Alex RaineyAlex is an experienced CTO and founder who largely focuses on all the technical areas of My AskAI, from AI Engineering, Technical Product Management and overall Platform Development.
Table of Contents
- Definition of Conversational AI
- How Conversational AI Works
- Traditional vs. Conversational AI
- Examples of Conversational AI in Different Industries
- Customer Support
- Healthcare
- E-commerce
- Banking
- Marketing
- Benefits of Using Conversational AI
- Challenges of Implementing Conversational AI
- Language barriers
- Privacy concerns
- Future Trends and Innovations in Conversational AI
- Conclusion
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Conversational AI is becoming the trend not only in the customer support industry but also in banking, healthcare, e-commerce, and marketing. It’s time for you to jump on the bandwagon.
Read on to learn everything you need to know about conversational AI, how you can use it depending on the industry, and how it could benefit you and your business.
Definition of Conversational AI
Conversational AI is a specific type of artificial intelligence designed to simulate human-like conversations. Unlike the traditional AI tools you see daily, it aims to understand and respond to natural language prompts as if you’re talking to a human.
How Conversational AI Works
Upon giving a prompt to a conversational AI system, it uses two core pieces of technology to process your input. These include natural language processing (NLP) and machine learning (ML). Here’s a brief breakdown of the two:
- Natural language processing: It helps computers understand the nuances of human language, including grammar, context, and intent. NLPs go beyond the literal meaning of words and can even process slang.
- Machine learning: Using ML, conversational AI trains on large datasets. This allows it to find and learn patterns in human conversation. As such, it can respond in a way that mimics a human. ML is also the reason conversational AI systems improve over time.
Traditional vs. Conversational AI
Traditional AI uses predetermined algorithms and rules to perform preset tasks. It’s used for predictive tasks like predicting stock prices or identifying spam emails. Since traditional AI is predominantly rule-based, people sometimes call it weak AI or narrow AI.
Recommendation engines are one particular example of traditional AI. For instance, Netflix may suggest some shows they predicted you’d like based on your watch history.
That’s what makes conversational AI different. Using NLP and machine learning models, it can accommodate more situations that don’t fit into a single flow chart. It is much more dynamic and designed for human interaction.
Examples of Conversational AI in Different Industries
Most conversational AI systems out on the market exist as chatbots, especially on the web. However, they can also exist as virtual voice assistants, generative AI agents, and app integrations in marketing tools.
Despite the similarities in format and modality, their usage varies widely depending on the industry. Here’s how conversational AI is used across different fields.
Customer Support
Upon visiting a website, you’ll typically see a bubble chat head somewhere on its landing page. This could be an AI-powered chatbot that handles customer requests, questions, and even complaints. Here are more specific use cases of conversational AI in customer support:
- Answering FAQs: Providing quick answers to common questions about products, services, orders, and policies.
- Troubleshooting issues: Guiding customers through basic troubleshooting steps or offering solutions for minor problems.
- Scheduling appointments: Booking appointments or reservations for customers without requiring human intervention.
- Collecting information: Gathering details about a customer's issue before routing them to the appropriate human agent.
- Providing live chat support: Offering an alternative to phone support for customers who prefer text-based communication.
There are many conversational AI chatbot makers that you can find online. One of the best platforms is My AskAI, which offers good chatbot templates and affordable subscription costs.
Healthcare
Mental health awareness has become more relevant in recent years, and conversational AI has found good use in it. One particular example of this is Woebot, an AI-powered chatbot that provides confidential self-help support.
Woebot offers evidence-based interventions for various cases, including anxiety in adults, emotional support for mothers, and substance abuse. While it cannot completely replace the expertise of human doctors, it provides extra help for a person’s mental well-being.
Other uses of conversational AI in healthcare include:
- Appointment scheduling and reminders
- Collection of patient feedback about their healthcare experience
- Answering basic questions about symptoms, treatments, and medications for common diseases and disorders
E-commerce
Amazon’s Alexa is perhaps the most prominent example of conversational AI in e-commerce. The product itself is a virtual assistant that can help you do basic tasks like setting an alarm, controlling the TV’s volume, and other day-to-day chores.
Those things aside, you can actually use Alexa to place an order on Amazon. For example,
- Say, “Order [item]” to add an item to your cart.
