Conversational AI: What it is and how to use it for customer service

Conversational AI is rapidly transforming both how we interact with technology and how companies do business. According to a report by Allied Market Research, the conversational AI industry global market is projected to reach $32.62 billion by 2030.¹

The main reasons behind this growth are a sharp rise in demand for AI-based chatbot solutions and AI-powered services. As more companies look to improve customer interactions and support, conversational AI technologies are becoming increasingly appealing.

This article explores what conversational AI is, how it works, and its various applications in customer service. Find out the benefits and best practices for a conversational AI platform to enhance your support outcomes.

What is conversational AI?

Conversational AI is a technology that replicates human-like communication through text or voice inputs and outputs, aiming for natural dialogues with users. This type of artificial intelligence (AI) uses machine learning and natural language processing (NLP) to make human-machine interactions more intuitive.

How conversational AI works

Conversational AI is trained on datasets containing samples of both written and spoken human language to understand how people communicate. 

When a user initiates an interaction in a conversational AI platform, like a chatbot, the system applies natural language understanding to analyze the input. It then uses natural language generation to create a coherent response. 

As the AI engages in more conversations over time, it continuously learns and improves its ability to understand language nuances and maintain fluid dialogues.

Components of conversational AI

• Machine learning: A subset of artificial intelligence that relies on algorithms and data to enhance performance over time. Machine learning enables conversational AI systems to learn and improve from experience, making them more adept at recognizing patterns and making predictions as they process additional data.

• Natural language processing: A branch of AI focused on human-computer language interaction. It employs rule-based and machine learning models, enabling systems to process, understand, and generate human language in various applications, including translation, sentiment analysis, and speech recognition.

Conversational AI use cases

Conversational AI finds practical uses in several business applications ranging from customer service to human resources. Here are some examples: 

Customer support

For customer support, chatbots are one of the main applications of conversational AI. They’re able to greet users, answer common queries, and engage in natural, back-and-forth conversations that help and guide them. 

Conversational AI chatbots can ask follow-up questions, offer product guidance, and even route customers to the support team for more complex issues and questions. 

Intercom’s Fin is an example of AI for support. Our chatbot can instantly resolve up to 50% of customers' questions.

Marketing and sales

Conversational AI software is also widely used to drive sales and marketing strategies, from prospecting to closing. In this case, chatbots can act as a "virtual sales agent," engaging customers throughout the buyer’s journey in a highly personalized way.

Another example involves the integration of AI into the lead nurturing process. The technology excels at generating enhanced emails and content tailored to nurture prospects.

Human resources (HR)

Many human resources processes benefit from conversational AI. For example, the technology can streamline employee training, enhance onboarding procedures, and efficiently manage employee data updates.

Gartner reports that 81% of HR leaders have either explored or put AI solutions into action to make their organization's processes more efficient.²

Internet of Things (IoT) devices

Internet of Things devices – including mobile phones, tablets, and smartwatches that are connected to the internet – also use conversational AI and automated speech recognition to interact with end users. Some examples of IoT devices that benefit from conversational AI include applications like Amazon Alexa, Apple Siri, and Google Home.

What is an example of conversational AI?

Now that we've explored various use cases for conversational AI, it's important to emphasize its versatility. This is a technology that can be tailored to a diverse array of contexts and requirements. 

Here are a few examples of conversational AI platforms: 

1. Fin: Customer service

Fin is a conversational AI chatbot for customer service. Created by Intercom, it uses a mixture of models, including OpenAI's GPT-4, as well as Intercom's proprietary technologies. 

Our chatbot is capable of solving complex problems by providing safer and more accurate answers than other AI bots. It doesn’t require training – simply direct it to your Help Center or support content, and it's good to go. 

Fin accurately answers questions based on the provided material. Any issues that Fin cannot handle are passed directly to human support teams for resolution.

2. ChatGPT: Multipurpose

ChatGPT is the popular chatbot from OpenAI, powered by their language model Generative Pre-trained Transformers (GPT) – which is actually behind many conversational AI platforms today.

ChatGPT stands out due to its immense scale and diverse functionality. Its capacities cover a range of different tasks. For instance, it can help generate creative ideas, provide educational explanations, and engage in natural-sounding conversations about almost any topic.

3. Google Bard: Web search

Powered by the model PaLM 2, Bard is Google’s conversational AI chatbot with one important difference from the free version of ChatGPT. Bard sets itself apart by harnessing web information, offering responses with a convenient "Google it" option for source validation.

This feature proves invaluable for tasks such as researching recent events and summarizing online content. Bard can also search the web directly for images.

4. PI: Personal Assistant

PI is a chatbot designed to work as an empathetic personal AI assistant for everyday tasks. While it can also be multipurpose, PI has a unique human-centric approach, creating a truly conversational and engaging platform for users.

