What is Conversational AI? Definition, Benefits & Limitations

Bridging the gap between humans and machines, conversational artificial intelligence is having a momentous impact on the digital customer experience.

In fact, the global market for conversational AI technologies is projected to reach $32.62 billion by 2030.

This sophisticated branch of artificial intelligence takes AI customer support to the next level by simulating human-like communicative and problem-solving abilities.

Yet, as with any emerging technology, there’s a maze of information, buzzwords, and misconceptions to navigate.

What exactly is conversational AI, and how does it work in the context of customer service?

Moreover, while the benefits seem promising, what challenges might you face in implementing a conversational AI system - and how can they be overcome?

If these questions have crossed your mind, you’re in the right place.

In this blog, we’ll demystify conversational AI, providing you with a clear understanding of the ins and outs of this technology. We’ll cover:

• What is conversational artificial intelligence, and how does it work?
• The benefits of conversational AI solutions
• The cons and limitations of conversational AI technology
• How to overcome the challenges associated with conversational AI

TL;DR:

Conversational AI encompasses a range of artificial intelligence technologies (e.g. natural language processing, generative AI, machine learning) that simulate natural communication between humans and machines.

Benefits & strengths...

• Advanced capabilities: Can automate a wide range of customer service interactions, adapting to situations with more sophistication than rule-based bots.
• Superior user experience: Offers complex, flexible, and natural conversations, improving customer satisfaction and the customer experience with human-like interactions.
• 24/7 availability: Ensures round-the-clock customer service, meeting modern expectations for immediate access to assistance.
• Cost-efficiency: Reduces operational costs by handling large volumes of interactions, leading to significant savings and a high ROI.

Challenges & solutions...

• Lack of emotional intelligence: Mitigated by creating chatbot personalities, utilising sentiment analysis, and ensuring seamless handoffs to human agents.
• Data & privacy concerns: Addressed through robust security measures, transparency with customers, and careful selection of AI providers.
• AI hallucinations: Minimised by optimising training data, implementing fallback mechanisms, and monitoring AI performance.
• Customer attitudes: Overcome by educating customers on AI benefits, making it easy to transfer to agents, and acting on feedback.

What is conversational AI?

Conversational AI refers to a spectrum of technologies that simulate natural communication between humans and machines.

This branch of artificial intelligence typically uses natural language processing (NLP), natural language understanding (NLU), generative AI (GenAI), and machine learning (ML) to understand, interpret, and respond to human inputs in an intuitive way.

At the core of conversational AI technologies is the contextual understanding and generation of language. This allows AI models to cope with more complex interactions, adapt to user inputs, and even learn over time.

In the context of customer service, conversational AI solutions are usually implemented in the form of advanced AI chatbots or virtual assistants.

These systems assist and communicate with end users via a messaging interface, effectively simulating a back-and-forth conversation.

When they’re designed and deployed successfully, conversational AI chatbots can automate a vast range of customer interactions and tasks in an efficient and scalable way.

How does conversational artificial intelligence work?

As we’ve mentioned, conversational AI technologies work by combining various forms of artificial intelligence.

This allows conversational AI tools to offer responses that are not just accurate and relevant but also appropriately detailed and engaging, creating a better user experience.

Let’s break down the components of conversational AI.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is the overarching technology that enables machines to read, decipher, understand, and make sense of human conversation.

In the context of conversational AI, natural language processing (NLP) is the first step that processes the user’s input.

It involves techniques like tokenisation (breaking down sentences into words or phrases), and part-of-speech tagging (identifying words as nouns, verbs, etc.).

Ultimately, NLP is what allows conversational AI platforms to take a human language input and prepare it for further analysis.

Natural Language Understanding (NLU)

Natural Language Understanding is a subset of NLP focused specifically on understanding the intent and context behind the user's input.

This is not just about processing words but getting to the heart of what the user wants and means.

For example, if a customer asked, “How do I update my billing information?” a system using NLU would be able to understand that the customer’s intent is to change the payment details associated with their account.

NLU involves tasks like entity recognition (identifying key elements like names, places, dates) and sentiment analysis (detecting the user’s mood or opinion).

This step is crucial for determining how the system should respond to a specific request or question.

Generative AI

Generative AI comes into play in the response generation phase.

Using the insights from NLP and NLU, generative AI facilitates natural language generation (NLG).

This enables the creation of fluid responses that are coherent, contextually relevant, and appropriately complex.

This is where the AI moves beyond pre-defined or canned responses to generate replies in real time that are tailored to the specific human conversation.

Conversational AI chatbots that leverage generative AI can produce responses that closely mimic natural language patterns, making the conversation feel fluid and engaging.

