CAGR 17.3% from 2023 to 2033! The sharp rise in the value of the AI chat market is driven by the need for a more personalised customer experience. Based on trends, the estimate could exceed US$47.6 billion by 2033. This is evident from the investment structure of industry organisations as a whole. Their strategic paths include many AI-powered conversational projects with AI-powered chatbots, voice assistants/virtual assistants, etc., to meet the demand.
Artificial intelligence technologies have become indispensable for enterprises to analyse large data sets and respond quickly. More and more companies are now realising this potential, and this will increase the number of use cases for conversational AI.
Conversational AI use cases in healthcare
Advancements in robotics and AI are reshaping healthcare. Conversational AI solutions have notably improved interactions among patients, doctors, and hospital staff. Its impact, especially during the pandemic, has led to a significant increase in adoption.
Here are some of the important conversational AI use cases in healthcare:-
Public health information dissemination
Rapidly spreading false or misleading information during public health emergencies can make people panic. We all experienced this when COVID-19 was at its peak. Healthcare organisations countered the issues by deploying AI Chatbots. WHO used an interactive chatbot to send accurate information about the virus to people in multiple languages directly on their mobile phones.
The bots have evolved since then. They can now help raise awareness across digital platforms, like social media, websites, messaging apps, etc. Such conversational AI tools disseminate correct and relevant information and answer all the questions people have. These AI chatbots are trained on real datasets to ensure that they pass relevant information to users.
But chatbots don’t come up with their answers; they scan through large amounts of data to find the correct response. For that, proper training and testing are important to ensure that responses from these AI bots are not biased.
To minimise bias in those replies, businesses must ensure that their bots have been trained on the right and comprehensive data.
Appointment scheduling
Scheduling appointments with doctors using traditional phone calls and emails is often time-consuming. With conversational AI, the process can be made simpler. AI-powered chatbots can act as a medium to check and verify availability. Based on that, patients can book, reschedule, or cancel appointments with the doctors anytime.
Doctors and hospital staff can confirm appointments using text messages or voice commands using chatbots. These chatbots with Natural Language Processing (NLP) are highly accurate, eliminating the risk of double bookings.
Healthcare staff can also use conversational AI apps for appointment reminders and sharing valuable information, such as required medical reports for assessments.
Patient support
Patients are not always tech-savvy. Many see digital platforms as a barrier to accessing their health data. They find processes like account creation or a new password setting cumbersome. Conversational AI can remove obstacles and promote ownership and trust in the healthcare system.
It can guide these people by breaking down the process per their requirements and helping them with their queries.
AI assistants can also help automate responses to certain FAQs and resolve specific queries. Cardinal Health, a global healthcare service company, has recently started using an AI-powered virtual Assistant, CHIA. This AI Assistant helps patients by answering queries about medical products and medicine shipments, improving their overall user experience.
Conversational AI use cases in fintech
In the fintech space, conversational AI has emerged as a significant game-changer. People generally perceive banking as a complicated process. But AI bots are now making it more engaging by enabling round-the-clock customer service and increasing the number of conversational AI use cases in fintech.
Fraud detection
Loss in the fintech industry due to fraud is huge. PYMNTS and Ingo Money conducted research to find that the average loss in the US alone is nearly $51 million per year. However, the recent developments in AI-powered fintech chatbots for real-time monitoring and multi-factor authentication to detect fraudulent activities show promising signs.
AI chatbots now use NLP to easily find irregularity in users’ chatting patterns. Once detected, they quickly block or flag such unusual transactions.
Personal banking assistants
Voice assistants and AI chatbots have become essential in handling and answering basic queries. Its credit goes to the Dutch bank ING for revolutionising the segment by launching Inge, the first voice assistant in banking, in 2014. Since then, the bank-customer dynamics have transformed. Customers now interact with similar chatbots in natural language, asking them questions like “What is the status of my loan application?”, “Can you show my past five transactions?” “How long does it take to process a home loan?” etc.
