Chatbots vs Conversational AI: Understanding the Differences

From customer support to digital engagement and the online buying journey, AI solutions can transform the customer experience.

For businesses, AI-enhanced customer service can yield significant efficiency gains and slash operational costs.

But, with all the hype and buzzwords out there, it can be hard to figure out what various AI technologies actually do and the differences between them.

Take 'chatbots' and 'conversational AI' as examples.

These terms are often confused or used interchangeably. However, although there is overlap, they are distinct technologies with varying capabilities.

So, if you’re struggling to cut through the jargon and understand the difference between these systems, never fear - you’ve come to the right place.

In this article, we’ll provide the low-down on chatbots vs conversational AI - empowering you to choose the right AI technology for your business needs and goals.

We’ll cover:

• Defining chatbots and conversational AI technologies
• The difference between chatbots and conversational AI systems
• Key use cases for conversational AI chatbots
• Choosing the right solution for your business
• Best practices for implementing chatbots and conversational AI software

TL;DR

Conversational AI is an umbrella term for various technologies that aim to facilitate human-like communication between humans and machines. 

Chatbots are a specific application of conversational AI, typically used to automate interactions and tasks in the context of digital customer service.

Key use cases for chatbots and conversational AI technology:

• Digital customer support & automated customer service
• Online sales & lead generation
• Streamlining internal operations

Best practices for implementing/using chatbots and conversational AI:

1. Assess objectives and requirements before evaluating platforms/providers
2. Design user-centric customer experiences
3. Combine conversational AI with an intent-based designed for a hybrid chatbot approach
4. Ensure seamless transitions between bots and human agents
5. Continuously monitor and improve performance

Understanding chatbots and conversational AI

Chatbots and conversational AI are related concepts that differ in their scope and capabilities.

Conversational AI refers to a broad set of technologies that aim to create natural and intelligent communication between humans and machines.

It encompasses various forms of artificial intelligence such as natural language processing (NLP), generative AI (GenAI), Large Language Models (LLMs), and machine learning (ML).

Conversational AI tools are designed to understand, interpret, and respond to human language in a contextually aware and flexible manner.

They can handle more complex inputs, adapt to user preferences/behaviours over time, generate original content, and even learn from past interactions to improve future responses.

Chatbots, on the other hand, are a specific application of conversational AI focused on simulating back-and-forth conversations with human users.

These bots are usually programmed to interact with users through textual methods, typically in the form of messaging interfaces. They can be AI-powered, rule/intent-based, or a combination of the two.

While chatbots can utilise conversational AI techniques (e.g. natural language processing, GenAI) to understand and respond to user inputs, their responses are often based on predetermined paths plus the datasets they’re trained on.

Chatbots are generally used for digital customer support to provide users with certain information and automate specific interactions/tasks.

In summary, conversational AI is a broad umbrella term. It encompasses various technologies that enable sophisticated outputs and communication between humans and machines.

Chatbots, on the other hand, represent a specific application of conversational AI, typically designed to simulate conversation in the context of automated customer service.

They are often rule-based but can also incorporate AI technologies (e.g. Natural Language Processing, generative AI) and act as virtual agents, providing a more humanised customer experience.

Differences between chatbots and conversational AI

The main difference between chatbots and conversational AI tools is how advanced they are in their abilities and how complex their underlying operations are.

While basic chatbots follow pre-set rules or decision trees, conversational AI leverages advanced NLP  and machine learning for more sophisticated and advanced interactions. 

The most up-to-date conversational AI solutions also leverage powerful LLMs and generative AI to provide fluid conversational experiences.

This creates a more immersive and engaging user experience by interpreting context, understanding user intent, and generating intelligent responses.

So, in short, conversational AI chatbots and virtual assistants can engage in complex interactions, making the user experience more enjoyable and human-like. 

More traditional chatbots, on the other hand, use scripted responses and often provide a more “bot-like” conversation.

But that doesn’t mean that intent and rule-based chatbots are completely redundant.

By integrating intent or rule-based chatbots with conversational AI, businesses can optimise their digital customer experience and get the best of both technologies.

Still, to achieve the best results, there are some more intricate differences to bear in mind between how chatbots and AI work.

Let’s break them down in more detail. 

1. Rule-based chatbots

The majority of basic chatbots operate using a structured decision-tree framework.

