All you need to know: what is a conversational AI platform and how does it work?

May 6, 2024
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Businesses want to meet customer expectations and... well make them happy. That's what it's all about right, hence businesses are continuously streamlining operations and enhancing customer interactions and increasingly turning to conversational AI platforms. So much so, that the conversational AI platform market is forecast to grow to $13.2 billion in 2024 to $49.9 billion by 2030.

Conversational AI platforms blend automation with a personal touch. They can transform your communication strategies as they are able to understand, process, and respond to human language in a natural manner. Businesses are using them to power chatbots and virtual assistants that improve real-time customer support, gather insights from cusomters and fuel engagement without requiring direct human input. This reduces cost and maintains or even improves customer support.

In this blog, we will look at what exactly a conversational AI platform is, why they have become so popular, and what features make a good platform. So here's everything you need to know.

What is a conversational AI platform?

Conversational AI, also known as CAI, is a branch of artificial intelligence that allows software to interact with humans in a conversational manner using natural language. 

This technology uses advanced machine learning and natural language processing (NLP) to analyse and understand speech or text inputs. It is then able to generate intuitive and more personable responses that closely imitate a real human interaction. 

There are various levels of complexity in these platforms with some designed for small businesses and others for large enterprises. For example, an enterprise conversational AI platform may act as a comprehensive solution for businesses, enabling the development, deployment, and management of AI-enhanced communication across multiple channels.

Whereas a small business conversational AI platform may focus on more streamlined functionalities that are easier to implement and maintain. They would look to provide automation and customer interaction capabilities without the need for extensive IT resources.

By integrating conversational AI, businesses can:

  • Handle a large number of customer queries simultaneously
  • Improve response times
  • Scale the personalisation of conversations

And as a result of all this increase customer satisfaction and loyalty.

The benefits of conversational AI for consumer service businesses

Conversational AI platforms are transforming customer service. Because they have the power to make quality scalable. How? 

By enabling:

- Faster customer resolutions

With conversational AI, your business can interact with customers faster and more efficiently. This technology enables quicker responses, reducing both waiting and processing times, which in turn leads to a higher customer satisfaction. 

💡 For instance, a retail or e-commerce business can use conversational AI to handle frequent customer inquiries about product availability, pricing, and specifications instantly. Customers won't have to wait for their answers anymore.

- A streamlined multichannel approach

Conversational AI can help you on all channels, so it doesn't matter where your lead or customers would like to contact you. Your team will be always on top of conversations.

💡 For example, in the automotive industry, a conversational AI can jump in for smaller questions via the Live Chat, about when someone can expect their new car. Or AI can even help the Sales-team with jumping in on a new sales opportunity, by redirecting the lead straight to the right sales employee.

- Operational efficiency that leads to consistent quality

The automation that comes with conversational AI can significantly reduce the need for manual operations and for humans to do repetitive and tedious tasks. In turn, this lowers your operational costs related to labour and saves time. 

💡 For example, a hospitality business, such as a hotel, can implement a conversational AI to manage bookings and inquiries. This ensures that the very simple interactions is handled efficiently and consistently. Resulting in your team being able to put the finishing touches on complex customer questions, and delivering quality service.

- Personalised recommendations at scale

Conversational AI can analyse customer preferences and behaviours to provide personalised product or service recommendations. By leveraging data such as past purchases, browsing history, and demographic information, businesses can offer tailored suggestions that enhance the customer experience and increase sales.

💡 For instance, a travel agency can use conversational AI to recommend next trips based on a customer's preferences and previous trips, creating a more personalised experience.

- Proactive support and engagement

Conversational AI can proactively reach out to customers to offer assistance or gather feedback, improving overall customer satisfaction and loyalty. Through automated messages or proactive chat invitations, businesses can engage with customers at key touchpoints, such as after a purchase or when navigating their website. This proactive approach demonstrates attentiveness and care, helping to resolve issues before they escalate and fostering stronger relationships with customers.

💡 For example, a subscription-based service can use conversational AI to send personalised messages reminding customers to renew their subscriptions or offering assistance with account management, ensuring a smooth and seamless experience.

How do conversational AI platforms work?