But these AI bots don’t just give answers to basic queries. They can handle more complex problems as well. They can offer customers personalised financial advice or investment decisions by processing data and analysing their accounting activities. Also, these tools can help set reminders for bill payments to avoid any overdue or additional charges.
Collect feedback
Industries are now more customer-driven. It forces businesses to value customer feedback for insights that can shape strategic decisions. They have started using a feedback bot to collect input to identify the user’s mood using NLP and respond to conversations accordingly.
Interaction with customer care, ATM issues, payment difficulties, etc., are some other modes customers can use to submit their feedback. It helps banks analyse and improve customer experience and understand areas for improvement.
Conversational AI Use Cases In Real Estate
When it comes to data about properties, communities, tenants, economic fluctuations, geopolitical issues, and the market, real estate investors have decades of experience. Modelling a conversational AI bot based on this information can be a good starting point. It can help real estate companies improve the productivity of their real estate agents, closing deals faster and, most importantly, increasing sales.
Exploring conversational AI use cases in AI can provide a more in-depth understanding of the scenario.
Lead Capturing And Management
Getting a lead is crucial for any business, but if it doesn’t convert, that lead becomes a zero lead. Conversational AI can be the first point of contact for collecting leads. By initiating a conversation, it can collect valuable information from the prospect. This information can be personal (name, budget, contact details, etc.) or about the prospect’s purpose (buying, selling, or renting).
With such information, companies can quickly create categories like warm, hot, promising, etc. They can use a database management system to store the collected data. Agents or retailers can then access this information to contact high-quality leads. These steps can streamline the entire process, making it more productive and less time-consuming.
Personalised Property Suggestions
Buying a home is a drawn-out task, dependent on the buyers’ tastes, which vary from one buyer to another. This variation creates the need for personalised solutions. Creating customised customer profiles based on their unique needs is crucial for such offers. Chatbot’s ability to collect information about preferences, preferred areas, property type, budget, etc., can be helpful for investors.
The chatbot can create individual profiles and offer personalised property suggestions with the information. If the visitor expresses interest in a property, the chatbot can use an advanced algorithm to scan the extensive listings, identifying those matching the customer’s criteria. It can then present a list of curated options, increasing the chances of successful conversion.
Scheduling Property Visits
Scheduling property visits is like test-driving a car before buying it. It allows buyers to feel the place’s vibes, explore every detail, and envision themselves living there. It’s the real-life experience that can make or break a deal.
Using conversational AI, real estate agents can automate the scheduling, facilitating buyers to book appointments at their convenience without the back-and-forth hassles. Chatbots can ask visitors about their preferred timeslot and date and check availability in the agent’s calendar to schedule a visit.
Once a suitable timeslot is found, chatbots can send reminders to both parties before the scheduled date. This automated process makes communication more efficient and helps save time for both parties.
Conversational AI Use Cases In Retail
Customers’ buying experiences and behaviour around how they should engage with businesses have changed over the past few years. These days, it makes more sense to bring a store to the customers where they are. Brands switching to conversational AI are reaping the benefits of more sales and a more extensive customer base.
Delving into conversational AI use cases in retail can clarify the dynamic shifts.
Improving in-store experience
With Amazon, Walmart, and others all spending big to make the in-store experience outstanding, one thing is clear: conversational AI is set to experience a seismic shift in its uptake. Its ability to make the in-store experience for shoppers more enjoyable, engaging, interactive, and efficient is the primary catalyst. By deploying AI chatbots in retail stores, retailers can offer customers more personalised assistance and real-time product recommendations.
Customers can ask an AI-powered bot or virtual assistant for details about specific items, availability, or assistance finding the right size when they visit the shop. Virtual assistants, on the other hand, will provide accurate information, helping shoppers save time and improve their overall experience.