These rule-based chatbots are designed with predetermined parameters and conditions, often necessitating users to use specific keywords or phrases in their inputs.

A decision tree system consists of a hierarchical arrangement where each node denotes a decision point, and the branches offer potential responses based on user input or system variables.

Throughout an interaction, a rule-based chatbot assesses user messages against its rule set, progressing through the decision tree to determine the most appropriate response.

As customers provide information or pose queries, the chatbot navigates through the tree, adhering to the rules specified for each scenario.

Rule-based chatbots are particularly well-suited for specific and narrowly defined scenarios, making them a useful and cost-effective solution for answering FAQs.

However, they’re very limited in their scope and functionality. So, if you want a chatbot that can automate more complex tasks and interactions, you’ll want to incorporate AI technologies, too.

2. Natural Language Processing & Natural Language Understanding

The ability of a conversational AI tool to comprehend and process language has significantly improved AI chatbots.

Natural Language Processing (NLP) enables a computer system to interpret and understand user input by extracting intents and entities.

This enables the AI to comprehend user requests accurately, no matter how complex.

Conversational AI utilises a range of Natural Language Processing (NLP) techniques, such as tokenization, part-of-speech tagging, and syntactic parsing, to process the subtleties of natural language within a vast array of data.

When rule-based chatbots are enhanced with NLP/NLU, they can go beyond their predefined scripts and respond to a broader range of inputs.

3. Generative AI & LLMs

NLP isn’t the only conversational AI technology that can be incorporated into a chatbot.

Generative AI and Large Language Models (LLMs) take the sophistication of AI chatbots to a whole new level - allowing them to produce complex and flexible responses that are almost akin to what a human might say.

One of the ways they achieve this is through integration with an AI knowledge base. 

For instance, with Talkative’s GenAI Chatbot, you can import URLs from your company website or file-based content into your own knowledge bases.

Then, when a customer asks a question, the bot will look for the answer in your knowledge base and produce a response using the relevant information plus the power of LLM/generative AI.

Overall, incorporating Generative AI and LLMs into a chatbot elevates its intelligence and conversational capabilities, allowing it to act as an expert virtual advisor for your customers.

Key use cases for conversational AI chatbots

Many businesses across all industries currently use conversational AI and/or chatbot solutions.

In fact, a recent estimate claims that the worldwide conversational AI market was around $5 billion in 2020 and is projected to reach $14 billion by 2025.

What’s more, according to Google Trends, interest in chatbots has grown ~4x over the past 10 years.

From improving efficiency to streamlining customer conversations, these AI tools are clearly causing significant changes in the business landscape.

Below, we’ll explore two key use cases for conversational AI chatbots.

1. Customer service & support

Conversational AI chatbots have brought about a revolution in customer service and support.

They enable customer service operations to function 24/7, improving response times and overall efficiency. This round-the-clock availability is particularly beneficial for businesses operating across multiple time zones.

AI-powered bots can automate a huge range of customer service interactions and tasks. In fact, some studies have found they can automate up to 80% of queries independently, reducing support costs by around 30%.

As a first line of support, chatbots supplement human agents during peak periods and offload repetitive questions - leaving your support teams with more time for complex cases.

They also offer self-service capabilities for customers, leading to increased customer satisfaction and a reduced volume of tickets requiring human intervention.

2. Sales & marketing

A conversational AI chatbot can also play a crucial role in increasing online sales and optimising marketing efforts.

They achieve this by helping with:

• Lead generation: Chatbots can digitally engage website visitors in interactive conversations, asking qualifying questions to identify potential leads. By collecting relevant information about prospects’ needs and preferences, they can assist in segmenting leads for targeted marketing campaigns.
• Personalisation: AI chatbots can analyse customer data and question them about their preferences/requirements. This information can then be used to offer personalised product recommendations and advice, increasing the likelihood of a conversion.
• Automated shopping assistance: Chatbots can aid your online sales efforts by providing customers with relevant information and guiding them through the purchasing process. They can retrieve product details, provide pricing information, and answer common purchasing queries.

By leveraging an AI chatbot to aid your sales and marketing efforts, you can streamline customer interactions, capture more leads, and increase conversions.

3. Internal operations

Beyond customer service and sales, chatbots and AI can also help with internal operations.

By automating workflows and providing simultaneous assistance to multiple users, they can free employees from repetitive tasks.

