals and inherently good at expressing agreement, disagreement and other human emotion in the presence of others. This is one of the fundamental differences between machines and humans – machines are made to perform monotonous tasks in repetitive fashion; humans are naturally flexible and creative in their mode of interaction. Thus, the ability to engage in conversations, along with the associated benefits of Conversational AI, represents a huge potential gain for businesses and the technology they employ. Indeed, businesses that use conversational AI will experience significant gains in customer retention, reputation management and so on.

In order to take advantage of conversational AI technologies, businesses need to ensure that they have the right linguistic infrastructure in place. Today’s business environment is highly competitive and customer demands for a unique selling point (USP) are constantly evolving. This means that today’s customers will expect more from business owners and agents, and are willing to spend more for perceived value. However, to engage with these new customers, agents and businesses need to be able to fully understand the customer’s language, which typically comprises three distinct layers.

The first layer refers to the set of standard verbal communications used to communicate with customers. This layer of language enables businesses to provide detailed descriptions of services, products and the functionality of a product in an easy-to-understand manner. In short, conversational AI involves using everyday language practices to ensure that conversations are as close to real life as possible. Conversational AI systems enable computers to quickly filter through unproductive responses and provide relevant responses based on what a customer is likely to be searching for. For example, if someone is browsing the web and comes across a product specification, the conversational ai system can extract useful information from the irrelevant data and suggest a reasonable purchase option based on the specifics of the situation. Similarly, business owners can use conversational AI to suggest reasonable shipping options and payment plans based on an individuals’ individual travel needs and preferences.

The second layer of conversational AI falls into the third category of nLP or “neuro-linguistic programming.” NLP is a field of study that studies how humans interact with one another based on their natural language processing skills. By using the same technology that businesses apply to standard speech transcription, conversational AI systems can capture users’ natural language input and apply it to different situations. NLP can then help business owners understand their customer’s intent, as well as helping them to create more engaging virtual interactions with their clients.

A final layer of the conversational a stack is defined by the fourth and final layer of nLP or artificial intelligence. This layer refers to the actual recording of a conversational experience. Businesses can then use this recorded audio stream as the basis for future training or as a testing platform before introducing live agents to their customers. NLP is heavily involved throughout the recording process, but it also provides businesses with the ability to discard problematic interaction outcomes and to make any necessary changes prior to live recordings. In short, the final layer of conversational ivr is very similar to the first three layers of a conversational AI stack. However, it provides businesses with the ability to leverage their own audio experience and apply it to any type of business need.

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