5. Character AI: Entertainment

Character AI is a conversational AI chatbot for those who want to have fun talking to different characters, or giving their platform multiple different roles to play. 

Users can interact with chatbots that simulate the personalities and speaking styles of real figures like Elon Musk or fictional characters like Harry Potter. 

And Character AI’s versatility extends even beyond that – the platform can transform into a multitude of different roles, from a creative writer to an English teacher or even a travel planner.

6. Snapchat My AI: Social Media

Powered by OpenAI’s GPT model, Snapchat My AI is good at generating interactive and entertaining discussions, making it ideal for casual and social engagements. 

The chatbot can answer questions, suggest gifts, help plan trips, and recommend dinner ideas as a friendly chat partner for all sorts of conversations.

What is conversational AI for customer service?

Conversational AI in customer service leverages AI tools to automate and improve customer interactions. It aims to provide faster, smoother, and more efficient support by covering common questions and enabling natural, free-flowing dialogues.

Benefits of a conversational AI platform

An AI platform for customer service delivers a range of benefits. Let's take a look at the main ones.

Available at all times

Conversational AI provides 24/7 support, ensuring customers receive assistance in real time. AI chatbots, like Intercom's Fin, deliver precise, business-specific answers, maintaining accuracy and personalization.

Faster customer support

AI efficiently handles backlog queries as soon as it starts running, and keeps the pace for incoming requests. When faced with complex queries, it gathers essential information upfront for swift resolution by support reps. 

In addition, support teams can streamline their communication using generative AI, allowing for quick sentence rephrasing, expansion, and tone adjustment with a simple click.

Generative AI is a type of artificial intelligence that can craft diverse kinds of content, such as text, images, videos, and computer code. It examines large amounts of data to produce different outputs that are closely similar to the original inputs.

Better customer experience

Conversational AI's availability and fast support enhance the overall customer experience. When customer support teams utilize the platform, clients enjoy quicker, more effortless issue resolution. More importantly, they always receive topical, trustworthy information for their queries.

Higher team productivity and efficiency

AI manages most common queries and smoothly transitions customers to human support reps for more complex ones. Research from the National Bureau of Economic Research shows that AI platforms boosted issue resolution by 14% per hour and cut time spent on handling questions by 9% in a company with 5,000 support agents.³

Reduced support costs

More efficiency, less costs. All of the benefits we’ve seen so far lessen the burden on support teams and reduce the associated costs. According to Intercom’s 2023 report, The State of AI in Customer Service, 60% of leaders expect to reduce support costs over the next five years by adopting AI.⁴

In fact, Gartner forecasts that conversational AI will reduce agent labor costs by $80 billion by 2026.⁵ It's important to note that cost reductions through AI does not necessarily mean downsizing support teams. Rather, automation aims to make personnel more efficient by enabling them to focus on higher-value tasks.

Data collection and analytics

A conversational AI chatbot for customer service can collect data – including name, email, order numbers, and previous issues – and then transfer the conversation to human reps who will have all the context required to support customers quickly and efficiently.

Another use case is related to data analysis. Conversational analytics is a valuable tool for data processing and reporting. The primary objective in conversational analytics is to extract actionable insights that improve customer experiences, elevate service quality, and provide managers with informed data for more effective decision-making.

How to use conversational AI in customer service

1. Consider your strategy

Before embracing a powerful new AI tool, it's crucial to consider how it can benefit your team and strategy. Customer service leaders should consider having a chat with their teams to define goals for introducing conversational AI.

Pinpoint the areas where a chatbot can provide the most value. Does your team spend a disproportionate amount of time answering repetitive, routine questions? That type of workload is better delegated to an intelligent chatbot. 

More complex issues, on the other hand, still require a human perspective. The goal isn't to replace people, but rather to free them from lower-level tasks so they can focus on more consequential, high-impact work.

By following a few key initial steps, you can fully leverage AI to boost your team's performance and efficiency.

2. Decide which support metrics to follow

Set clear expectations when introducing a new tool like conversational AI. Establish benchmarks and goals to measure success over the first week, month, and beyond.

Ideally, you’ll select key metrics that reflect the tool’s intended impact on your team. Whether it’s automated resolutions, average response time, customer satisfaction (CSAT), or deflection rate, choose metrics relevant to your goals.

Know your team's baseline performance in these areas so you can accurately gauge the bot's contributions. Start tracking results from day one of the rollout.

These are the metrics support leaders expect to change as a result of AI:

3. Optimize your knowledge base for AI

A trustworthy chatbot can quickly address common queries if it's given the right information to work with. The key is to allow the chatbot to use your knowledge base's content to build reliable responses that directly answer the customer's questions without them needing to read a complete support article.

