Conversational AI

What is conversational AI?

Conversational artificial intelligence (AI) refers to technologies, such as chatbots or virtual agents, that users can talk to. They use large volumes of data, machine learning and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages.

Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms.

Components of conversational AI

Conversational AI has principle components that allow it to process, understand and generate response in a natural way.

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.

Natural language processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.

NLP consists of four steps: Input generation, input analysis, output generation, and reinforcement learning. Unstructured data transformed into a format that can be read by a computer, which is then analyzed to generate an appropriate response. Underlying ML algorithms improve response quality over time as it learns. These four NLP steps can be broken down further below:

Input generation: Users provide input through a website or an app; the format of the input can either be voice or text.
Input analysis: If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data.
Dialogue management: During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response.
Reinforcement learning: Finally, machine learning algorithms refine responses over time to ensure accuracy.

Conversational AI use cases

When people think of conversational artificial intelligence, online chatbots and voice assistants frequently come to mind for their customer support services and omni-channel deployment. Most conversational AI apps have extensive analytics built into the backend program, helping ensure human-like conversational experiences.

Experts consider conversational AI's current applications weak AI, as they are focused on performing a very narrow field of tasks. Strong AI, which is still a theoretical concept, focuses on a human-like consciousness that can solve various tasks and solve a broad range of problems.

Despite its narrow focus, conversation AI is an extremely lucrative technology for enterprises, helping businesses more profitable. While an AI chatbot is the most popular form of conversational AI, there are still many other use cases across the enterprise. Some examples include:

Online customer support: Online chatbots are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
Accessibility: Companies can become more accessible by reducing entry barriers, particularly for users who use assistive technologies. Commonly used features of Conversation AI for these groups are text-to-speech dictation and language translation.
HR processes: Many human resources processes can be optimized by using conversational AI, such as employee training, onboarding processes, and updating employee information.
Health care: Conversational AI can make health care services more accessible and affordable for patients, while also improving operational efficiency and the administrative process, such as claim processing, more streamlined.
Internet of things (IoT) devices: Most households now have at least IoT device, from Alexa speakers to smart watches to their cell phones. These devices use automated speech recognition to interact with end users. Popular applications include Amazon Alexa, Apple Siri and Google Home.
Computer software: Many tasks in an office environment are simplified by conversational AI, such as search autocomplete when you search something on Google and spell check.

While most AI chatbots and apps currently have rudimentary problem-solving skills, they can reduce time and improve cost efficiency on repetitive customer support interactions, freeing up personnel resources to focus on more involved customer interactions. Overall, conversational AI apps have been able to replicate human conversational experiences well, leading to higher rates of customer satisfaction.

Benefits of conversational AI

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

Cost efficiency: Staffing a customer service department can be quite costly, especially as you seek to answer questions outside regular office hours. Providing customer assistance via conversational interfaces can reduce business costs around salaries and training, especially for small- or medium-sized companies. Chatbots and virtual assistants can respond instantly, providing 24-hour availability to potential customers.
Human conversations can also result in inconsistent responses to potential customers. Since most interactions with support are information-seeking and repetitive, businesses can program conversational AI to handle various use cases, ensuring comprehensiveness and consistency. This creates continuity within the customer experience, and it allows valuable human resources to be available for more complex queries.
Increased sales and customer engagement: With the adoption of mobile devices into consumers daily lives, businesses need to be prepared to provide real-time information to their end users. Since conversational AI tools can be accessed more readily than human workforces, customers can engage more quickly and frequently with brands. This immediate support allows customers to avoid long call center wait times, leading to improvements in the overall customer experience. As customer satisfaction grows, companies will see its impact reflected in increased customer loyalty and additional revenue from referrals.
Personalization features within conversational AI also provide chatbots with the ability to provide recommendations to end users, allowing businesses to cross-sell products that customers may not have initially considered.
Scalability: Conversational AI is also very scalable as adding infrastructure to support conversational AI is cheaper and faster than the hiring and on-boarding process for new employees. This is especially helpful when products expand to new geographical markets or during unexpected short-term spikes in demand, such as during holiday seasons.

Challenges of conversational AI technologies

Conversational AI is still in its infancy, and widespread business adoption began in recent years. As with any new technological advances, there are some challenges with transitioning to conversational AI applications. Some examples include:

Language input: Language input can be a pain point for conversational AI, whether the input is text or voice. Dialects, accents, and background noises can impact the AI’s understanding of the raw input. Slang and unscripted language can also generate problems with processing the input.
However, the biggest challenge for conversational AI is the human factor in language input. Emotions, tone, and sarcasm make it difficult for conversational AI to interpret the intended user meaning and respond appropriately.
Privacy and security: Since Conversational AI is dependent on collecting data to answer user queries, it is also vulnerable to privacy and security breaches. Developing conversational AI apps with high privacy and security standards and monitoring systems will help to build trust among end users, ultimately increasing chatbot usage over time.
User apprehension: Users can be apprehensive about sharing personal or sensitive information, especially when they realize that they are conversing with a machine instead of a human. Since all of your customers will not be early adopters, it will be important to educate and socialize your target audiences around the benefits and safety of these technologies to create better customer experiences. This can lead to bad user experience and reduced performance of the AI and negate the positive effects.
Additionally, sometimes chatbots are not programmed to answer the broad range of user inquiries. When that happens, it’ll be important to provide an alternative channel of communication to tackle these more complex queries, as it’ll be frustrating for the end user if a wrong or incomplete answer is provided. In these cases, customers should be given the opportunity to connect with a human representative of the company.
Finally, conversational AI can also optimize the workflow in a company, leading to a reduction in the workforce for a particular job function. This can trigger socio-economic activism, which can result in a negative backlash to a company.

Best chatbot examples are:

Here is a list of the top conversational AI examples: