Today’s world embraces digital evolution, enabling conversational AI to gain traction and provide superior customer service. It’s quite challenging to communicate with machines. A CAGR of 21.9% is expected for the global market for AI in conversations over the next three years.
By employing virtual assistants and cost-effective artificial intelligence tools, businesses can bolster their relationships with their customers across multiple digital channels. Moreover, even companies use artificial intelligence-powered by conversational interfaces to capture a larger chunk of the highly competitive market and service their customers’ needs.
So conversational AI can potentially help companies accomplish both goals.
An engaging chatbot can transform the way companies interact with their customers wherever they are and however they want. Chatbots open up a whole new class of business operations for business leaders serving both customers and stakeholders.
However, to gain more insights about AI in conversations and how it works, you can read a detailed article.
Let’s take a look!
Table of Contents
AI in Conversations: What is it?
The term conversational AI refers to the fusion of several communication technologies. The system supports dynamic and fluid response scenarios without a predefined scope.
The conversation-based artificial intelligence technology offers greater flexibility and human-like abilities than rule-based chatbots. As a result, it allows businesses to offer both personal engagements and scalable support – giving them the best of both worlds.
AI in conversations seeks to simplify interactions, provide better understanding, and simplify processes.
How does Conversational AI Work?
AI in conversations combines several high-tech technologies, including natural language processing (NLP), intent recognition, entity generation, and text-to-speech.
A conversational application would work like this:
- When an AI application receives input (whether written or oral), it begins processing the information.
- After that, the Automated Speech Recognition (ASR) technology analyzes and converts the spoken input into machine-readable text.
- Next, a computer application must interpret the input text. Natural Language Understanding (NLU) helps understand the text’s message.
- Using Dialog Management, it formulates its response based on its understanding of the intention of the text.
- Natural Language Generation (NLG) is used for dialogue management to convert responses into a human-understandable format.
- After that, conversational AI applications either deliver text or text-to-speech replies.
- A final step involves the components continuously optimising and learning the application. This is called Reinforced Learning, in which the application learns from its experiences and applies them in future interactions.
Key Components of Conversational Artificial Intelligence
Using AI in conversations, a natural language processing algorithm gets combined with machine learning algorithms. As a result, it interacts with users more intuitively and adapts to their context.
Machine Learning (ML)
An analysis of how human agents respond to users is performed with the assistance of algorithms, features, and data sets.
Natural Language Processing (NLP)
When text can be ‘read’ or parsed, it can understand natural sentence structures rather than simple keywords.
APIs and other business operations tools allow systems to perform more autonomously by enabling their end-to-end functionality.
The benefits of Conversational AI
With Internet use growing increasingly conversational in recent years, businesses need to connect with people and machines more personally. AI in conversations provides the following benefits when embedded in everyday business operations:
Efficiency in terms of costs
Staffing a customer service department can be extremely costly, especially if you need to respond to questions outside regularly scheduled working hours. However, small and medium-sized businesses can reduce compensation and preparation costs by providing customer support through conversational interfaces.
A virtual assistant or chatbot can react quickly, giving potential customers 24-hour access.
Revenue growth via cross-selling and up-selling
An AI in conversations approach helps build loyal clients by connecting and engaging with them proactively. With this approach, you can provide a better customer experience, recommend appropriate products, send promotions to your customers, and upsell and cross-sell products.
Due to conversational AI, users receive immediate responses, which is one benefit of the technology. As an example, we can talk to Alexa, and it will answer rapidly, or when it talks to a customer, the virtual assistant can respond more rapidly than a human.
Efficacy of agents
AI can sometimes handle customer assistance scenarios without the need for human interaction. For example, you can use AI to calculate an account’s balance or find a store’s location. This frees up agents’ time to handle more complex cases that require their attention.
Aspects of scale
As with the technology set, adding infrastructure allows AI in conversations to be processed faster and at a lower cost than recruiting and onboarding new employees. It is particularly relevant since items are growing into new business sectors or when there are momentary spikes of interest, such as during the holiday season.
