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"Star Trek" fans are quite familiar with conversational AI. When Jean-Luc Picard, says “Computer!” and asks a question or requests a cup of “Earl Grey, hot!” and then listens to the answer or waits for the replicator to create his cuppa, he is giving a demo of it.
In a business environment, conversational AI can be as helpful to your team as it was (or will be in the fictional year 2364) to Picard’s crew on the Star Ship Enterprise. It can power everything from customer service to sales to help desks and more.
“Chatbots provide instant support, AI tools handle lead nurturing and voice assistants simplify day-to-day tasks,” explained V. Frank Sondors, the founder of Salesforge. “Conversational AI helps companies scale communication without losing responsiveness.”
If you are trying to better understand what conversational AI can do at your company, this guide will get you there, fast. We may not yet live on a spaceship staffed with AI, replicators and androids — yet — but we are well on our way.
Table of Contents
- What Is Conversational AI?
- How Does Conversational AI Work?
- 3 Examples of Conversational AI
- Real-World Applications of Conversational AI
- The Benefits of Conversational AI
- The Challenges of Conversational AI
- Top Conversational AI Products
What Is Conversational AI?
“Conversational AI is an artificial intelligence technology capable of simulating human dialogue and interacting with users through voice or text,” explained Alex Li, founder of StudyX.
It's a group of technologies that make it possible for machines to understand, process and respond to human language in a natural and intuitive manner. Without it, you would have to click on a folder, type on a keyboard or swipe a screen to move your files, get your answers or find the data you need. Conversational AI is not a single technology. It is a collection of systems — from simple chatbots to complex virtual assistants — that allow you to speak, listen and experience what feels like a conversation with a computer.
“At its core,” explained Peter Murphy Lewis, CEO at Strategic Pete, “it's about creating systems that interpret intent, recognize context and deliver relevant, adaptive responses that make interactions more intuitive and seamless.”
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How Does Conversational AI Work?
Conversational AI relies on a set of advanced tools to interpret and respond to human language. “It works by combining several technologies in a complex way, with the main ones being natural language processing (NLP), machine learning (ML) and advanced speech recognition algorithms,” explained Lewis. These technologies allow the AI tool to understand, process and respond to your input in a way that feels like a natural conversation.
Here is a breakdown of the tools conversational AI uses to accomplish this:
Natural Language Processing (NLP)
“NLP helps the system parse human input into tokens, syntax and semantics to arrive at meaning,” said Lewis. The goal of NLP is to bridge the gap between human communication and computer understanding and to enable machines to respond to human text or speech.
Accomplishing this involves several processes:
- Tokenization: Breaking down text into smaller units such as words or phrases.
- Part-of-Speech Tagging: Identifying the grammatical parts of speech in a sentence.
- Named Entity Recognition (NER): Identifying and categorizing entities such as names, dates and locations.
- Sentiment Analysis: Determining the sentiment or emotion expressed in a piece of text.
Machine Learning (ML)
“AI uses Machine Learning to improve over time,” said Phil Portman, founder and CEO of Textdrip. “For example, let’s say you frequently ask about the weather in different cities. The AI learns from your past queries and adapts to give you more personalized answers — perhaps suggesting things like, ‘I noticed you like sunny weather for your trips. Would you like to explore destinations with the best weather this time of year?’ By analyzing these patterns, the AI enhances its ability to predict what you might ask next and improve your overall experience.”
It uses several technologies to accomplish this:
- Supervised Learning: This is where models are trained on labeled data. The model is given input-output pairs. Its goal is to learn the mapping between the inputs and the corresponding outputs. These inputs might be spam/not spam, correctly identifying the cat in an image or something similar. The more it does this, the better it gets at it.
- Unsupervised Learning: The AI identifies patterns and correlations in unlabeled data. This means it has to figure out what the pattern is and identify it without being given the correct answer. This leads to more open-ended results. It might be asked, for example, to group customers into segments.
- Reinforcement Learning: Here the AI learns through trial and error. A classic example of this is the cart/pole problem. The AI is asked to balance a pole on a cart. It can move the cart left or right to keep the pole upright. It has to process the environment and gets a positive reward when the pole stays balanced or a negative one when it falls over.
Artificial Intelligence (AI)
“Finally, AI brings everything together by predicting and delivering the best possible response,” said Portman. “It taps into vast datasets, such as weather databases, to gather the latest info. The AI cross-references that data with your location, preferences and timing to provide the most accurate forecast.” AI is the brain in the system. It analyzes, recognizes patterns, processes the inputs and decides on the answer or action.
For this, the conversational AI systems use techniques like:
- Deep Learning: This is a subset of machine learning that uses neural networks with many — deep — layers to analyze large amounts of data. It's very effective at image and speech recognition, natural language processing and game playing. These models can learn from raw data. This allows them to solve complex problems without explicit programming.