Machine Learning (ML)

Machine learning is what allows conversational AI systems to learn and improve over time based on the data they collect from interactions with end users.

It involves training the system on large datasets of customer interactions so it can learn patterns, styles, and preferences. 

This training helps the AI predict the most appropriate responses based on the context provided by NLU.

Machine learning algorithms can adjust their responses over time as they are exposed to more data, which means the more the system is used, the better it gets at understanding and responding to users.

Strengths & benefits of conversational AI

Conversational AI capabilities offer a multitude of benefits for both businesses and consumers.

Let’s explore each of the key advantages in turn.

1. Advanced capabilities & automation

Before the emergence of conversational AI tools, automated customer support was mainly delivered by simple, rule-based chatbots.

These systems typically operate using a predefined set of rules and scripted responses known as a “decision tree” framework.

As rule-based bots are limited to their pre-set parameters, they lack the adaptability and sophistication of modern AI-powered models.

Consequently, this type of bot design only really shines when it comes to answering FAQs. Any query or issue beyond that often leads to inadequate responses and a subpar user experience.

In contrast to this, AI-powered chatbots can process huge datasets, produce human-like outputs, adapt to unique situations, and refine their performance over time.

They can even provide multilingual customer service using real-time translation technology, allowing end users to communicate in their native tongue.

These advanced capabilities mean that a conversational AI chatbot can automate a much wider array of interactions, queries, and tasks - effectively becoming an intelligent virtual agent for your customers.

That said, rule-based chatbot systems aren’t completely redundant. They can be a great method for steering conversations and can even act as a fallback mechanism for AI.

It’s why the Talkative solution has the option to incorporate conversational AI into a decision tree bot design.

A hybrid chatbot like this might answer FAQs using a decision tree framework but switch to AI when confronted with more complex issues or queries.

This approach combines the efficient simplicity of rule-based systems with the intelligence and flexibility of AI, allowing you to get the best of both worlds.

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2. Superior user experience

The advanced capabilities of conversational AI solutions naturally create a superior user experience.

While rule-based bots run the risk of delivering a frustrating “bot-like” interaction, conversational AI models allow for a more humanised and seamless customer experience.

With AI enabling more complex, flexible, and natural conversations, these systems can offer detailed and contextually relevant outputs that are almost akin to a human response.

One of the ways conversational AI bots achieve this is by integrating with an AI knowledge base.

Talkative’s Generative AI chatbot is a good example of this.

With our solution, you can use file-based content and web pages from your company website to create multiple knowledge bases.

Then, the AI bot can be trained using your knowledge base datasets to answer innumerable queries about your business, products, and services - using the information provided plus conversational AI.

This optimises the user experience in two ways.

Firstly, it enhances the accuracy and consistency of chatbot support by allowing it to extract information straight from your knowledge base resources.

Secondly, it considerably broadens the scope of queries/issues that can be fully automated. This means your customers have a higher chance of getting their problem resolved immediately - without needing to transfer to a human agent.

Ultimately, the incorporation of conversational AI into chatbot architecture ensures that customers receive the best automated service possible and a user experience that closely mirrors human communication.

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3. 24/7 availability

The 24/7 availability of conversational AI is a game-changer for customer service.

Unlike traditional customer contact channels, which are limited by business hours and human resource availability, AI-powered chatbots are always on, ready to assist customers at any time of day or night.

For businesses operating globally or in multiple time zones, this feature is indispensable, ensuring that customers feel supported and valued at all times.

The constant availability of chatbots and virtual assistants meets the modern consumer’s expectation for immediate access to assistance and self-service.

Furthermore, the impact of 24/7 support extends beyond customer convenience - it also allows businesses to better manage and reduce the load on their customer service teams.

By providing automated support and instant, accurate self-service, conversational AI can handle a significant volume of interactions without human intervention.

This is significant as 81% of consumers will try to resolve queries themselves before contacting an agent, and 67% prefer self-service over speaking to a human.

In turn, you’ll experience reduced wait times, a faster average response time, happier agents, and a more efficient support system overall - all of which benefit your customers as well as your business.

4. Maximum cost-efficiency

Implementing conversational AI can lead to substantial cost savings for businesses.

By automating customer conversations and various tasks, customer service chatbots slash costs through efficiency gains, optimal agent productivity, and significant time-saving.

What’s more, the scalability of conversational AI platforms means you can manage increasing volumes of queries/tasks without hiring more agents.

This improved efficiency can lead to significant operational savings, allowing businesses to allocate resources more effectively and invest in other areas of development.