Conversational AI platforms leverage sophisticated technologies such as natural language processing (NLP), foundation models, and machine learning to enable seamless interactions between humans and machines. 

Here’s a closer look at the mechanics behind conversational AI:

Training with large data sets

The training process involves large amounts of text and speech data. This data teaches the AI system how to understand and process human language by giving it data on various language patterns, accents, and dialects. Over time, as the system encounters more data and scenarios, it learns and adapts, continuously improving its ability to understand and respond.

Natural language processing (NLP)

NLP enables the AI to interpret human speech or text input. This technology breaks down language into smaller, manageable pieces, helping the AI to grasp grammar, context, and the intent behind words. Through NLP, conversational AI can understand queries and commands and generate responses that are relevant and grammatically correct.

Machine learning and foundation models

Machine learning algorithms help conversational AI systems to learn from past interactions and improve over time. Foundation models, which are trained on huge quantities of diverse data, provide a broad understanding of language and context. These models are then refined to specific tasks like customer service, making them better in their given roles.

What elements make a good conversational AI platform?

The integration of AI into customer service strategies is set to redefine the efficiency and effectiveness of consumer interactions. By 2025, customer service businesses that incorporate AI into their multichannel customer engagement platforms are expected to boost operational efficiency by 25%

This significant improvement is largely attributed to the sophisticated elements of conversational AI platforms, which:

  • Enhance conversational flows
  • Improve customer satisfaction
  • Streamline communications between businesses and their customers

The top 3 elements your conversational AI needs

1. Intelligent virtual assistants and agents

Virtual assistants and agents are key elements of any conversational AI platform. These tools are designed to mimic human agents by understanding and processing human language so that they can have meaningful conversations with your customers. 

They can handle a range of tasks such as:

  • Answering common questions regarding product details, pricing, and policies
  • Assisting customers with booking appointments or reserving products and services
  • Helping customers place orders, check order status, and manage returns and exchanges
  • Providing step-by-step assistance for troubleshooting common product or service issues
  • Gathering customer feedback to improve products and services and to enhance the customer experience
  • Offering customised product or service suggestions based on customer preferences and past interactions

🔎 How does it work? Check out HelpMate

2. Optimisation of conversational flows

Conversational flows are the predefined pathways that virtual assistants and agents follow to guide interactions towards a resolution. These flows should be carefully created to ensure that conversations are smooth and logical, mirroring natural human interactions as closely as possible. 

By optimising these flows, you can ensure that your virtual assistants are not only efficient but also provide a human-like and satisfying customer service experience.

3. Enhancing the efficiency of your team

While virtual assistants handle routine queries, your customer service team is freed up to manage more complex customer interactions that require empathy, negotiation, and deep problem-solving skills. This division of labour allows your team to focus on areas where they add the most value, thus improving overall customer service quality and efficiency.

What else can conversational AI do?

In addition to handling routine customer service tasks, conversational AI platforms could help you in ways that extend beyond direct customer interactions. 

  • Proactive customer engagement: initiating conversations based on customer behaviour or significant events, such as reminding customers about renewals or suggesting products during sales events.
  • Market research and analysis: using conversational data to gain insights into customer preferences, behaviours, and trends, which can influence your marketing strategies and product development.
  • Language translation: offering real-time translation services to help businesses engage with customers globally.
  • Sentiment analysis: analysing customer interactions to assess emotions and sentiments, so that you can then adjust your strategies and communications to better align with customer moods and preferences.

💡Future of work

Looking ahead, conversational AI is set to become a productive personal assistant that augments human capabilities within the workplace. It's not just about automating tasks but also about enhancing the skills and efficiency of teams. This includes optimising task-based processes and dramatically reducing the time required to find specific data or information.

The architecture of a conversational AI platform

The architecture of a conversational AI platform is strategically designed to handle the complexities of human communication. 

Here's a more in-depth look into the parts that make up these sophisticated systems:

- Natural language understanding (NLU)

At the core of conversational AI is NLU, which processes user inputs to grasp the underlying intent and context. This component uses advanced linguistic algorithms to analyse text or speech for keywords, phrases, and semantics, enabling the AI to understand the user's needs accurately.