Use cases of Conversational AI
When they envision automated customer support services or omni-channel deployments based on online chatbots and voice assistants, many people think of conversational AI. AI conversational apps are often designed with extensive analytics built into the back end, helping to ensure human-like interaction.
The applications of AI in conversations are limited to a narrow field of activities. Artificial intelligence, which is still a theory, envisions a consciousness that is similar to human beings and is capable of solving various problems.
While conversational AI is a narrowly focused technology, it can be exceptionally profitable for enterprises, thus improving profits. Even though AI chatbots make up the vast majority of conversational AI, there are many other uses in the enterprise as well.
Below are some examples:
- Customer service online: As the customer journey moves online, chatbots replace human agents. As they answer commonly asked questions (FAQs), cross-sell products, or provide personalised advice, we’re rethinking website engagement and social media engagement.
Bots that handle e-commerce site transactions with virtual agents, messenger apps, such as Slack or Facebook Messenger, and bots that automate tasks normally handled by virtual assistants are also available.
- Providing accessibility: Companies can become more accessible by lowering entry barriers, particularly for users of assistive technologies. For these groups, Conversation AI’s most popular features include text-to-speech dictation and translation.
- The HR process: An AI in conversations can streamline the training and onboarding processes for employees and update employee information and other HR processes.
- Getting health care: A conversational AI system can make healthcare more affordable and readily available, improve operational efficiency, and streamline administrative procedures, such as claim processing.
- IoT devices: In nearly every household, there is at least one IoT device, such as Alexa speakers, smartwatches, and smartphones. End users can communicate via an automated speech recognition program with these devices. The most popular apps are Google Home, Amazon Alexa, and Apple Siri.
- A computer program: Many aspects of an office environment can be automated via artificial intelligence, such as Google search autocomplete and spell-check.
The majority of AI-based chatbots and apps exist now, but their capabilities do not match those of complex problems. Nevertheless, these solutions can reduce the cost and time of repetitive interactions with customers, allowing the resources to be used for more complex ones. Therefore, conversational AI apps have proved to be effective in mimicking human conversations, which leads to higher customer satisfaction.
Conversational AI – Top challenges
Businesses have already started using AI in recent years. However, as with any technology development, the shift to AI in conversations has its challenges. Among examples are:
The language input
Whether via text or voice, language input can be a stumbling block for conversational AI. The AI’s ability to interpret raw text and spoken language input is dependent on its understanding of dialects, accents, and background noise. As well, unscripted and slang words can complicate matters.
Conversing with AI is a massive challenge due to the human factor. Conversational AI does not understand emotions, context, tone or sarcasm; these factors are difficult to assess.
Safety and privacy
Conversational AI collects data from users to answer their queries, so it also poses security and privacy risks. However, businesses can win end users’ trust by building conversational AI apps that uphold high privacy and security standards while incorporating strict monitoring systems.
Fear of the user
Users can be hesitant about sharing sensitive information with machines, particularly when they realise that the conversation is with a computer rather than a human being.
Considering that not all your customers will be early adopters, you’ll need to educate and socialise your target audiences about the benefits of these technologies to improve their customer experience. User experience may be negatively affected, and AI will perform less efficiently, negating its positive effects.
Generally, a chatbot will be unable to answer complex questions. Therefore, an alternative communication channel will be needed to handle these more complex topics. Furthermore, when users receive an incorrect or incomplete answer, they will be frustrated. In these cases, they can speak with a representative of the company.
Last but not least, conversational AI can optimise the company’s workflow, thereby reducing the need for particular employees. Companies will face a backlash if social activism triggers.
The popularity of conversational AI is well-founded. Artificial intelligence is being used by companies to serve their customers better, manage their marketing campaigns, and improve their overall customer experience.
People care more about their interactions with companies nowadays. They tend to expect instant, effortless solutions across various channels and can turn away from a company after just one bad experience.
By providing instant answers to everyday questions and issues, conversational AI can help companies scale customer experiences. Thus, human agents are only hired when customers have complex, sensitive, or unique needs.