In fact, conversational AI models have been found to decrease customer support costs by up to 40%.

Not only that but recent research has found businesses enjoy a 250% average ROI for their AI investments.

Challenges of conversational AI (& how to overcome them)

While conversational AI boasts many benefits, it also poses some challenges. Even an advanced conversational AI model won’t be completely immune to limitations.

In this section, we’ll dissect the main challenges conversational AI brings and the steps you can take to mitigate/overcome them.

1. Lack of emotional intelligence

While conversational AI can mimic human interactions, these systems fall short when it comes to replicating genuine emotional intelligence and empathy.

Empathy is crucial in natural communication, enabling humans to understand and react accordingly to other people’s feelings, concerns, and individual circumstances.

A lack of emotional intelligence can be a significant challenge with AI, as it impedes its ability to provide a meaningful and humanised customer experience.

This means a conversational AI tool might not fully understand or respond appropriately to the emotional states of customers.

In emotionally heightened support scenarios where sensitivity or tact is required, an absence of emotion can make the interaction feel cold and mechanical - which may even exacerbate the customer’s frustration or distress.

This can prove a roadblock to building positive customer relations and may lead to customer dissatisfaction or a feeling of being undervalued by your brand.

To mitigate the lack of emotional intelligence found in conversational AI applications, businesses can take the following steps...

• Create a chatbot personality: Developing a compelling personality for your conversational AI chatbot can help its responses feel more personal, emotive, and engaging. This involves defining a tone and style of communication for your bot that aligns with your brand and resonates with your target audience. A well-crafted bot personality can bridge the gap between mechanical responses and human language, creating a more empathetic conversation experience.
• Utilise sentiment analysis: Sentiment analysis technology can significantly improve conversational AI’s ability to identify and respond to customer emotions during interactions. Sentiment analysis algorithms detect the underlying tone and emotional context of user messages, allowing the AI to adjust its responses to be more empathetic and contextually appropriate. By recognising whether a customer’s sentiment is positive, negative, or neutral, the AI can tailor its communication style, potentially defusing emotionally charged situations.
• Ensure seamless handoffs to humans: It’s important that end users have the option to bypass your AI and speak to a human agent when needed. This not only ensures that customers are receiving the support they need in sensitive or complex situations but also helps in maintaining customer loyalty and trust.

2. Data & privacy concerns

One prevalent issue surrounding the use of conversational AI in customer service is privacy and data security.

Currently, there’s a degree of concern regarding how AI systems use and process user data. As AI technologies are still relatively new, these reservations are understandable and only natural.

AI-powered chatbots and virtual assistants can collect and process vast amounts of user information during interactions, posing risks if this data is not properly secured.

Customers may even be wary of disclosing certain details or information to a chatbot due to fears of data misuse or breaches.

Additionally, many providers of conversational AI solutions integrate their platforms with other AI companies and models (e.g. OpenAI's Large Language Model, ChatGPT).

This can be a notable concern for businesses worried about sharing their interaction data with third parties and how it may be stored/used.

The data and privacy concerns surrounding conversational AI systems can be addressed in the following ways...

• Adhere to robust security measures: It’s crucial that any conversational AI platform you implement has stringent security protocols in place to protect user data. This includes end-to-end encryption of messages, secure storage of interaction data, regular security audits, and compliance with data protection regulations (e.g. GDPR, CCPA).
• Be transparent with customers: You should clearly communicate to customers how their data is being used, stored, and protected. Providing information about data handling practices can help alleviate concerns and build trust with consumers. This includes informing customers about the specific types of data the AI collects, how it contributes to improving service, and any options they have for managing their data, such as accessing, correcting, or deleting their information.
• Check privacy policies: Before using software that integrates with third-party AI solutions or models, it’s essential for businesses to thoroughly review and understand their privacy policies and data usage terms. This review should ensure that third-party practices align with your company’s data protection standards and comply with relevant regulations.

3. AI hallucinations

AI hallucinations refer to instances where AI systems like chatbots output incorrect, nonsensical, or misleading information in their responses.

This phenomenon is not due to the AI intentionally deceiving or misleading. Rather, it arises from limitations in the AI's training data, algorithms, or the inherent complexities of language processing.

These shortcomings can lead to the dissemination of false information, inappropriate responses, or answers that don't align with reality, despite sounding plausible or being presented with confidence.

Essentially, when an AI model encounters a query or a context that is poorly represented in its training data or when it's asked to make inferences beyond its capabilities, it might "hallucinate" details or create responses that have no basis in reality.

For example, in text-based AI models like OpenAI's ChatGPT, hallucinations may manifest as factual inaccuracies, made-up quotes, or fictional events presented as real.

