Originally posted on https://dasha.ai/en-us/blog/conversational-ai-use-cases
Conversational AI is a set of technologies that let a machine understand what is being said, evaluate what would be a contextually relevant response, and reply in a human-like manner.
To recognize human speech and be able to talk like a human a set of machine learning (ML), natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT), and named entity recognition (NER) tools are used.
The conversational AI global market size is expected to grow from $4.8b (2020) up to $13.9b by 2025. Companies across various industries are quickly adopting conversational AI for business-specific purposes.
Let’s explore what industries use conversational AI and what real-world problems they solve with it.
Chatbot vs conversational AI
Chatbots are based on strictly scripted, limited conversations that use specific keywords. Chatbots are useful when it comes to simple interactions. Conversational AI on the other hand understands the subtleties of human words, intonation, and tone. By using speech recognition, it understands the spoken language of your customers. Natural language processing (NLP) helps it understand and analyze human language, be it written or spoken. Natural language understanding (NLU) helps it understand what the customers need to be done (intent classification.
Arthur wrote a fantastic article where he goes deep into differentiating chatbots and conversational AI. A highly recommended read!
Benefits of conversational AI
Here are some of the benefits companies get by employing conversational AI:
Increased productivity and effectiveness
Conversational AI automates repetitive processes, letting employees respond to more complex calls that require human creativeness.
The AI is available to take and make calls 24/7, has no sick days, is able to handle requests and answer questions no worse than a human can.
In a call center environment, for instance, the AI would let the employees advance to sales positions and qualify only the high-quality leads. Conversational AI would do all the preliminary work and qualify all the incoming leads.
Lower operating costs
Businesses spend over $1 trillion on customer service calls and conversational AI lowers that cost drastically
Conversational AI is able to handle hundreds of calls simultaneously.
Businesses save money on employee’s salaries and training. The AI reduces the number of customer support employees needed while reducing average wait time.
Increased customer satisfaction and experience
Higher engagement: customers will no longer need to wait for hours for an agent to free up or wait for working hours to start. They can get their issues solved anytime, anywhere.
Conversational AI can augment live agents’ work. The AI can provide all the information gained during the conversation, making the agents solve customer’s issues faster once the call is transferred to them.
Customer satisfaction can be maximized by the AI analyzing what happened in the past conversations. It can predict how the customer would react and what they would say based on the existing data.
Increased employee satisfaction
Conversational AI helps to reduce employee churn that occurs due to the repetitive nature of the calls. The employees can spend more time handling complex calls. It would give room to increase both hard and soft skills, and build stronger relationships with customers.
Customer support employees handle around 50 calls per day, which is extremely stressful to some. Conversational AI alleviates this problem.
Let’s now take a look at different industries that use conversational AI to achieve business-specific goals.
What industries use conversational voice AI and what use cases do they have?
Conversational AI use cases for the automotive industry
There are two primary ways car companies use conversational AI: vehicle discovery and maintenance and in-car voice assistants. The latter one is getting popular fast. In the United States, 60% of people are inclined to buy a car with an assistant installed. Nearly 130 million people have already tried using one in their car and over 80 million enjoyed using their in-car voice assistant so much that they’re using it constantly.
Let’s take a look at the two use cases in more detail:
Vehicle discovery and maintenance
Conversational AI makes it easy for customers to discover new cars, book test drives, compare different models of cars, and find a car that matches their requirements. Customers can also have the ease of making inquiries about car loans and payment options.
Automotive industry companies also use conversational AI to automate car check and repair appointments.
Connected vehicles
Nowadays, cars are smarter than ever. Car manufacturers incorporate conversational AI into cars so that drivers can have a safer and more efficient driving experience. For example, in-car voice assistants can make calls, control smart home devices, shop for products, and book a table at a restaurant.
It doesn’t stop there, though. In-car voice assistants can control virtually all car functions. Consumers want their in-car assistants to have more than just basic functions. Customers are expecting their assistants to support various areas of life and help control in-car conditions. To name a few, the control conditions within the car include opening and closing of the windows or the trunk, locking/unlocking the doors, help with parking, AC control, and managing car settings in response to time of the day and weather conditions.
We have created in-car voice assistant app as a proof of concept. .
Conversational AI for banking industry
Conversational AI is transforming the way banking industry companies retain their customers and gain new ones. It is expected that 90% of communication between the banks and their customers will happen through a bot. It’s also expected by 2023 that banks will save $7.3B
of the operational by cost bots. These contribute to the wide adoption of AI in the banking sector.
Here are some of the possible ways banks use AI:
FAQ
Asking a frequently asked question doesn’t require the customer to provide any additional information to prove their identity. This makes this use case one of the simplest ones. Since banks don’t operate on a 24/7 basis and neither do their customer support lines (for the most part), having conversational AI that solves this is a valuable addition.
Loan application automation
There’s no more need for a customer to physically be at the bank or wait on the phone for a representative to free up to create a loan application. When integrated with other sources, AI can analyze documents, make notes, and perform data verification. This makes the job of the bank representative easy when it comes to the loan approval process.
Scheduling an appointment with a bank representative
It might be tough to get a hold of a bank representative once a customer is at the bank. Scheduling an appointment in advance saves customers’ time and lets them create plans beforehand. Appointment scheduling can be easily automated via a conversational AI app.
