Have you ever heard of automation? In order to assure impenetrable front-end functionality, firms have used automation to close process gaps. When talking about frontline automation, conversational AI should be brought up.
Conversational AI has gained a lot of traction over the past few years, and because of how widely it is used, it has become a must-have tool for many. Despite the fact that conversational AI has been present for a while, its use was completely absent from 2005 to 2017, rapidly expanding after that.
One benefit of conversational AI is that it has increased the resiliency of digital businesses and improves their ability to engage or communicate with customers, allowing them to receive wonderful customer service at multiple points.
We will first study how conversational AI functions and how it differs from conventional chatbots before discussing the future of conversational AI in various customer-facing areas. Why is it advantageous? So let’s get going.
What exactly is conversational AI?
Conversational AI is a technology that helps to create human-like interactions between a human and an agent. Customers can interact through text or speech, but this methodology facilitates clear communication.
Conversational AI is what backs virtual assistants, voice assistants, and chatbots. Conversational AI can recognize speech or text patterns in such a way that they deliver the best response to the user. This is possible with machine learning, natural language processing (NLP), natural language understanding (NLU), and speech recognition (ASR).
These tools are often used in customer facing segments like sales, and they help in streamlining answers to common or complex queries to provide a superior customer experience.
Known Examples of Conversational AI are:
- Siri, Alexa, Google Assistant, and Cortana are examples of virtual assistants
- FAQ bots to provide prompt responses to frequently asked inquiries
- Automated virtual assistants when calling customer service
- Chatbots that are responsive for live chats when you visit online retailers or websites
What steps has Google taken for conversational AI?
Google has made enormous investments in conversational AI over the past ten years, both inside the Google cloud and outside of it. As a result, businesses can now take advantage of this fundamental conversational AI technology and offer their customers solutions that can address real-world problems and use cases.
So, whether it’s highly accurate speech to text in a number of settings and ways, high quality text to speech voices with customizations, or other AI elements available in dialogue flow in document base support or sentiment analysis, all these areas benefit from the research and innovation that Google as a whole is doing. With Google AI being used in the process, most businesses are improving.
Why Conversational AI?
- No need for training and maintaining business continuity: Enterprises make use of conversational AI so they can quickly deliver customers what they are asking for and maintain the continuity of business. Since the interactions are based on natural language, training is not required.
- Reduces cost and operates constantly: Contrary to humans, conversational AI is available around-the-clock, allowing for more customer support. The conversational AI can handle the majority of repetitive or low-assistance tasks, which lowers costs and improves customer experience.
- Quick customer assistance increases customer retention. When conversational AI is available across a variety of channels, including phones, IVR, tablets, smart speakers, smart watches, etc., consumers may receive quick responses whenever and wherever they need it.
- Offers a unified customer experience: Conversational AI enables customers to have a unified experience despite the fact that most organisations’ backend data is dispersed across numerous lines of business.
- Simple and systematic: It streamlines and organises processes. Since conversational AI is adaptable to organisational changes, switching to alternative technologies won’t have a negative impact on customers. It also gets rid of the inflexible synchronous communication format. It is more efficient to use asynchronous formats like text-based or conversational AI than IVR or slow form-based formats.
Is a conversational AI chatbot superior to standard chatbots or distinct from them?
The advantages of AI chatbots over conventional ones include enabling clients to interact with gadgets, programs, and websites in their own language, which may be effectively assessed by the sales representative to provide a suitable response.
Traditional chatbots can’t always increase customer satisfaction because the technology isn’t fully updated to provide the same information that customers are asking for. Due to the availability of scripted programs for common operations, it is suitable for straightforward tasks. Traditional chatbots can only respond to specific keywords because they must write, making it difficult for them to answer questions that are not commonly asked.
AI chatbots that can carry on a conversation do not require a script and can develop through reinforcement learning. Because they are simple to scale and have a thorough grasp of the consumer, they work best for complicated use cases.
The following list includes several popular conversational AI assistants used in many industries:
1. Virtual Assistant in Lufthansa:
Nelly, Maria, and Elisa are the virtual assistants for the Lufthansa group. They communicate with consumers via chat when there is a flight cancellation, missed connection, etc., so they can find a better alternative.
Therefore, these AI-powered chatbots assist clients 24x7x365 days a year by responding to frequent questions. If there isn’t a best solution, the customer is instantly forwarded to the call center where the next available person takes over.
These virtual chat bots have been a huge benefit to Lufthansa since less time is spent on calls to answer ordinary questions, clients don’t have to wait in line for a simple question, and more calls are around significant questions that bots can’t solve.
2. Sephora’s fashion Bot
The first AI chatbot for the fashion sector was first introduced by Sephora. When a consumer enters Sephora online, the chatbot prompts a questionnaire that has been built to identify the customer and their product preferences so that customers can receive excellent customer service. The chatbots are Kik-based to assist customers when they visit the store online.
Customers can use this to digitally try on various products and book in-store services by submitting selfies. The AI-based chatbot has assisted Sephora in establishing its brand as a valuable partner in enhancing the consumer experience and empowering customers to make the best purchases.
3. Woebot’s mental health chatbot:
There were numerous people who sought access to mental health services, but there was no assurance that they would get the support they required.
By communicating with patients via a chatbot on demand, Woebot, a sophisticated artificial intelligence-based mental health service, removed all obstacles to offering mental health therapy to patients. The chatbot was created to provide cognitive behavioral treatment (CBT) on the patient’s terms.
The Woebot chatbot uses sophisticated AI and in-depth psychological understanding to evaluate the signs of sadness, anxiety, and other conditions in order to react with empathy. The AI chatbot Woebot allowed users to text chat with it, and its therapeutic tools were centered on cognitive behavioural therapy (CBT), dialectical behaviour therapy (DBT), and interpersonal therapy (IPT).
As a result, the bot could provide users with psychoeducation and assist them in correctly addressing their mental disorders. By using the bot, users got insightful information at a time of their choice, enabling them to tackle their problems more effectively.
What will conversational AI look like in the future for various customer-facing segments? It is obvious that best practices should be followed to enhance customer service and experience, as well as for a variety of other reasons. Conversational AI is expanding rapidly, and since it should be able to make wise decisions for the consumer throughout their trip, its implementation should be goal-oriented.
Natural language processing, or NLP, needs to be resilient because each customer frames their inquiries differently, else there will be a delay in fixing their issues. To handle conversational AI without the aid of developers, enterprises should employ low- or no-code approaches. Maintaining the client experience as the top priority means addressing actual pain points rather than attempting to force oneself into the mix. Finally, it should be advantageous to both the business and its clients.