Conversational AI revolutionizes how businesses interact with customers, manage operations, and make data-driven decisions. No longer confined to basic functions, today's conversational AI systems leverage advanced technologies like Natural Language Processing (NLP) and Machine Learning to deliver increasingly sophisticated services across many industries. According to the Deloitte report, the global conversational AI market is expected to grow at a CAGR of 22% during 2020-2025, reaching almost US$14B by 2025.
From virtual health assistants in healthcare to predictive maintenance in manufacturing, these intelligent systems are redefining what is possible, allowing companies to focus on creating lasting business value. After we’ve covered such topics as generative AI for enterprises and generative AI use cases, it’s time to review the compelling conversational AI use cases across various sectors. Let’s unpack their key functionalities, required technologies, benefits, and challenges. Use this guide as a baseline for evaluating conversational AI's transformative potential across various industries.
How does conversational AI work: Industry examples
Diving into conversational AI use cases can seem complex, but a profound grasp is crucial for maximizing its benefits. Using various industry examples, we'll uncover its capacities, from data collection to refining it through user feedback.
1. Data collection
Data collection refers to the process of gathering user inputs during an interaction. The AI captures this data through various means, such as typed text or spoken words. Once gathered, this data is securely stored in backend databases, where it is queued up for analysis. This historical data helps improve the AI's understanding of user intent, preferences, and behaviors over time.
Example: A retail chatbot collects data when users inquire about the availability of specific item variations in stock.
The input is divided into smaller units, or tokens, which can be as small as characters or as long as words. These tokens can be as small as individual characters or as long as complete words. The tokens are then analyzed to understand their grammatical roles and relationships, enabling the AI model to comprehend the sentence structure and meaning.
Example: In a query like "What's my account balance?" tokens include "What's," "my," "account," "balance," and "account balance" is the subject.
3. Understanding context
The applications of conversational AI go beyond simple tokenization and grammatical analysis to infer the user's intended action or query. This can include recognizing requests for information, making purchases, or any other user objectives. Moreover, conversational AI applications can identify crucial details within the query, such as product names, dates, or geographical locations, referred to as entities.
Example: For a query like "Can I get same-day delivery?", the intent is to inquire about delivery options, and the entity is "same-day."
4. Machine Learning algorithms
Machine Learning comes into play when the AI system uses algorithms trained on existing data sets to understand and predict user behavior. These algorithms identify patterns and preferences, allowing the model to adapt and respond more accurately and efficiently to user queries.
Example: Algorithms can predict the most likely reason a customer contacts support, such as a lost card or a fraudulent transaction.
5. Response generation
The conversational AI is designed to generate responses that suit the query's complexity. For simple questions, it might use predetermined, templated responses. However, conversational AI employs Natural Language Generation (NLG) techniques for more intricate queries to dynamically create unique and contextually appropriate replies.
Example: For supply chain management, templated responses for common questions like shipping fees and dynamic responses for supply chain disruptions.
6. User verification (if required)
When the user wants to access sensitive information or perform secure operations like payments, your conversational AI application may require additional steps to verify the user's identity. This could range from simple PINs to more robust methods like two-factor authentication.
Example: In education, the conversational AI application might require a school ID to access grade-specific information.
7. Integration and delivery
The AI system typically doesn't operate in isolation. It is integrated with existing platforms through APIs (Application Programming Interfaces), connecting to databases, CRM software, inventory management systems, and more. Once implemented, it can pull real-time data to provide more accurate and timely responses. The replies are then delivered through the most appropriate user interface, such as a chat window, a voice assistant, or a mobile app.
Example: In the healthcare industry, a chatbot integrated with Electronic Health Records can update patient files in real time.
8. Feedback loop
Post-interaction, users often have the option to provide feedback about their experience. This feedback is valuable as it helps in tweaking the system for improved performance. Based on user ratings or comments, adjustments can be made to the algorithms, enhancing the system's accuracy and reliability for future interactions.
Example: In retail, after the purchase, customers may be asked to rate the effectiveness of the chatbot's product recommendations, aiding in system improvement.
Conversational AI use cases blend various tech tools and methods to create a smooth experience for the end user. By understanding how data gathering works hand-in-hand with Machine Learning, you'll be well-equipped to tap into the game-changing possibilities of this technology.
Five major conversational AI use cases
Now, we'll examine five essential methods conversational AI reshapes in retail and healthcare. We'll discuss its inner workings, benefits, and challenges to reveal why it's a cornerstone of efficient business.
Conversational AI is making significant strides in retail, transforming how businesses interact with customers and manage operations. Here’s how:
- Customer service automation. Conversational AI chatbots are increasingly deployed to handle routine customer queries, freeing support teams to tackle more complex issues. Chatbots can answer various customer queries, from product availability to store hours, reducing the need for human intervention for basic questions. For more complicated issues that require human expertise, the chatbot can seamlessly transfer the customer to a human agent, ensuring that the case is resolved effectively.
- Sales and product recommendations. AI-powered chatbots can analyze customer behavior and preferences to offer personalized product recommendations. The AI system can track customer interactions and purchases to understand their preferences and needs. Based on this analysis, the chatbot can suggest products the customer is likely interested in, increasing the chances of a sale.
- 24/7 customer support. One of the most compelling advantages of conversational AI in retail is providing uninterrupted, 24/7 customer support. Customers can get their queries answered at any time of the day or night, improving customer satisfaction and loyalty. Many chatbots offer multi-language support, making it easier for global customers to get help.