Payment processing
Customers might need to transfer money from one bank to another or from one account to another at any moment of time. Accounting for such a use case, Dasha AI created a simple app banks can use to automate payment processing.
Feedback surveys
Phone feedback surveys used to be expensive, however, with the rise of conversational AI, that’s no longer the case. Phone surveys are proven to be the best channel to get feedback from customers - they have an over 90% response rate on picked up lines. Here’s a simple app that illustrates how to create customer feedback conversational AI app .
Conversational AI for healthcare industry
The Healthcare Global Forecast projected that by 2026, the healthcare industry will experience an immense boost in revenue and see improvements in patient health management due to AI adoption. Healthcare companies use AI in myriad different ways: drug discovery, patient care, illness diagnostics, etc. Conversational AI is also on the list:
Appointment scheduling
Nurses and administrators are already busy enough tending to the patients, so it’s necessary to free them from easily automatable calls. With Dasha AI it’s easy to make a conversational AI app that lets patients search for doctors and arrange appointments all within one conversational interface. An appointment scheduling app can also ask screening questions since it would provide the doctor with background information prior to the appointment.
Mental health management
With machine learning techniques, a mental health management conversational AI app can analyze the tone and the speed of the patient’s voice and provide recommendations based on that. It also could be programmed to analyze patterns of a patient’s mood, activities, and overall mental state over time and, if necessary, make appointments with healthcare professionals. For instance, if it recognizes either by speech tone or words that the patient is stressed out, it could suggest a pre-programmed range of meditations or provide a range of relaxation techniques. Such conversational AI apps could be integrated with various sources of health trackers (heart rate, sleep, diet, blood pressure, sugar level) and make recommendations according to those indicators.
Yet when creating a mental health conversational AI app you should exercise caution. For instance, it’s important not to use such AI to diagnose or treat suicidal patients, as a human with medical education is the right fit.
Such a mental health management AI app can be easily created with Dasha conversational AI API and integrated with various services and health trackers to provide further benefits for the patients.
Health plan research and management
Insurance companies employ conversational AI to generate customers out of leads by walking them through existing insurance policies and helping them pick the one that suits their needs. The AI can assess risks based on the customer’s historical data and provide them the best insurance recommendations. Here’s an example of a conversational AI app that lets you qualify leads better and faster.
Symptom checkers
This use case encompasses more than simple symptom checking.
Conversational AI can call patients who are in rehabilitation after surgery to check on their health, ask about their symptoms and suggest some pain-alleviating strategies if needed. Patients can also make a call to ask a pressing health-related question, be it about medication or illness, and be provided with a preliminary diagnosis and referred to a specialist. If conversational AI deems that the symptoms are serious, it can either call emergency services on behalf of the patient or make a strong recommendation to call 911.
Companies outside of the healthcare industry can make use of conversational AI symptom checkers. For example, a company might want to check their employee’s health status prior to their shift. We’ve created a conversational AI symptom checker app that assesses whether a shift worker has any Covid-19 symptoms. In case they do, the AI suggests they stay at home, otherwise, it lets them know they’re expected to come to work.
If you’re interested in other ways the healthcare industry uses AI, check out this post
Conversational AI for hospitality industry
Restaurants, hotels, amusement parks, cruises, entertainment, and tour agencies can all use conversational AI. Customer support is the one that can be automated regardless of what the business sector is.
Automated hotel operations
Hotels, for instance, can automate the room booking process, 24/7 access to information about the hotel, its services, hotel vicinity questions, or other traveling information. When hotels employ conversational AI they can expect rapid growth of their business. The AI helps to increase customer loyalty, as a happy traveling experience starts with having a great place to stay.
Automated restaurant operation
Restaurants can have table booking calls automated, making it possible for hosts to focus on welcoming customers. Another possibility is automating food ordering and delivery. We’ve created a simple food ordering app that helps pizza restaurants take orders in a seamless way, you can check the use case here.
Conversational AI for logistics industry
To increase customer satisfaction, logistics companies automate the way customers get package delivery updates. One of the best ways to do that is through conversational AI.
Delivery status, tracking, and rescheduling
Over 530 million packages are delivered per day by UPS, USPS, and FedEx alone. In order to manage such volume without the risk of losing customers due to overwhelmed customer support, logistics companies use automation.
The conversational AI can pull the package information from the database once the customer provides a tracking number. From there it can solve virtually any issue customers can have: rescheduling the delivery date and/or time, checking the status of the delivery, check for other deliveries, etc.
Check out this conversational AI app logistics companies can use to automate package tracking.
Conversational AI automation is easy - and mega efficient
While we took a closer look at only a few industries, there are many other ones that find value in using conversational AI. While the use cases are different for all, one remains constant: customer support automation.
Happy customers mean loyal customers that are ready to support your business financially for a long period of time. That’s why companies are obsessed with having superior customer support. Employing conversational AIl makes companies even more customer-centric. Most of the customers’ inquiries can be solved by AI in a prompt and personalized manner, while more complex ones can be handled by human employees.
Dasha AI makes automating virtually any process with conversational AI easy, so why don’t you try creating an app yourself? You can start with this tutorial. Be sure to join our developer community first.
Top comments (0)