Conversational artificial intelligence (AI) is a branch of AI that uses machine learning and natural language processing (NLP) to interact with humans. Its primary purposes are to address problems and influence customer interactions.
For example, online retailers use conversational AI to help customers search for products and troubleshoot purchasing issues. Banks use it to suggest services according to the user’s risk profile and financial status. Large language models (LLMs) even enable conversational AI solutions to assist in critical sectors like healthcare.
In this article, we introduce the key concepts of conversational AI and address crucial factors to consider when you incorporate conversational AI into your business.
How does conversational AI work?
Conversational AI guides conversational messaging systems that interact with humans and accomplish tasks using natural language. These systems engage with customers through social media channels, such as Facebook, WhatsApp, or Google Business. With conversational AI, systems can understand and generate natural language, recognize context, and maintain conversation.
The NLP concepts that conversational AI systems are built upon include:
- Named entity recognition
- Intent classification
- Dialogue management
- Sentiment analysis
Additionally, the systems consist of either a bot framework or an LLM. Let’s take a look at the differences between them.
- Utilize cloud-based services (such as Google Dialogflow, Microsoft Bot Framework, and Amazon Lex V2)—or open-source frameworks (such as Rasa).
- Enable developers to create training phrases to identify user intent, annotate relevant entities, and create conversational flows.
- Leverage training data obtained from billions of sentences on the internet.
- Perform common NLP tasks without the need for training phrases (also known as zero-shot learning).
- Allow developers to fine-tune models with small data for specific domains.
LLMs are better at understanding natural language than bot frameworks. Although LLMs can be difficult to control and are often inaccurate, new advancements, such as ChatGPT-4, show greater accuracy.
Should my business use conversational AI?
AI systems require a considerable investment of resources from technical and business teams. So consider the following 3 questions before you implement conversational AI.
1. Can conversational AI help increase revenue?
Conversational AI can increase revenue by driving sales. For example, an ecommerce website can use a chatbot to promote products to customers.
2. Can conversational AI help create new products?
Customer interactions with conversational AI can generate valuable data for product development. For example, as customers interact with a conversational AI system to select clothes, the system might recognize patterns in their requests for specific materials. These insights could help businesses create new products to meet customer needs.
3. Can conversational AI help reduce costs?
Conversational AI can reduce human involvement and manual processes, resulting in improved resource utilization and cost efficiency. For example, chatbots are accessible 24/7, allowing contact centers to only engage human agents when necessary. AI-based customer service systems that shoulder some of the workload can improve resource allocation and reduce costs.
Benefits of conversational AI
The suitability of conversational AI for your business depends on the benefits it offers. Let’s consider 5 key advantages of conversational AI.
1. Increased revenue
Conversational AI helps organizations sell more and increase revenue. By serving as an always-available sales assistant, it can guide customers to the right products through conversation and natural language generation.
2. Enhanced customer experience
Conversational AI can improve the customer’s online experience by guiding them through their purchasing journey. This approach can replace the use of traditional ecommerce website search categories and reduce the number of steps in the purchasing process. For example, a request like “I want an investment scheme with built-in life insurance” is more efficient than browsing through a category tree on a website.
3. Informed innovation
Conversational AI can analyze the conversation history of customer interactions and help you gather insights. For example, why your customers choose certain products over others or why customers are unsatisfied with specific products. You can use this data to drive innovation of your products and services.
4. Reduced operational costs
A conversational AI system that is always available and requires no human intervention can resolve customer issues promptly and efficiently. For example, in retail, it can help customers by facilitating product returns, providing delivery estimates, or processing a replacement—all actions that improve customer satisfaction and drive brand loyalty.
5. Improved productivity
Conversational AI solutions can improve employee productivity. For example, a customer service agent can leverage conversational AI to help customers with frequently asked questions or reduce resolution times by using AI to guide them through the organization’s massive knowledge base.
Chatbots versus conversational AI
Chatbots existed before significant NLP advancements led to conversational AI as we know it today. Let’s explore 4 key differences between chatbots and conversational AI systems.
1. Predefined content versus dynamic content
Chatbots operate based on straightforward rules and often rely on predefined content. As a result, these bots can’t generate natural language responses that simulate human behavior.
Conversational AI systems use state-of-the-art NLP models that generate complex sentences independently, understand the user's intent, and respond as humans would.
2. Simple use cases versus complex use cases
Chatbots are suitable for simple use cases that use predefined content, such as the FAQ section on a website.
Conversational AI systems are best suited for complex use cases that require subject matter knowledge and longer conversational journeys. For example, a conversational AI system can handle an entire business process like a ticket rescheduling request.
3. Rule-based logic versus language model-based implementation
Chatbots use simple, predefined rules to handle various scenarios. These scenarios often contain a collection of if-else rules.
Conversational AI systems rely on LLMs to identify user intent and respond with self-generated sentences that mimic the nuances of human conversations.
4. Simple understanding versus reasoning capability with context resolution
Chatbots understand customer queries in simple, natural language and thus can’t reason with customers or determine context. Compared to conversational AI systems, chatbots are rudimentary but can still support various use cases. Read more about chatbot benefits.
Conversational AI systems can reason with customers, understand their references to previously mentioned entities, and interact with them like a human would.
Get started with conversational messaging with Twilio
Conversational AI systems help increase revenue, reduce costs, and fuel the innovation of new products.
If your organization needs an all-in-one customer engagement platform that incorporates conversational messaging, Twilio has you covered.
Twilio’s platform provides a global infrastructure to engage customers through digital channels and integrates with state-of-the-art cloud-based conversational AI systems. For more information, read about Twllio's messaging solutions or get started for free now.
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