Use cases for conversational AI are increasingly impacting the healthcare industry by assisting in diagnosis, managing patient care, and analyzing medical data. Let’s explore these cases in more detail:
- Diagnostics support. Conversational AI is increasingly being used to assist doctors in diagnosing various conditions. AI algorithms analyze medical images, lab data, and electronic health records to provide diagnostic suggestions. For example, AI is helping nephrologists use images from radiology or histopathology and images taken by smartphones to diagnose a patient's condition.
- Symptoms checking and triage. Conversational AI is expected to see expanded use in symptom checking and triage in the coming years. AI algorithms can help healthcare providers differentiate between patients requiring immediate emergency care and those a primary care physician can manage. This streamlines the healthcare process and ensures that resources are allocated more efficiently.
- Clinical trial matching. NLP can be utilized to review large volumes of unstructured clinical record data to match patients with appropriate clinical cancer studies. For instance, NLP helped physicians check about 10,000 medical charts per hour to find patients with the proper inclusion criteria for clinical trials.
With the backing of healthcare executives and promising use cases, conversational AI is poised to play a transformative role in healthcare delivery.
3. Finance and banking
From automating customer service to providing real-time financial advice, AI-driven chatbots are becoming indispensable tools. Let’s review how conversational AI use cases revolutionize the finance and banking industries:
- Customer service automation. Voice-based virtual assistants are taking customer service automation to the next level in finance and banking. Unlike text-based chatbots, these voice-activated systems allow customers to engage in more natural, conversational interactions. Customers can ask about their account balance upcoming bills, or even set up new accounts through voice commands, making the service more accessible and user-friendly.
- Fraud detection and security. Conversational AI can play a crucial role in enhancing the security measures of financial institutions. Advanced AI algorithms can analyze user behavior and real-time transaction patterns to detect fraudulent activities. If suspicious behavior is detected, the AI system can immediately alert the customer and temporarily lock the account until the issue is resolved, thereby adding an extra layer of security.
- Market analysis and investment suggestions. In its broader definition, AI can provide real-time market analysis and investment suggestions for customers interested in investments. The AI algorithms can scan vast amounts of financial news, trends, and data to offer investment options that align with the customer's risk profile and financial goals. This democratizes access to financial advice and enables more informed investment decisions.
Conversational AI has the potential to become a game-changer in the finance and banking sector, offering various applications that benefit institutions and their customers.
Conversational AI shines bright when it comes to education. It offers a myriad of applications that can enhance both teaching and learning experiences, such as:
- Administrative automation. Conversational AI can handle administrative tasks like enrollment procedures, fee payments, and course registrations. For instance, a student can interact with a chatbot to find out the available courses for the upcoming semester, register for them, and even complete the payment process. This streamlines administrative functions and allows educational institutions to allocate human resources to more critical tasks.
- Personalized learning assistance. Voice-activated virtual assistants can offer personalized learning experiences by adapting to each student's learning style and pace. For example, if a student struggles with a particular math concept, the assistant can provide additional exercises and explanations to help the student better grasp the concept. This level of personalization can significantly enhance the learning experience.
- Language learning and practice. Conversational AI is highly effective in language learning scenarios, offering interactive practice sessions that mimic real conversations. Students can engage in dialogues with the AI system to practice pronunciation, vocabulary, and sentence structure. Additionally, many AI-powered frameworks come with progress-tracking features, enabling students to see how much they've improved over time, which can be highly motivating.
By offering these diverse and interactive features, conversational AI provides a comprehensive and engaging approach to education, making it an invaluable tool in modern education.
5. Supply chain management
Conversational AI is vital in supply chain management, offering innovative solutions that enhance operational efficiency and decision-making. From automating inventory queries to providing real-time shipment tracking, AI-driven voice-activated virtual assistants are becoming indispensable tools in the supply chain ecosystem. Some of the most prominent use cases for conversational AI in this field are:
- Inventory management and queries. With the help of conversational AI solutions, you can handle various inventory-related tasks, such as checking stock levels, updating inventory databases, and predicting future inventory needs based on historical data. The conversational AI system can also automate the updating of the inventory database. Whenever a new shipment arrives, or products are sold, the chatbot can update the records accordingly, reducing the risk of human error.
- Shipment tracking and updates. On top of that, this technology AI can provide real-time tracking information for shipments, offering updates on location, estimated arrival time, and any possible delays. The AI system can proactively send alerts if it detects any delays in the shipment, allowing for timely interventions and adjustments in the supply chain.
- Crisis management and contingency planning. Conversational AI can assist in developing and executing crisis management plans, providing real-time updates and alternative solutions during disruptions. The AI system can monitor market fluctuations that could disrupt the supply chain and alert the concerned parties. Based on the nature of the crisis, AI can suggest alternative suppliers, transportation routes, or even temporary storage solutions, helping businesses minimize losses and maintain operations.
On a broader scale of supply chain management, conversational AI is a technological add-on and a fundamental shift in supply chain management, particularly in real-time inventory control, supplier communication, and crisis management.
In an age where efficiency, customer engagement, and data-driven decision-making are paramount, conversational AI is a transformative force across multiple industries. From healthcare and retail to finance and supply chain management, we've reviewed how these intelligent systems simplify processes, deliver invaluable insights, optimize resource allocation, and enhance user experience.
The use cases explored here show the technology's vast potential. Ready to elevate your customer experience with cutting-edge conversational AI? Let N-iX's two decades of tech expertise guide you through seamless integration and optimization. Contact us today to discover how we can transform your business operations and drive revenue growth.