What is Conversational AI and what can you do with it?

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In this blog, read all about how conversational AI is transforming customer service, and how you can use it to your advantage.

First, it's good to define conversational AI. When we talk about conversational AI, we are essentially talking about a form of artificial intelligence (AI) that can simulate human conversations. Sounds pretty simple.

While the definition may sound simple, the operation and underlying mechanics are a bit more complex.

Conversational AI uses advanced technologies such as natural language processing (NLP) and machine learning (ML) to understand, respond to and learn from interactions with language. This technology enables systems to interact naturally with people through text, such as chatbots, or through speech, such as voicebots.

What is the difference between conversational AI and chatbots?

To understand how conversational AI and chatbots are related, it is good to explain how chatbots work.

There is a difference between traditional chatbots and chatbots that use conversational AI. Traditional chatbots do not use conversational AI technology, but are rule-based. These chatbots provide limited responses based on predefined scripts.

Chatbots based on conversational AI can process complex questions and respond much more dynamically. Rule-based chatbots often understand only specific words and phrases and struggle to deal with unexpected questions. Conversational AI, on the other hand, can understand the context of a conversation and learn from new interactions, making it much more flexible and intelligent.

In other words, traditional chatbots will give the same answer in many cases, while chatbots using conversational AI continue to learn from interactions.

This ensures that the answer the chatbot gives to one person may be slightly different from the answer the chatbot gives to another person. So it depends entirely on the context at the time.

Natural language processing and machine learning: what about it?

A key component of Conversational AI is Natural Language Processing (NLP). NLP enables technology to understand human language, including its nuances, emotions and intentions.

NLP analyzes the grammar, meaning and context of words to interpret the user's question very precisely to provide a relevant answer. NLP makes interaction with a chatbot feel fluid and human.

Conversational AI also uses machine learning (ML), a technique that allows the system to improve itself through experience. As the AI engages in more conversations, it learns more and more to anticipate what the user needs and how best to help them.

This makes for more personalized and accurate conversations in addition to more efficient interactions.

The history and evolution of conversational AI

The technology behind conversational AI has come a long way, and developments continue apace today. Perhaps even faster than ever before.

Artificial intelligence is getting smarter and smarter and applied in more and more ways. To understand where we are now in that development, it is good to look at where artificial intelligence, and conversational AI in particular, started.

The concept of a computer that can simulate human conversations was first developed in the 1960s with the creation of ELIZA. This chatbot was one of the first programs that could respond to text input. ELIZA simulated a psychotherapist by simply repeating the user's answers in the form of questions. Although ELIZA was not truly intelligent, it became a pioneering experiment in human-computer interaction. Note that this took place years before the Internet revolution!

In the 1970s and 1980s, the first knowledge systems emerged, based on preset rules, but these were very limited and could not carry out complex conversations.

A breakthrough followed in the 1990s with NLP, which allowed chatbots to have conversations that felt human.

Conversational AI emerged in the early 2000s with the introduction of virtual assistants such as SmarterChild and later Siri. Siri, which we all know today from Apple, combined speech recognition with machine learning, which led to the development of voice assistants such as Google Assistant and Alexa.

In the 2010s, machine learning really revolutionized conversational AI. Systems became self-learning, allowing them to improve their performance without the need for new rules.

Deep learning allowed chatbots and voicebots to respond even more naturally and understand more complex language, which led to a huge growth in the use and applications of AI-driven customer interaction.

Conversational AI today

These days, conversational AI is an integral part of customer interaction.

The technology is now so advanced that it is able to proactively assist consumers, provide multilingual support and even detect emotions in conversations.

No one has a crystal ball, but we think conversational AI in the future is all about even deeper integration into the daily lives of consumers and businesses.

Conversational AI will likely focus on more self-directed and context-aware systems. The use of predictive analytics to estimate customer needs in advance, emotional intelligence to humanize conversations, and omnichannel integrations will be central to further development.

In addition, multilingual AI will become increasingly important to serve customers worldwide without language barriers.

Applications of Conversational AI in customer service

It's clear by now that conversational AI is transforming the way companies interact with their customers. But what specific applications are we seeing in action today?

What benefits does conversational AI offer?

Alright, we know the practical applications that are possible with conversational AI. But what benefits does applying conversational AI in your customer service actually offer?

A tip of the hat: It goes beyond simply increasing customer satisfaction.