These occurrences can confuse or mislead end users, erode trust in the AI system, and potentially lead to poor customer service.

You can minimise the risk of AI hallucinations with the below practices...

• Optimise training data: The quality of conversational AI outputs is largely dependent on the quality of training data. Therefore, training the AI on extensive and pre-approved datasets will reduce the likelihood of hallucinations and optimise overall performance. This involves keeping your AI knowledge bases up to date with accurate and comprehensive information, defining clear boundaries for the AI’s scope of knowledge, and exposing your bot to a wide range of conversation scenarios.
• Implementing fallback mechanisms: Designing AI systems with fallback mechanisms that trigger when the AI is unsure about a response is a great way to prevent hallucinations. For instance, AI chatbots can be programmed to escalate unknown queries to human agents or respond with phrases indicating uncertainty, such as "I didn't quite get that. Let me connect you with the team."
• Monitor performance: Effective performance management is vital for mitigating the risk of AI chatbot hallucinations and misunderstandings. You can best achieve this by tracking metrics and leveraging chatbot analytics or reporting features. For example, with the Talkative platform, you can use our AI Knowledge Gap Report to produce a full list of all the questions customers have raised with your chatbot, plus whether or not the bot was able to answer them using your knowledge base. Armed with this information, you can optimise and expand your AI's training dataset, eliminating hallucinations while improving the accuracy and performance of your chatbot.

4. Customer attitudes

Getting consumers to like, trust, and engage with your conversational AI chatbot can be challenging.

Although 64% of people believe that AI has the potential to improve customer service, there’s still a degree of negative customer perception or cynicism towards customer service chatbots and automation.

This often stems from a preference for human conversation, misconceptions about AI, or scepticism towards AI’s ability to understand and solve problems effectively.

These attitudes can make consumers resistant to engaging with AI-powered tools, impacting adoption rates and customer satisfaction levels.

If customers perceive your AI chatbot as unhelpful or as a barrier to human support, it can create annoyance, disappointment, and detachment.

That said, not everyone is pessimistic towards AI-powered customer service. In fact, 40% of consumers don’t care if they’re helped by an AI tool or a human as long as their question gets answered.

This indicates that you can overcome a lot of scepticism by devising an AI system that proves doubtful users wrong by being able to meet their needs and resolve their queries efficiently.

Negative user attitudes towards conversational AI can also be overcome by...

• Educating customers: Informing customers about the benefits of conversational AI, such as instant responses and advanced human language capabilities, can help challenge and change negative perceptions. Consider implementing marketing campaigns that highlight the advantages of AI solutions for customers, plus the security measures in place to protect their data and privacy.
• Making it easy to transfer to a human: Providing clear options for customers to opt out of AI interactions and speak with a human agent can alleviate cynicism and give customers control over their service experience. Knowing they can easily switch to a human agent at any point often makes customers more open to trying AI-based services.
• Acting on customer feedback: Actively seeking and incorporating customer feedback can guide businesses in refining their conversational AI tools to better meet customer needs and preferences. This approach demonstrates a commitment to improving the user experience and can transform negative attitudes over time.

The takeaway: Implementing conversational AI with Talkative

Conversational AI is no longer a futuristic concept but a reality that’s revolutionising business operations across industries.

By unlocking the full potential of AI to understand, respond, and assist, conversational AI systems are setting new standards for digital customer service.

Understanding how conversational AI works, its benefits, and how to manage its limitations will empower you to get optimal results from this technology.

But for any conversational AI solution to succeed, it also needs to be powered by the right platform.

That’s where Talkative comes in. With our chatbot solution, you can:

• Choose between a conversational AI-powered bot or an intent/rule-based system (or a combined approach - an AI chatbot with rule-based fall-back for maximum efficiency).
• Integrate our Generative AI bot with your own AI knowledge base to create virtual assistants that are experts in your brand, products, and services.‍
• Automate agent training with conversational AI simulations of customer service interactions
• Gain enterprise-grade AI features at a fraction of the usual cost.
• Seamlessly escalate from chatbots to human agents when needed.
• Leverage AI-driven analytics and reporting.
• Meet and serve customers across your website, app, and messaging channels.

In addition to chatbots and AI solutions, we offer a suite of customer contact channels and capabilities - including live chat, web calling, video chat, cobrowse, messaging, and more.

Our solution also supports numerous integrations into other contact centre systems and CRMs. In fact, our Salesforce integration is one of the most in-depth on the market.

Want to learn more? Book a demo with Talkative today, and check out our interactive product tour.