what is it and how to use it

Conversational AI vs Chatbots

Conversational AI and chatbots are often discussed together, so it is important to know how they are related.

Chatbots are one application of conversational AI, but not all chatbots use conversational AI. Most chatbots are rule-based, where they come pre-programmed with specific canned responses and scripts and cannot handle more complex conversations.

The AI ​​chatbot can handle a wide range of conversations and topics and uses data to deliver the most accurate responses.

How does conversational AI work?

Conversational AI exists through machine learning, natural language processing (NLP) and natural language generation (NLG).

Machine learning is how a conversational AI tool gets its intelligence. It starts with human input, where someone feeds a unique data set to the machine to learn from. It studies the data, understands the connections and eventually gets ready to have real conversations with real humans.

Natural language processing is the ability of a machine to recognize words and phrases from human interactions as it is learned from raw data. The tool then uses NLG to develop the best possible responses to human queries.

Conversational AI gets better and more accurate over time because it continuously learns from every conversation.

The overall process is:

1. Input is received in the form of text or audio (spoken words or normal sounds).
2. The machine analyzes the input with natural language processing to uncover what the input means and what the response might include.
3. Once the input is understood, conversational AI provides the best and most accurate information (NLG) to the user.

Machines use the data from each interaction to build knowledge and generate more accurate responses.

Examples of conversational AI

A common marketing application of conversational AI is content generation tools that research online topics and create content outputs such as blog posts, emails, and even ad copy.

Benefits of Conversational AI

With these examples in mind, what benefits can conversational AI bring to business?

1. Conversational AI can save time.

Conversational AI can take charge of conversations with consumers and drive relevant results, helping teams focus on more pressing issues that require a human touch.

Conversational AI can also process massive amounts of data points and quickly bring insights and answers to business teams, helping make data-driven decisions and freeing them from the burden of data processing.

2. Conversational AI provides data-driven insights

The data that conversational AI tools collect can be a useful resource for businesses to learn about consumers and what they want, be it commonly asked questions that can be used to create an FAQ page This can be done to update or to know how people talk about you online.

3. Conversational AI can increase purchases.

Conversational AI tools can use NLP to understand customer questions, learn needs and pain points, and generate product or service recommendations that inspire purchases.

4. Conversational AI can find the most suitable customers.

Conversational AI can sort through multiple data points to help you find the ideal customer.

5. Conversational AI can do brand monitoring.

As mentioned above, conversational AI can analyze what people say about your business online and scan common phrases and keywords to understand brand sentiment. This is a significant time saver, as marketers can spend less time sorting through hundreds of conversations and interactions.

This is where conversational AI has shortcomings, as nothing can mimic the importance of human understanding.

Challenges of Conversational AI

Conversational AI is an exciting frontier for marketers, but it’s always important to understand the full picture, as there are two sides to every coin.

The most important way brands can go wrong with their adoption of conversational AI is if it takes over tasks that could still benefit from human oversight and interaction.

For example, a device can monitor online conversations, but a human can pick up on subtleties that a machine cannot. An HR tool can sift through job applications to find the best-fit candidates for specific keywords and phrases, but a human reviewer can tell whether a candidate has similar experience that would make them a good fit. Makes a great fit, even if they don’t have targeted keywords in their resume.

Some additional challenges of conversational AI include:

• language input: Dialects, slang, and even background noise can affect the machine’s ability to process language input.
• Privacy: Interactive devices store and collect data to improve their processes, but security or data breaches can lead to security concerns if consumers’ personal information is exposed.
• Human and Cultural Development: Machine learning must continually progress to learn alongside human cultural evolution, whether general cultural knowledge or something more specific like the showtimes for a newly released movie.

Conversational AI Statistics

AI is an ever-evolving field. If you are confused about whether to take it up or are just looking to learn more about this field, here are some important statistics to know.