That means you need to make sure you’re providing the chatbot with accurate information that you want to share. It’s always worth checking if there are gaps that need filling, or outdated information in need of review.

⚡ Check out this article for tips on optimizing your help content for an AI chatbot.

4. Keep open communication with your team

Rethinking processes in order to incorporate a conversational AI chatbot is exciting, but it also presents a significant challenge. Some team members may worry about how it impacts their roles.

That’s why early and transparent communication must be a priority from the start. Ensure you clearly convey all upcoming changes and keep your team well-informed. During the AI implementation process, the lines of communication should always be open. 

As your team members acclimate to the AI platform and learn to harness its multifaceted potential, they'll free up valuable time that was once filled with repetitive queries. 

This newly achieved bandwidth will allow staff members to explore more fulfilling roles within the customer support space, ultimately giving them the opportunity to make a more significant impact in their roles. 

🖥️ Register now to watch our recorded webinar on how to prepare your support team for AI.

5. Choose the right conversational AI platform

Selecting the ideal platform should be a straightforward process. First and foremost, an effective AI platform prioritizes ease of setup and management. This means zero coding hassles – just intuitive configurations and user-friendly interfaces.

When assessing a conversational AI platform, keep the following criteria in mind to help you make the most informed choice possible:

Setup time

Be aware that setup times for different platforms can vary. While some solutions, like Intercom's Fin, can be set up within minutes, others may require days or even weeks.

Data privacy and security

Understand the legal and privacy practices of your chosen AI chatbot – including how data is handled – to reassure customers that their information is secure. 

Control

Confirm that you have control over the chatbot. This involves ensuring that the platform’s responses are being drawn exclusively from the help content you’ve selected and managing the situations that trigger the chatbot to transfer a conversation to a support rep.

Pricing

When choosing an AI chatbot pricing model, prioritize one based on outcomes for better ROI. For example, with pricing models based on resolutions – which include Intercom’s – you pay only when customers receive satisfactory answers without needing human support. 

In contrast, pricing based on usage metrics may not reflect customer satisfaction accurately. 

Integration with your tech stack

Integrating an AI chatbot into your support team's existing setup should be a priority. The platform should complement your current workflows rather than complicating or impeding it. 

Some AI bots function as add-ons to your platform, while others are native to specific ones. If you're considering a platform switch for advanced AI capabilities, the chatbot should be compatible with various support platforms.

Reporting 

Consider the reporting capabilities required for your support team. A reporting system is essential for determining the ROI of your chatbot, providing insights into its impact on key metrics.

Customization

The ability to fine-tune and personalize the chatbot according to your specific business needs is crucial. A one-size-fits-all solution rarely fits any company perfectly.

If you want to know more, we highly recommend our AI chatbot Buyer’s Checklist. This will give you a better grasp of how to find the right conversational AI platform for your specific support needs.

6. Evaluate conversational AI performance

While metrics are useful, you should place equal emphasis on qualitative feedback from your team members and your own customers. They often have keen insights into how you can strengthen your processes.

Have an open dialogue with team leaders about the AI’s impact on their work. Getting candid answers will help ensure the chatbot genuinely helps teams, rather than just altering the nature of their routines and workflows.

Don't forget to dedicate time to assess your chatbot's performance from your customers' perspective. In one of our recent research studies, a participant perfectly summed that up after engaging with our chatbot, saying, “I would love for all the chatbots to be like this because I don’t need to speak to humans. I get the answer that I want and that’s it.”

What is the best conversational AI?

In the world of conversational AI solutions, you'll find countless options designed to meet various needs. The ideal choice often depends on your specific requirements. When it comes to AI for customer service, we do believe Fin is the best solution available.

We’ve built Fin to be unlike any other chatbot. Fin is trustworthy, controllable, and seamless:

• Fin offers answers exclusively sourced from your support content. When it can't find an answer to a customer question, it deftly hands the conversation over to a member of your team, ensuring accurate and reliable customer support.

• You have full control with Fin, deciding when it's the right time for that human touch. You can preview Fin before deploying it live, ensuring it aligns perfectly with your business needs and operations.

• Fin is a straightforward, plug-and-play solution. If you're an Intercom user, you'll find that Fin seamlessly integrates with the entire Intercom platform, respecting your current setup, automation, and workflows.

Be part of the conversational era for customer service. Start your free trial or ask for a demo today of our breakthrough AI chatbot powered by OpenAI.

Sources

1. Allied Market Research, Global Conversational AI Market 2021.

2. Gartner, AI in HR 2023.

3. National Bureau of Economic Research, Generative AI at Work. Study on the introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents.

4. Intercom, State of AI in Customer Service Report 2023. We surveyed 1,013 global customer service leaders and practitioners.

5. Gartner, Conversational AI press release 2022.