Conversational AI: Everything recruiters need to know 

In this guide, we’ll ‌break down what conversational AI is, some differences between chatbots and conversational AI, and how conversational AI helps organizations like yours engage customers and deliver services more effectively. 

Introduction

Consumers' conversations with businesses frequently begin with conversational artificial intelligence (AI), which is the technology behind automated messaging intended to mirror human interactions. Natural language processing (NLP) systems are used to provide human-like interactions by recognizing speech and text, as well as comprehending a variety of inquiries and languages. This program is frequently utilized before customers communicate with a real person to further narrow down their questions. However, it is simpler than you might think. If you’ve ever interacted with a live chatbot, there's a good chance it was powered by conversational AI. You've likely used the technology firsthand if you've ever used smart speakers like Siri, Google Home, or Alexa.

In this article, we’ll discuss how implementing conversational AI will help your business succeed.

What is conversational AI?

Conversational AI is a type of artificial intelligence that enables consumers to interact with computer applications the way they would with other humans. Conversational AI refers to the artificial intelligence that powers chatbots and voice assistants. The technology is still new, but it is rapidly improving and expanding.

In contrast to the restricted capabilities that a person may encounter when interacting with a typical chatbot, a conversational AI chatbot can answer frequently asked queries, troubleshoot problems, and even initiate small talk. While a static chatbot is generally available on a business website and restricted to textual conversations, conversational AI interactions are intended to be accessed and carried out via a variety of channels, including audio, video, and text.

5 core components of conversational AI

We can break conversational AI down into five core components. The following five components work in tandem to enable a computer to understand and respond accordingly to human conversation:

1. Natural language processing

Natural language processing (NLP) is the ability of a computer to interpret human language and respond in a natural manner. This implies comprehending the meaning of phrases as well as the structure of sentences, as well as being able to deal with idiomatic expressions and jargon. NLP wouldn't be possible without machine learning. To train computers to understand language, algorithms use sizable data sets that show relationships between words and how those words are used in various contexts.

2. Machine learning

Machine learning is a branch of computer science that lets computers acquire knowledge without being specifically programmed. Machine learning algorithms may automatically improve as they are immersed in more data. Machine learning allows computers to read and learn from language, as well as discern patterns in data. It can also create models of different systems, like the human brain.

3. Text analysis

The goal of text analysis is to extract information from written data. The subject, verb, and object are all examples of sentence parts that must be identified. It also entails recognizing the many types of words in a sentence, such as nouns, verbs, and adjectives. There are many reasons to analyze text, including understanding the meaning of a sentence and identifying the relationships between different words. You can also use text analysis to discover the topic of a piece of writing, as well as its overall sentiment (whether it is positive or negative).

4. Computer vision

Computer vision refers to a computer's ability to interpret and understand digital images. This involves being able to identify different objects in an image, as well as the location and orientation of those objects. Computer vision algorithms analyze images to identify their contents as well as the relationships between different objects in the image. They can also interpret the emotions of people in photos and understand the context of a photo.

5. Speech recognition

Speech recognition is the capacity of a computer to comprehend human speech. This refers to identifying the many voices in a spoken phrase, as well as the sentence's grammar and syntax. Essentially, speech recognition takes what you say and turns it into editable text. However, its utility doesn't stop there-- this technology can also be used to gauge the emotions of those speaking in a video or conversation, as well as understand the general context.

Conversational AI

How does conversational AI work

 

Conversational AI requires a variety of backend procedures and workflows. This starts with the beginning of the interaction when a human makes a request. The solution extracts the meaning of the words transmitted using natural language processing (NLP). After the platform has handled the words transmitted, it employs natural language understanding (NLU) to comprehend the client's intended question.

The platform employs machine learning in order to understand how best to respond to a customer's question. Machine learning makes the AI application more accurate over time as it is constantly learning from human interaction. In addition, the platform uses natural language generation to turn the data it gathered from the interaction into a written response that is easy for the customer to understand.

Conversational AI vs. chatbots

Although this software may seem similar, it shouldn't be confused with chatbots. AI chatbot software is a type of AI that uses natural language processing (NLP) and understanding (NLU) to create human-like conversations. While these tools can still speak with humans, their capabilities are much more limited. Chatbots usually only respond to keywords and are designed mostly for website navigation help.

Conversational AI usually works in a similar way but is much more effective since it can interpret human speech and text, understand the speaker's intent, and even identify different languages. The software uses automated speech recognition to listen to interactions, natural language processing to comprehend it, and natural language generation (NLG) to offer a response that is similar to what a human would say.

Here’s a breakdown of the different features you can expect in conversational AI vs chatbots:

Feature

Conversational AI

Chatbot

Natural Language Processing (NLP)

Contextual awareness

x

Intent understanding

x

Integration, scalability, and consistency

x

 

Our featured offer 

Try Tengai for free

Contact us

Benefits of using conversational AI in business

Conversational AI is a cost-efficient solution for many business processes. The following are examples of the benefits of using conversational AI.

Conversational AI helps you save money, no matter your industry

One of the most significant advantages of this program is that it may help your company save money. More specifically, you may scale your support department at a lower price. Running an effective support staff necessitates spending money. Sales management AI uses data from a company's customer base to help companies optimize their marketing performance. This implies you can quickly discover a client's demographic, psychographic, and other characteristics. As a result, implementing this AI into your software architecture may save money on consultants and outsourcing analytics.

Improve your customer service with conversational AI

Your customers want answers fast when they reach out to your customer support team. Valuing their time is the most important thing companies can do to provide good customer service — conversational AI can help with that. This software can easily improve your customer service team's productivity and efficiency.