- Dialogue management

Dialogue management is the control centre for conversation flow, making each interaction based on insights from NLU. It determines the next steps in a conversation, whether to seek clarification, provide an answer, or escalate the issue to a human. This component is essential for ensuring the interaction progresses logically.

- Natural language generation (NLG)

NLG is where conversational AI communicates back to the user. This technology takes the structured output from dialogue management and translates it into natural, coherent language. The sophistication of NLG directly impacts how natural and engaging the conversation feels to the user.

- Machine learning models

Machine learning models give conversational AI the ability to learn from past interactions and improve over time. These models are continually refined with large training datasets, which allows the system to improve its predictions and responses, adapting to new language uses and user behaviours.

- Integration layer

An often-overlooked but critical component is the integration layer, which connects the conversational AI platform with existing business systems and data repositories. This integration ensures that the AI can access the information it needs in real-time, so that it can give relevant responses that are also personalised based on user data.

How do businesses use their conversational AI platforms?

Conversational AI platforms are increasingly becoming a staple across various industries. 

  • Financial services: conversational AI is used for better customer service, detecting fraudulent activities, and delivering personalised banking advice.

For example, banks are using AI to answer customer queries about account details or assist in reporting lost cards.

  • Retail: retailers can use AI for customer support, offering product recommendations based on consumer behaviour, and managing inventory.

For example, an online store might use AI to suggest products to customers based on their browsing history and purchase patterns.

  • Hospitality: this industry uses conversational AI to manage reservations, handle customer inquiries, and provide personalised guest services.

For example, hotels might deploy AI to handle check-ins, provide information about amenities, and even take room service orders through a virtual assistant.

  • Energy and utilities: In the energy sector, conversational AI supports customer service operations, helps with the setup of services, and can help with troubleshooting common issues.

For example: utility companies may implement AI to assist customers with billing inquiries or to report outages and track repair status.

  • Automotive: Automotive businesses use AI to respond to customer inquiries, schedule services, and offer detailed product information.

For example, car dealerships might use AI to provide instant information on vehicle specs, availability, or book test drives.

Future trends of conversational AI

The future of conversational AI is set  to significantly influence customer engagement in the coming years. Here are the key trends that will shape the development and application of conversational AI.

1. Enhanced emotional comprehension

The demand for AI assistants that understand and respond to human emotions is increasing.  Customers now expect AI solutions to understand and react to their emotional states, a feature that is rapidly advancing. Currently, conversational AI is becoming more adept at interpreting emotions. This improvement is driven by advancements in large language models (LLMs) that boost the NLP capabilities, allowing AI to better interpret and respond to the emotional nuances in communications.

2. Conversational AI as a business standard

Conversational AI is rapidly becoming a fundamental customer engagement tool across various sectors. According to IBM statistics, 77% of businesses are either using or exploring AI solutions. 

The top five things that are stopping successful AI adoption for businesses are:

  • Limited AI skills, expertise or knowledge (34%) 
  • The price is too high (29%)
  • Lack of tools or platforms to develop models (25%),
  • Projects are too complex or difficult to integrate and scale (24%)
  • Too much data complexity (24%)

As AI evolves and these challenges are overcome, we will see that conversational AI becomes a standard in almost every business.  

3. Cross-technology integration

The integration of conversational AI with other technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR) is set to further change the customer experience. 

This mix of technologies will allow for more immersive interactions, where customers can use AR and VR to visualise products and receive personalised recommendations. Research from Threekit indicates that 71% of consumers would be more inclined to shop using AR interfaces.

4. Demand for transparency and privacy

As conversational AI becomes more popular, the need for transparency and privacy is becoming more critical. Consumers are increasingly demanding that their personal data be handled securely and with clear governance.

Businesses will need to establish new internal standards and regulations to ensure responsible use of AI tools. As conversational AI is implemented there will need to be a focus on user control over data sharing and secure data handling to build trust and ensure a broader user adoption.

Up your service game with conversational AI

Conversational AI is not just a fancy new technology; it is a transformative force in customer service. By automating and personalising interactions,it addresses the growing customer demand for fast, efficient, and empathetic communication. The ability of conversational AI to manage and respond to customer inquiries in real-time is invaluable. This capability not only improves operational efficiency but also significantly enhances the customer experience by providing consistent and personalised support.

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