24/7 and real-time answers in the user's language naturally makes for happier customers, provided they are well served. If that is the case, it results in fewer repeat inquiries, which in turn reduces costs. In the process, fewer employees are needed, which also saves costs.

Another benefit of deploying conversational AI is the tremendous scalability it brings.

Conversational AI makes it easy for companies to scale their customer service without hiring additional staff. Whether you have thousands or millions of customers, an AI system can effortlessly handle large volumes of customer inquiries without creating bottlenecks.

This is especially useful during peak periods, such as holidays or during product launches. Speaking of scalability!

In addition, deploying conversational AI increases efficiency, which comes from the scalability it provides. By automating simple tasks, employees can focus on more complex and valuable tasks that require more human expertise.

And perhaps most importantly, conversational AI offers the ability to personalize customer interactions.

Unfortunately, when it comes to improving customer service, too often people still think in terms of the benefits it brings to a company, but at the end of the day, you want to help the customer in the best way possible.

Conversational AI does just that: improving customer service, by helping customers move forward with their problem or question with a personalized and contextual answer.

Emotional intelligence and sentiment analysis

The last benefit described, namely personalizing customer interactions, goes beyond a customized response. In fact, Conversational AI can also improve customer interactions by using sentiment analysis and emotional intelligence.

Like humans, AI can understand the context of a conversation.

Through sentiment analysis, the AI can detect a customer's tone and emotion, such as frustration or enthusiasm, and respond accordingly.

For example, a customer who sounds dissatisfied will receive a more empathetic response, while a satisfied customer will receive an additional recommendation.

This combination of personalization and emotional intelligence creates a more human interaction.

However, it remains crucial to clearly indicate whether a customer is interacting with a human or a text- or voice-based system, especially as empathic responses increasingly blur the line between human and machine.

What about conversational AI and privacy?

One of the biggest concerns around conversational AI is privacy. And that, of course, is justified.

After all, customers are entrusting their personal data to AI systems. From basic information to sensitive data such as payment details or medical information. Companies must therefore do everything they can to protect this data.

But how do you ensure that privacy is safeguarded?

It starts with data minimization: AI chatbots should collect only the information strictly needed to perform the task. This not only limits the amount of data stored, but also reduces the risk of misuse or data leakage.

It’s also important to encrypt data during transmission and store it on secure servers that comply with international privacy regulations, such as the AVG (GDPR) in Europe.

In addition, transparency is important. Customers should be clearly informed about how their data is used, with whom it is shared, and how long it is kept.

This can be done through a privacy statement and the option to consent to data processing (opt-in). Moreover, it is important to always give customers the option to delete or access their data.

The power of an omnichannel strategy

Conversational AI plays a crucial role in a well-functioning omnichannel strategy.

After all, customers want to be able to start conversations on the website and then continue them via WhatsApp, for example, just as they do with friends and family. In doing so, they expect not to have to ask their question over and over again.

Therefore, it’s essential that all channels are well integrated. When channels do not work well together, it can lead to frustration and a negative customer experience.

To avoid this, the technology behind conversational AI helps ensure that customer data and interactions are managed centrally, regardless of the channel used. This enables a seamless customer experience, where all communication flows flawlessly into one another.

Companies that integrate their systems into a single platform make their service more efficient and smarter.

A flexible AI solution placed on top of existing systems also makes it possible to implement an omnichannel strategy without major infrastructure changes.

De ROI of conversational AI

Sure, real-time support in multiple languages and automation in many cases yield satisfied customers.

But what does implementing conversational AI yield financially? In other words, what is the ROI (Return on Investment)?

The ROI is primarily in operational efficiencies and cost savings. By automating repetitive tasks, such as answering frequently asked questions or processing simple customer requests, you can reduce personnel costs.

This allows agents to focus on more complex tasks that add more value. This increases productivity, reduces response times and provides a better customer experience.

In addition, conversational AI enables scalability without exponentially increasing costs. Whether a company serves 10 or 10,000 customers at a time, an AI-powered solution can handle all interactions with the same ease.

And don't forget the value of data analytics: with AI, companies can gain deep insight into customer needs and behaviors, which in turn leads to more targeted marketing strategies and product improvements.

Getting started with conversational AI yourself?

Seamly offers businesses a powerful solution to effectively deploy conversational AI and integrate it with existing customer interaction systems.

Through our flexible platform, you can provide seamless customer service on your live chat, chatbot, voicebot and social channels without any infrastructure changes.

Want to learn more about how we can work together to take your customer service to the next level? Sign up for a free demo or get in touch!