What is conversational AI, and how does it work?
If you’ve ever interacted with cutting-edge customer service chatbots, virtual assistants like Siri, or intelligent IVR (Interactive Voice Response) systems, you’ve already encountered conversational AI.
Conversational AI is one of the most popular and widespread forms of artificial intelligence. By 2030, experts believe the market for this technology will grow to $49.9 billion.
This type of AI can help companies deliver 24/7 efficient and personalized services to customers, provide access to valuable business insights, and increase sales. But how exactly does this technology work, what makes it different from other AI models, and what does the future look like?
I’ll answer all those questions and more with this comprehensive guide.
What is Conversational AI?
Conversational AI (artificial intelligence) is a form of AI technology capable of mimicking or simulating human conversation. It powers tools like chatbots and virtual agents, leveraging huge volumes of data, machine learning, and natural language processing (NLP) techniques to understand speech and text inputs.
So, is conversational AI just another word for “chatbots”? Not necessarily. Traditional chatbots were designed to simulate human discussions, but strict rules govern them. They answer questions by mapping the words input by a user to a selection of pre-configured responses.
Conversational AI, on the other hand, learns from each interaction, grows, and understands context and intent, allowing it to respond more effectively to humans.
This AI can power various types of bots and virtual assistants too, such as:
- Generative AI bots: While generative AI and conversational AI aren’t the same, generative AI bots can leverage conversational AI methodologies to interact with human beings, producing creative responses to queries with natural language generation (NLG).
- Voice assistants: Voice assistants use automatic speech recognition and “speech to text” (STT) technologies to understand human speech and voice commands. Examples include options like Amazon Alexa, Siri, and Google Assistant.
Besides chatbots and AI copilots, conversational AI is present in various other applications, such as text-to-speech software used to create audiobooks, speech recognition software for transcription and translation, and conversational analysis tools.
The Difference Between Conversational AI and Generative AI
You might wonder how conversational AI differs from generative AI since both solutions are designed to facilitate natural conversations between machines and humans. Simply put, generative AI is a solution that can generate new content based on input.
Like conversational AI, generative AI is powered by machine learning (foundation models) and can improve its output over time. However, both tools have different goals. The purpose of conversational AI is to understand human speech and enable conversations.
Generative AI, on the other hand, aims to develop original, new content based on prompts. It’s a tool that augments creativity rather than simply communicating with humans. However, it is worth noting that many AI tools today, including many modern chatbots, use both conversational and generative AI simultaneously.
How Does Conversational AI Work?
Conversational AI uses foundation models, machine learning, natural language processing (NLP) techniques, and other AI technologies. These solutions allow computer systems to translate human input (text and speech) into a language machines can understand.
The top systems are trained on huge volumes of data (text and speech), which show the system how to understand human language. Over time, as the system gathers more data, it can become increasingly efficient at delivering contextual responses to questions.
A typical flow will include:
- Input recognition: An interface that allows users to input text into a system or speak to a bot. For voice interactions, bots use Automatic Speech Recognition (ASR) and speech-to-text technology to convert speech into an understandable format.
- Natural language processing: NLP solutions allow machines to extract the intent from audio or textual input and translate that into structured data.
- NLU and NLG: Natural language understanding allows the machine to process the data based on meaning, context, and grammar. Natural language generation allows the system to respond based on its analysis on the most accurate answer to a query.
The Components of Conversational AI
Not all conversational AI systems are alike. Some are used specifically for customer service, while others are designed to extract valuable data from interactions (like sentiment analysis tools). However, most systems rely on a few key components:
Machine Learning Algorithms
Machine Learning, the subfield of AI that allows computers to learn from data without being specifically trained and programmed, is crucial to conversational AI. These systems comprise various features, data sets, and algorithms that improve themselves with experience, growing more advanced over time.
Some cutting-edge AI tools leverage deep learning algorithms, neural networks, and more advanced mechanisms to deliver unique responses. Reinforcement learning may also be used to help boost accuracy.
Data Analysis
Data mining, input generation, and analysis are often considered components of “Natural Language Processing,” but they’re also worth examining individually as elements of conversational AI. Intelligent systems need to collect input from users and transform it into a format that the machine can understand.
Depending on the type of AI system, developers can design solutions that can analyze text, understand speech with automatic speech recognition (ASR) and speech-to-text capabilities, and even evaluate images using computer vision.
Natural Language Processing, Understanding, and Generation
NLP is the method conversational AI solutions use to analyze and understand language with the help of machine learning. NLP can consist of multiple steps, including:
- Input generation: Initially, users input data into a chatbot or application in voice or text for the system to review and analyze based on its existing data set.
- Input analysis: If the information entered is text-based, the AI solution uses Natural Language Understanding (NLU) to decipher intent and meaning. When the input is speech-based, the system uses ASR, followed by “NLU.”
- Response generation: After evaluating the input, the system formulates a response using “Natural Language Generation.” It predicts the best response for each user based on the input’s intent and the AI model’s training data.
What is Conversational AI Used For? Use Cases
Most people associate conversational AI with chatbots, voice assistants, and customer service – for good reason. Conversational AI is everywhere in the contact center today, growing increasingly more advanced.
Notably, while conversational AI is considered a form of “weak AI” or “narrow AI” because it focuses on a narrow field of tasks, it’s still extremely versatile. Plus, it’s worth noting that “strong AI” is still just a theoretical concept.
Some of the most common use cases include:
Enabling Self-Service
Online chatbots and virtual assistants are becoming increasingly common in the contact center landscape. Around 51% of customers even say they prefer interacting with bots when they want rapid answers to their questions. Messaging bots on ecommerce sites, virtual agents built into phones and speakers, and even intelligent IVR systems all use conversational AI to enable self-service.
Improving Accessibility
Conversational AI is a powerful tool for improving accessibility, particularly for people who already use assistive technologies. You might already be familiar with apps that use text-to-speech dictation to support those with vision issues. Some tools can even translate content into different languages, enabling global multilingual customer service.
HR Processes
Conversational AI can optimize numerous HR (Human Resources) tasks. With this technology, companies can create smart assistants for employees that guide them through conversations or offer real-time coaching. AI assistants can also walk users through onboarding processes and help them troubleshoot common problems.
Data Capture
Conversational AI tools are excellent for capturing user data and feedback. They can gather insights from customers automatically in a contact center and analyze the emotion behind conversations with sentiment analysis. AI tools can also track various stages in a customer journey and help companies make intelligent growth decisions.
Automation
Like many AI tools, conversational systems are frequently used to automate tasks, from data capture to customer interaction. They can send transactional messages to customers about their order status, or help them tackle common problems, or assist clients in completing purchases. Some AI tools can proactively reach customers with product recommendations and suggestions using predictive analytics and rich data insights.
The Benefits of Conversational AI
Conversational AI might seem to be everywhere today, but we’re still in the early stages of discovering all the benefits this technology can bring to people and businesses. As AI algorithms continue to evolve, the opportunities could be limitless. Right now, however, the biggest benefits include:
Improved Customer Experiences
Conversational AI chatbots take self-service to the next level. They enable access to 24/7 and immediate support for a wide range of customers worldwide, reducing frustration caused by long waiting times. These tools can draw information from past customer interactions to create more immersive, personalized experiences.
For instance, a bot can leverage insights into a customer’s previous purchasing history, as well as data about intent to send specific product recommendations to each buyer. Some solutions can even upsell and cross-sell when interacting with customers, increasing revenue potential for companies.
Moreover, because conversational AI can interact with customers in multiple languages and convert responses into different formats, it can improve accessibility for all users. The right tools can even provide businesses with valuable insights into customer behaviors, trends, and sentiment, helping them to plan more comprehensive journey maps and customer service strategies.
Improved Operational Efficiency and Reduced Costs
Staffing any customer service department can be expensive, particularly as your target audience grows and contact volumes increase. Conversational AI solutions allow companies to deflect some of the most common questions that would reach agents to self-service alternatives.
This benefits companies and consumers in a multitude of ways. First, customers get more consistent, faster responses to their queries on a 24/7 basis. Second, team members spend less time handling routine and repetitive tasks so they can focus on more important, strategic work.
At the same time, conversational AI reduces operational costs by minimizing the number of additional team members businesses need to hire to manage increasing call volumes and customer requirements. Businesses can offer around-the-clock service without hiring a global team.
Scalability and Growth
Conversational AI is a highly scalable solution. Companies can adapt their AI technologies to address various use cases over time. For instance, they can combine AI bots with intelligent IVR systems to enhance customer service. They can also create virtual agents to coach, train, and onboard new employees.
AI solutions can be adapted to serve customers from different regions and dialects, as well as those with various communication requirements and needs. What’s more, because many conversational AI solutions come with built-in analytical capabilities, they offer businesses the insights they need to make intelligent decisions about how to scale and grow.
Some solutions can even use predictive analytics to help businesses determine how to allocate resources and staff during various periods of the year.
The Challenges of Conversational AI
Conversational AI is a powerful resource, but like many cutting-edge AI technologies, it’s still in its infancy and faces various problems. There are still systems that can struggle with different types of language input. Dialects, accents, and even background noise can impact how well a system can understand a user’s query.
Noise cancelling tools and advanced training algorithms can help address this issue, but we’re still searching for a way to ensure conversational AI can effectively understand true human emotion and tone, this can make sentiment and intent analysis more complex.
Additionally, like generative AI, conversational AI has security, privacy, and compliance issues to consider. These tools can accidentally generate incorrect and inaccurate responses to questions, occasionally show bias based on their training data, and be subject to attacks and data breaches.
In addition, while people are becoming more comfortable with the concept of using AI in their everyday lives, there is still some apprehension in the market. Not all customers trust bots to keep their sensitive information safe or deliver the best answers to their questions.
Many consumers still prefer to interact with human beings, to the extent that some regulators are considering implementing the “right to talk to a human” in customer service. Conversational AI bots can’t show true empathy or compassion, so they also can’t completely replace human beings in training, onboarding, and customer service.
How to Implement a Conversational AI Strategy
These days, investing in conversational AI is a lot easier than it once was. Many contact center and customer service technology vendors already embed AI tools into their ecosystems, ensuring businesses don’t have to create their own models from scratch.
However, it’s still important for organizations to implement the right strategy.
1. Establish Use Cases and Goals
The only way to ensure your conversational AI solution delivers a significant return on investment is to determine what you want to accomplish with the technology. Do you want to automate customer experiences and enhance self-service or deliver more support to agents?
Be specific about the problems you want to solve to ensure you can identify the right conversational AI technology for your team. Specify which metrics and KPIs you will track to determine whether your solution is driving positive results and consistent growth.
Remember to think carefully about the specific challenges you’ll face, too. For instance, if you’re in a highly regulated industry, ensuring you work with a vendor with robust security systems and policy control features in place will be essential.
2. Be Selective with Automation
One of conversational AI’s biggest benefits is that it can help businesses automate various tasks, from employee onboarding to customer service. However, not everything should be automated by artificial intelligence. Keeping humans in the loop is still crucial for building valuable relationships in a range of environments.
Leverage business data for insights into where conversational AI can offer the most value without detracting from customer experiences. Repetitive tasks with clear solutions are excellent candidates for automation with AI. For instance, you might allow users to automatically track the progress of an order or request with an online app.
In all instances, make it easy for a user to escalate an interaction with a bot into a conversation with a human being when necessary.
3. Consider your Budget, Resources, and Infrastructure
Next, after you determine how you’re going to use conversational AI, determine what kind of resources you can allocate to the implementation. If you’re running a smaller business, you’ll likely want a cost-effective no-code solution that doesn’t require a lot of training and configuration.
However, remember to ensure you can customize your solution to match your brand’s tone and intended goals. A flexible solution will allow you to create more powerful experiences for end-users.
Additionally, consider your existing infrastructure carefully. Ideally, your solutions should easily integrate with the tools you already use for customer support, information management, and employee onboarding or training.
4. Choose the Right Technology
Next, choose the technology you’re going to use to develop your AI solution. You may build a system from scratch if you have the right technical resources in-house. There are even low-code and no-code tools available today that can make it easier to design AI workflows.
If you’re opting for a pre-built solution, consider your options carefully. Look at how the conversational model has been trained. For instance, if you’re using a system for customer service, check to see if it’s been trained on billions of genuine customer interactions.
Examine whether the solution will integrate seamlessly with your existing tools, and how much support your vendor can offer to help you configure your technology. Additionally, look at how often the vendor invests in research and development, to improve its software.
5. Track, Evaluate and Optimize
Finally, implementing a conversational AI solution isn’t a set-it-and-forget-it process. Although these tools can automatically learn and improve over time, it’s also your responsibility to ensure that your system is evolving in the right direction.
Collect customer feedback, user feedback, and data to find out exactly how well your solution is performing. Pay attention to any challenges users are having with your technology and look for ways to resolve them with additional training and fine-tuning.
Moreover, since AI is constantly evolving, it’s important to keep up to date with the latest developments in the market. This will help you to implement new features and capabilities into your models over time, increasing your return on investment.
What is Conversational AI? Finishing Thoughts
Ultimately, conversational AI is a powerful resource that allows computers and human beings to interact seamlessly. This technology serves a range of use cases, from improving customer service interactions, to making teams and employees more productive.
As the AI landscape continues to evolve, with new training models, generative AI, and advanced capabilities, conversational AI’s value will only increase.
The key to success with conversational AI implementation is ensuring you find the right technology, identify the correct use cases, and use your technology to augment rather than replace human beings.
FAQs
What’s is the difference between a chatbot and conversational AI?
A chatbot is a tool that can simulate human-like conversations with customers and users. Chatbots can be rules-based or AI-driven. Conversational AI is a broader form of technology, that enables computers to understand, process, and simulate conversations. It can also analyze intent and sentiment in discussions, and power a range of applications.
Is ChatGPT a conversational AI?
Technically, ChatGPT is a generative AI and conversational AI tool. It leverages generative AI to produce creative responses to prompts, but it also uses conversational AI to process input, understand human language, and interact with people. Many similar generative AI bots also use elements of conversational AI.
What is an example of conversational AI?
There are many examples of conversational AI in the modern world. Virtual assistants like Amazon Alexa and Siri are examples. Messaging bots on ecommerce platforms and websites also use conversational AI. Intelligent Voice Response systems can also leverage this AI to enable self-service.