Conversational interfaces, such as live chat, now have the capacity to employ AI technologies thanks to the quick adoption of deep learning, allowing for real-time engagement. Calls may be routed automatically by an intelligent virtual agent or chatbot using customer support chats and IVR systems. These systems may be integrated with CRM to allow for unprecedented levels of personalization.

In a similar manner to instant messaging, the bot detects questions and answers them, looking for specific keywords or phrases that a consumer might use to notify an issue (such as "damaged item" or "track package"). The bot begins to recognize typical events and provide the best solution it can. This functionality has now been integrated into social media platforms such as Facebook Messenger. Customer wait times may be drastically decreased with the speed of this AI. Customers feel valued when fast response times are provided. This quickness allows your support staff to be accessible 24 hours a day, seven days a week. If your firm doesn't have the financial means to outsource overnight service, you could miss out on servicing specific sections of your consumers during their workdays.

Conversational AI can increase your conversions

These AI systems not only improve service for your current customers, but they can help increase sales and conversions from potential leads. The software's automation capabilities make the process of turning a lead into a customer much quicker and easier. This tool can help your business quickly weed out bad leads and sort them by relevance and potential to become customers. The lead scoring feature will assess each lead's value and pass on the most promising ones to your sales team. Your platform may become an SDR if your sales staff is overworked. These platforms can function as virtual assistants to your team members, helping them with the often time-consuming chores that consume a lot of their time as SDRs.

Building automated bots and AI solutions can create more engaging customer interactions that are not hindered by distractions or delayed answers. Using content analysis, optical character recognition, and machine learning to create a more precise user experience is possible for chatbot builders.That way, they can artificially replicate human interaction patterns. These software solutions will propel your business into the future, giving you an edge over your competition.

With conversational AI, it's simpler to collect customer data.

Having extensive customer data is pivotal for businesses, and conversational AI sifts through mountains of information to help you find what you need quickly and easily. With traditional data mining tools, it can be difficult to sift through all of the noise to find needle-moving assumptions about potential customers' likes or needs.

AI solutions like these learn from other customers' experiences. Because these technologies can mimic deep and sophisticated conversations that people have with one another, consumers who contact your representatives will feel as if they're receiving individualized attention. Customer support chat may be one of the most frequent cases in which this technology is used. Integrating your tool with an automatic semantic understanding solution (ASU) will benefit your business by informing your virtual agent of what to look for in customer interactions. Since your tool can be available 24/7, you'll be able to gather data about customers continuously.

You can scale your business with conversational AI

Not only is conversational AI cost-effective, but it can also be quickly and easily scaled to meet changing demands. This makes it ideal for businesses that are expanding into new markets or for those who experience spikes in demand during peak periods, such as the holiday season.

When considering AI's impact on scalability, it's important to look at not just the technology investment required versus hiring more people. AI also changes how your agents will work, making them more productive overall. The value is all quantifiable, based on key performance indicators like efficiency, according to the report. When you have a consistent, repeatable AI-supported procedure in place, scale will come as a natural consequence that does not require additional money or personnel.

The screening interview should be convenient, rewarding and efficient for candidates.

Sinisa Strbac, Chief Product Officer at Tengai

Conversational AI and real-world issues

Conversational AI is expanding and offering benefits to many industries, including but not limited to:

Healthcare

Conversational AI can help patients describe their conditions online through a series of questions meant to circumvent wait times.

Retail

AI-powered chatbots provide 24/7 customer support, which was previously unavailable through call centers and in-person visits during traditional office hours. With AI chatbots, businesses are no longer limited to providing customer service through only one medium or channel.

Banking

AI chatbots can relieve the strain on bank staff caused by having to deal with complex requests in a way that normal chatbots would find difficult. AI assistants are already present in most homes via devices such as Amazon Echo and Apple's Siri. Conversational AI agents may also be interacted with through smart home technologies.

HR

Instead of manually looking through candidate credentials, which can take a lot of time, Conversational AI can do it for you. For example, in the banking industry, conversational AI assists human workers by lightening their load.

Conversational AI

Challenges of conversational AI technologies

Conversational AI is a relatively new field, and widespread corporate adoption has only recently begun. Conversational AI apps have some difficulties with making the transition from conventional applications. Some challenges include:

Global users 

A major obstacle to conversational AI development is that they have only trained these models using English, not providing bilingual or multilingual options for global users. 

Language input

One issue conversational AI often runs into is language input. Whether the input is text or voice, dialects, accents, and background noise can all affect the AI's understanding of the raw data. Slang and unscripted language can also create problems with processing the input. However, the biggest barrier to conversational AI is the language input human element. Conversational AI finds it tough to interpret the intended user meaning and react appropriately due to emotions, tone, and sarcasm.

Privacy and security

Conversational AI, like most machine learning applications, is susceptible to data breaches and privacy concerns. Building trust among consumers by developing conversational AI apps with strict privacy and security standards as well as monitoring systems will assist in the long run in increasing chatbot usage.

User apprehension

Users may be hesitant to reveal personal or sensitive information, especially if they realize that they're talking with a machine rather than a person. Because your target audiences will not all be early adopters, you'll need to inform them on the advantages and safety of these technologies in order for them to have better customer experiences. This might result in poor user experience and decreased performance of AI technology, which would negate the intended benefits.

Conclusion: Conversational AI will help your business succeed 

If you're looking for a way to better engage with your customers and leads, then conversational AI is the way to go. With this technology, businesses can interact with their target audiences more quickly and efficiently than ever before. Most live chat features already in use by sales and support teams are powered by some form of AI—conversational AI simply takes customer engagement to the next level.

Our above tips for adding live chat to your website will help make sure you're always giving customers the interaction they want and expect.

Ready to start using conversational AI? 

Tengai Unbiased can help you provide exceptional conversational-AI centric customer service to your customers. Turn customer conversations into valuable engagements with our self-learning chatbot.

 

Not sure what you need?