Order Tracking
Customers prefer to receive their orders on time, and it is even better if they receive it early. That is why they monitor the level of demand from retailers. Voice assistants, messaging bots or AI chatbots, online stores, and e-commerce businesses can make this process efficient and convenient.
Conversational AI solutions use NLP and ML technologies to give customers real-time order updates. It can also improve a retailer’s efficiency by reducing the need for manual tracking and customer support.
Better Decision Making
Analysing user behaviour and other real-time data can help retailers make more informed decisions. Conversational AI that can understand natural language makes adopting AI chatbots a big step. They interact with customers, understand their intentions, and collect feedback and other important information for better decision-making.
AI chatbots can continuously learn and adapt to changing customer needs and preferences thanks to built-in machine learning algorithms. When used correctly, retailers can reach more customers by meeting their unique needs and staying ahead of the market.
Conversational AI Use Cases In Recruitment
Recruiting teams face intense pressure during talent shortages when they expect to find candidates quickly. Finding the best candidate can be a nightmare if the position is niche. Conversational AI can ease this pressure on HR professionals.
Here are some of the prominent conversational AI use cases in recruitment:
Candidate Screening
Sifting through hundreds of applications to find the perfect candidate can strain resources. An AI-powered chatbot has started streamlining the selection process by interacting with human candidates to obtain basic information.
To check the value of a candidate for a position, AI bots usually ask about their qualifications, interests, skills, etc. Based on the answers, they filter the candidate for the next step. This automated search helps save time for both parties and keeps candidates interested.
Scheduling Interviews
The most frustrating part of the recruiting process is scheduling interviews. Arranging interviews with multiple candidates and interviewers can sometimes be very tedious as it requires searching for interviewers and candidates to find a regular time to arrange an interview.
Conversational AI can speed up the hiring process by scheduling interviews based on mutual availability, eliminating the need for manual coordination. AI-powered chatbots can help streamline the recruiting process, helping hiring managers focus on other important tasks.
Chatbots can also send reminders to recruiters and candidates and reduce the likelihood of cancellations or no-shows.
Onboarding
When a candidate is hired, recruiting teams collect specific information such as salary, benefits, and other administrative functions. This system can be customised using conversational AI. This tool can make code entry much easier and faster and free up time for your team.
Conversational AI can also provide new employees with personalised skills by answering their questions and providing the information they seek. These bots can direct new hires to company pages and participate in training, ensuring candidates feel connected on their first day.
Benefits of Conversational AI
Conversational AI has become a certainty for businesses across industries. Smart implementation can help companies stay competitive and effectively meet customer needs. A starting point would be to identify the key benefits of conversational AI.
Boost Productivity And Save Cost
One of the primary profits of using conversational AI solutions is automation. Conversational AI, when integrated into any website or social media platform, can handle all laborious tasks, allowing employees to focus on complex tasks. This can boost staff productivity and efficiency, resulting in cost savings for the company.
Availability
AI-powered conversational solutions can work around the clock to provide customers with quick answers and assistance.
AI-powered chatbots can manage multiple interactions simultaneously, ensuring each customer receives the attention they deserve without sacrificing response time. This approach helps businesses expand and serve a growing customer base.
Scalability
As with business needs, conversational AI is easily scalable because it can serve multiple clients simultaneously. This is useful when a product/service enters new geographic markets or when web traffic increases during sales.
This scaling process helps the company provide superior customer service even during peak periods, increasing customer satisfaction and sales.
Personalisation
Conversational AI can analyse and provide personalised product recommendations based on a user’s preferences, browsing behaviour, and purchase history. Companies can use AI-powered chat solutions to improve cross-selling and expand sales opportunities by analysing customers’ unique requirements and preferences, increasing revenue.
Bottom Line
The conversation around artificial intelligence is changing the way industries operate, and its impact will only increase with the creation of artificial intelligence. Conversational AI and artificial intelligence can create conversations that feel human, personalised, and intelligent when used together.
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