Conversational Artificial Intelligence (AI) is a collection of AI technologies that enables machines (software programs) to interact with humans in their language. It uses Natural Language Processing (NLP), Machine Learning, and Generative AI to process and respond to user inquiries in real time. Today, conversational AI technology is used in virtual assistants and AI chatbots like Alexa and Siri. Conversational AI can also help businesses automate their customer support.
How does Conversational AI work?
On a fundamental level, conversational AI is an AI model made up by combining other AI models. Think of a company’s organizational structure. While the primary goal of a company is to generate revenue, each department contributes differently towards this goal. Similarly, conversational AI uses other AI technologies in conjunction to hold human-like conversations.
- Natural Language Processing (NLP): NLP enables AI to process natural language. It can break down speech and text input into data that a system/machine can analyze and interpret. This way AI can understand both spoken and written words. Under the NLP umbrella, we also have Natural Language Generation (NLG) which in this case, allows the machine to respond using speech and text.
- Natural Language Understanding (NLU): This technology allows a machine (software program) to interpret and understand the intent behind human language.
- Machine Learning: As the machine analyzes and interprets responses, it gets better. This is machine learning, where the machine learns with each interaction. Over time, this makes it smarter. The data it collects is known as training data, the more data the machine has, the better it gets for that use case.
Optimizing your Conversational AI Workflows
The type of conversational AI that you use will dictate what you can do to optimize it. Specific optimization for each model would look different, but the fundamentals would remain the same. Here’s what to look out for:
- Comprehensive Training Data: All AI models need comprehensive data to train themselves. Comprehensive data means both the quality and quantity of data given to the AI. In this case, it would be both verbal and written data. Good data makes for capable conversational AI models. Within Phonely, we use knowledge bases and your website to form the basis of this data. The AI can then pull from this information as it deems necessary.
- Smooth Conversational Flows: The data by itself is not going to be sufficient. The AI needs to have conversation flows that it will follow based on the user input. Conversations and questions elicit responses, from the user and the AI. Using a workflow to guide the conversation will set the required guardrails for the AI to work effectively.
- Agent Handoff for Complex Queries: AI-powered chatbots and voice apps are very competent when they have a solid knowledge base and a guided workflow. However, complex queries are still better handled by a human agent. Within any customer service task, the customer should not feel that it is difficult to speak with a human. Have points within workflows where a call gets transferred to a human agent when the query gets complex.
Business Use Cases for Conversational AI
Conversational AI is a strong proposition for businesses handling multiple customer interactions using chat or phone. It primarily helps you improve customer experience while lowering your operational costs.
- Scalable Customer Support: Conversational AI can help you manage customer inquiries at scale without having a large team of agents on-call. A conversational AI solution like Phonely can handle 1000s of inquiries using AI workflows. This keeps agent requirements to a minimum.
- Instant, 24/7 Support: Unlike human agents, conversational AI is live 24/7. There are no wait times because conversational AI solutions can handle multiple calls at once.
- Cost-Effective: By automating common inquiries, businesses can reduce staffing requirements. Agents are only necessary for complex queries.
- Better Customer Experience: Customers don’t have to wait in a queue to speak to an agent, this reduces wait times to 0. When customers use AI to resolve basic queries, their net time for issue resolution goes down. This decreases average handle time. Our post on CCAI digs deep into the impact of AI in Contact Centers.
Conversational AI tools have changed how we as humans interact with machines. Using AI models for machine learning, natural language processing, and natural language generation; these tools are capable of holding a human conversation all by themselves. With the right workflows in place, you too can tap into the benefits of conversational AI.