- Knowledge Graphs: These are structured representations of information that capture the relationships between different entities in a way that is machine-readable while making sense to humans. Knowledge graphs are used to categorize knowledge — people, places, concepts, events or things, for example — to understand how these entities are related.
3 Examples of Conversational AI
You might encounter conversational AI in a variety of scenarios. Typically, wherever humans might want to tap into a large amount of data to find quick answers to questions, conversational AI can be used to do the job.
“Tools like chatbots, virtual assistants and voice interfaces are great examples of conversational AI,” explained Sondors. “You’ve seen this in action with Siri, Alexa or support bots on websites. We use AI-powered bots to qualify leads quickly and answer FAQs, freeing up our sales teams for more meaningful conversations and important decisions.”
Chatbots
Chatbots are small software programs that often sit on web sites, apps or messaging platforms. They are designed to simulate human-like conversations. They can be categorized into:
- Rule-Based Chatbots: These follow predefined rules and scripts to respond to queries. These simple tools are used for common questions or repetitive tasks.
- AI-Powered Chatbots: These chatbots tap into NLP and ML to attempt to understand questions and give an informed response. They can learn from these interactions to get better at this over time.
Virtual Assistants
Virtual assistants are considerably more advanced than chatbots. They can handle more complicated questions and tasks. They can be integrated into smartphones, computers, smart devices and various other platforms where they interact with you, understand your requests and offer answers and solutions. These assistants typically use voice recognition, natural language processing and machine learning to understand and respond to user commands.
You probably use one of these every day:
- Alexa (Amazon): This voice-activated assistant can act as a smart home controller, answer basic questions by finding answers in its own database or the internet, play music, set reminders and more.
- Siri (Apple): Siri sits in the iPhone and on Apple’s smart home devices and can help with tasks like sending messages, making phone calls and providing weather updates.
- Google Assistant: This AI-powered assistant lives in Android phones and Google smart home devices and can answer questions by searching Google, manage schedules, provide turn-by-turn navigating and act as a smart home manager.
Interactive Voice Response (IVR) Systems
IVR systems are a telephony-based conversational AIs. They can answer the phone and interact with callers, gather information and route calls to the appropriate recipient. They can answer common questions, look up account details and serve as automated customer service agents. These systems use voice recognition and DTMF (dual-tone multi-frequency) signaling to understand and respond to user inputs.
Related Article: Practical Applications of AI in Software Development
Real-World Applications of Conversational AI
Conversational AI is being used across the enterprise for a wide range of applications. “The most common use cases for businesses are organizing internal documents and customer support,” said Komninos Chatzipapas, founder of HeraHaven. But there are other uses, too.
Some notable examples of the ways conversational AI is used include:
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Customer Support
Conversational AI is a boon to customer support. Because these AI bots don’t need sleep or food, they are on the job 24/7 and can give instant responses when customers have common questions. They are usually smart enough to escalate more complex issues that they can’t handle to human agents. They save money, lighten the load for humans and make for a better customer service experience.
Sales and Marketing
AI-powered chatbots and virtual assistants can engage with potential customers, answer their questions and guide them through the sales funnel. This means there is always someone there ready to help sell and collect customer data to make the human salesperson’s job easier and inform the company’s marketing strategies.
Education
Conversational AI can act as a tutor, answering student questions, explaining concepts and providing feedback on assignments. AI-powered language tutors can engage students in real-time conversations, helping them practice and improve their language skills through interactive dialogue.
Travel & Hospitality
Conversational AI make great travel agents. They can help find flights, book hotels and suggest activities or experiences. They can also provide real-time updates on delays or cancellations. In the hospitality industry, they can handle booking queries, room requests and provide tourists with information about local attractions, restaurants and more.
Entertainment and Media
Conversational AI tools are often used in video games or interactive media to create immersive experiences, allowing characters to respond to players in a conversational manner. They are also used to recommend content such as movies, music or TV shows based on a user’s previous choices, preferences and trends.
Retail
Conversational AI is often used by retailers to provide instant support to customers, answer product-related questions, assist with returns and help with order tracking. This technology can make for a better shopping experience, too, by helping shoppers find products that match their preferences or resemble previous purchases. Retailers have even begun using AI to provide personalized shopping experiences by guiding customers through product options, assisting with size choices and suggesting complementary items.
Smart Home & IoT
If you have a smart home system such as Amazon Alexa, Google Assistant or Apple HomePod, you are already living with conversational AI. These devices can control lights, thermostats, security systems and more. They can also play games with your kids, answer your questions and help you stay on schedule.