- Say, “Alexa, buy it now.”
- Say, “Check out my Amazon cart,” and follow the prompts.
Of course, conversational AI can also offer round-the-clock customer support, including product returns, recommendations, and more related to online shopping.
Banking
Banks commonly use conversational AI as part of their customer support. To be more specific, they use it for:
- Answering FAQs and providing account information: If consented, AI-powered chatbots can answer common questions about account balances, transaction history, loan details, and more, 24/7.
- Resolving basic issues: Chatbots can assist with tasks like resetting passwords, reporting lost cards, scheduling appointments, and freeing up human agents for complex matters.
A great example of this is Erica® by the Bank of America. It’s a virtual financial assistant that:
- Alerts you when refunds get posted to your bank account
- Rewards tracking and redemption
- Monitors your charges, including increases and duplicate payments
- Offers live chat support from a Bank of America specialist
Except for customer support, AI might not be that common in local banking systems, especially in areas that access the user’s financial information. Still, improvements in terms of security and privacy are being developed.
Marketing
Conversational AI found good use in the marketing industry in terms of lead generation. In particular, it helps in customer personalization. AI tools can analyze data on a lead's interests, preferences, and behavior to tailor messages to their needs.
AI-powered chatbots can also help determine good leads. For example, they can ask predefined questions and provide sales teams with the data they need for further lead engagement decisions.
Aside from lead generation, a related application of conversational AI is the creation of marketing material. A great example is HubSpot AI, which can help you generate content, create personalized marketing emails, and more.
Benefits of Using Conversational AI
The benefits vary depending on how well you can leverage conversational AI in your systems. But here are tangible advantages to using it in your business:
- Customer engagement: Conversational AI can help businesses interact with customers in real time and understand their target audience. The promptness and 24/7 availability of online chatbots can also improve customer satisfaction.
- Cost savings: AI can automate tasks that would otherwise require human interaction. Instead of hiring new people for other tasks, AI can free up employees from those tedious to-dos and focus on areas that need more attention.
- Accuracy: It can handle more requests than humans, provide relevant and correct information faster, and improve accuracy and complexity over time.
- Better personalization: As mentioned before, conversational AI uses machine learning models. They’re capable of building clear user-profiles and offering products or services tailored to their needs.
- Increase agent efficiency: By automating repetitive tasks, such as data entry and follow-up tasks, sales teams can focus on more important, high-value activities like closing deals.
Challenges of Implementing Conversational AI
Just like other AI-powered systems, conversational AI isn’t perfect yet. Here are some common challenges against conversational AI:
Language barriers
Most conversational AI systems only accept English questions and requests. The datasets that they’re trained on are also predominantly English. As such, foreign speakers might have a hard time using conversational AI.
While NLPs can go beyond the literal meanings of words, they can still be stumped by slang, regional languages, colloquialisms, and jargon. This requires more extensive datasets to train on.
Privacy concerns
Conversational AI systems and chatbots should have security measures compliant with international standards like GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), and more.
Especially for interactions that involve personal and financial information, businesses need to invest in robust security and privacy protection measures to keep data conversational.
Future Trends and Innovations in Conversational AI
Many conversational AI tools take user input using a chat-like interface. But as virtual assistants like Siri and Google Assistant become better, speech-based input is also becoming a trend. More extensive language-learning models are also emerging, as evident by Microsoft’s action to replace Cortana with Copilot.
However, perhaps one of the biggest trends in conversational AI is its integration into the Metaverse. This allows Meta to make such a digital world more human-like using simulated natural conversations.
Facebook is also releasing the beta version of Meta AI, which is accessible via Messenger, WhatsApp, and Instagram. It will also be available in the Ray-Ban Meta smart glasses. Meta AI allows you to create images based on your prompts, access real-time information, and more applications.
Conclusion
Conversational AI isn’t just for customer support—it also has great applications in banking, healthcare, e-commerce, and marketing. It can make your business’s processes much more efficient and help engage your customers better. If you’re looking to make your own conversational AI chatbot, start with My AskAI today.
Written by
Alex Rainey
Alex is an experienced CTO and founder who largely focuses on all the technical areas of My AskAI, from AI Engineering, Technical Product Management and overall Platform Development.