Healthcare
Conversational AI helps healthcare providers with patient triage and appointment scheduling. It can provide medical information to patients and partners. Virtual health assistants can even offer personalized health advice and remind patients to take their medications.
Human Resources
Conversational AI is a big help in the document-heavy HR department. It can help process employee onboarding, search databases to answer HR-related queries from employees and more. With this work handled by AI, HR teams can focus on more strategic initiatives.
Banking and Finance
Conversational AI tools make terrific customer service agents for banks and other financial institutions. They can help customers with transactions, offer financial advice and provide answers about accounts and other services. This makes banking easier for customers, enhances the customer experience and improves operational efficiency.
The Benefits of Conversational AI
When you bring conversational AI into your enterprise, the biggest benefits are faster response times, round-the-clock availability and taking repetitive tasks of people's plates, according to Sondors.
“Real conversational AI isn’t about replacing humans. It is a useful tool, working alongside us, making communication smoother, smarter and a lot more efficient.”
Here are some examples:
A Better Customer Experience
With the ability to deliver instant answers — always conveyed cheerfully and often tuned to reflect the personality of your company — conversational AI helps customers interact with your company easily at all hours of the day. This frictionless and easy communication method typically leads to higher customer satisfaction.
Cost Savings
By automating routine tasks and reducing the need for human intervention, conversational AI can significantly lower operational costs. This allows your company to allocate human resources to more strategic work.
Increased Efficiency
Conversational AI can handle multiple interactions simultaneously, so customers are never asked to wait in a queue to get questions answered, pay their bill or complete transactions. This improves the productivity of your team and resolves issues faster.
Enhanced Data Collection and Analysis
Conversational AI systems can gather and analyze large volumes of data from all of its customer interactions. This data can give you valuable insight into your customer’s preferences, pain points and needs. You can use this data to inform your high-level decisions.
Scalability
As your company grows, your AI-powered systems can scale to handle an increase in interactions without compromising performance or requiring you to staff and train personnel. Similarly, it can scale down again after a spike so that you don’t have to respond to seasonal demands with the same urgency.
The Challenges of Conversational AI
There is no doubt that conversation AI brings benefits to the enterprise. But it also has challenges and risks.
Some of these include:
Accuracy & Understanding
“The biggest challenge is, as always, accuracy,” said Chatzipapas. “Even state-of-the-art LLMs are very prone to hallucination. So, it's important to have at least some level of human oversight when deploying AI solutions.”
Conversational AI does not always accurately understand and respond to user queries. This can be frustrating for customers and difficult for the business to resolve. When dealing with complex languages, idioms and context-specific nuances, the AI might deliver poor answers. Continuous training and improvement are necessary to enhance system performance.
Integration With Existing Systems
Integrating conversational AI with your existing systems and workflows can be a complex and time-consuming undertaking. It often requires investment, careful planning and expert skills to build a system that works well and is integrated with your other functions.
Data Privacy & Security
Handling sensitive customer data requires robust security measures. Conversational AI will need to be built to withstand attempted breaches and unauthorized access. You also need to comply with data privacy regulations and protect the security of your customers’ data.
User Acceptance
“The tricky part is getting it to sound natural,” explained Sondors. “AI still has a hard time with tone and context sometimes, which can make conversations feel robotic or a little off.”
People resist the idea of working with a robot. Many people are irritated by AI-driven systems because they worry about being ruled by robot overlords, find the idea creepy or are worried about privacy. Many people prefer human interaction. Building user trust and confidence in conversational AI can be challenging.
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Top Conversational AI Products
The marketplace is full of conversational AI products that are ready for you to purchase, code and install.
Some of the more popular tools are:
IBM watsonx Assistant
This powerful AI-driven conversational platform enables your organization to quickly build and deploy chatbots and virtual assistants. It offers advanced NLP capabilities, integrates with various channels and delivers robust analytics.
Microsoft's Azure AI Bot Service
The Azure AI Bot Service is a suite of tools and frameworks that can enable your team to develop, deploy and manage a conversational AI solution. It supports multiple programming languages and integrates with various AI services.
Google's Dialogflow
Dialogflow allows developers to create chatbots and voice assistants. It leverages Google's machine learning and NLP technologies and can deliver accurate and natural interactions.
Amazon Lex
Lex is Amazon’s conversational AI toolset. It can communicate via voice and text and integrates with AWS services, making it possible to create sophisticated chatbots and virtual assistants.
Rasa
Rasa is an open-source conversational AI framework that you can use to build and deploy custom-made chatbots. It is flexible, offers lots of customization capabilities and has strong community support.
Nuance's Nina
Nina is a virtual assistant solution that offers natural language processing, speech recognition and more. It is designed to enhance customer engagement and support.