Some ways chatbots can enhance internal operations include:

• Employee onboarding and training: Chatbots can streamline the onboarding process by providing new employees with essential information, policies, and training materials in a conversational format.
• HR support: Chatbots can assist HR departments by handling routine queries from employees related to benefits, policies, and procedures. They can also provide self-service options for tasks like requesting time off, updating personal information, or accessing payroll details.
• IT helpdesk: Chatbots can offer internal support for IT-related issues by troubleshooting common technical problems and providing step-by-step guidance for resolution.
• Workflow management: Integrated with workflow management systems, AI systems can automate repetitive tasks and streamline internal processes.

These capabilities empower employees with self-service and allow various departments to focus on more critical tasks, boosting operational efficiency.

Choosing the right solution for your business

With a plethora of chatbots and AI platforms on offer, finding the right one for your business can be tricky.

In this section, we’ll explore the key things to bear in mind when choosing a chatbot or conversational AI tool.

Assessing your objectives & requirements

The process of finding the right chatbot or conversation AI system begins with deciding your objectives and requirements.

This includes understanding the purpose of the chatbot and how it can improve your current solutions and processes.

In simple terms, what do you want the chatbot or conversational AI solution to do for your business and your customers?

If you want an intelligent virtual assistant that can deliver the most advanced automated support in a humanised way - a chatbot powered by conversational AI technologies (NLP, GenAI, LLMs, etc.) is the best choice.

But, if you just want to reduce workloads for your customer support teams in a cost-effective way, intent or rule-based chatbots might be a viable option.

Either way, it’s important to ensure that the solution you choose aligns with your specific business needs and customer service goals.

You also need to think ahead. Long-term goals must be established prior to implementation to ensure your chatbot/conversational AI initiatives align with your overarching business strategy.

The preferences and behaviours of your target audience should also be considered to ensure that your chosen solution meets their needs and expectations.

Evaluating platforms & providers

Once you’ve defined your goals and requirements for a chatbot or conversational AI solution, you can start researching providers.

Choosing the right platform or provider means considering a number of important factors, for example:

• Scalability: Assess the scalability of potential solutions, especially if your business anticipates growth or fluctuations in demand. The provider should be able to support increases in usage effectively.
• Integrations: Ensure that the chatbot/AI solution can integrate seamlessly with your existing communication channels, CRM software, and other relevant systems. Compatibility with multiple platforms (e.g. your website, app, and messaging channels) is also important.
• Security and compliance: Prioritise software that adheres to security standards and compliance requirements, especially if your business operates in regulated industries like healthcare or insurance. A provider’s privacy policy, access controls, data security, and compliance with GDPR are key considerations.
• Analytics: Look for a provider that offers robust analytics and reporting capabilities to track your bot’s performance and effectiveness. Access to AI-driven analytics such as AI-generated insights reports and interaction summaries are good things to look out for.
• Customer support: Evaluate the provider’s customer success offerings, including documentation and customer support services. A responsive support team can help address any issues or challenges that arise during implementation and operation.
• Cost: Compare the pricing structure of various providers and determine which ones align with your budget and expected ROI. Consider factors such as subscription fees, usage-based pricing, and any additional costs for customisation or premium features.
• Future roadmap: Assess any potential provider’s commitment to innovation and ongoing development of new features and capabilities. Choose a partner that is invested in staying at the forefront of AI technology and can adapt to evolving trends and consumer expectations.

By carefully evaluating these factors, businesses can make informed decisions when selecting a chatbot or conversational AI provider that best fits their needs and objectives.

Best practices for implementing chatbots and/or conversational AI

The process of implementing chatbots or conversational AI systems requires careful planning and execution.

Even advanced, AI-powered chatbots have limitations - so they must be implemented and used properly to succeed.

In this section, we’ll cover the key best practices for deploying and using a chatbot - whether you opt for a rule-based solution or a conversation AL system.

1. Design user-centric experiences

The design of your chatbot customer experience is crucial for long-term success.

To create better conversational experiences and maintain brand consistency, it’s important to match the AI’s personality with your brand’s tone and personalise the chatbot experience based on user research.

This involves understanding your target audience’s preferences, pain points, and communication style.

User-centric chatbot experiences should mimic real conversations, bringing human-like elements to chat interfaces and providing quick, relevant, and manageable responses.

Fallback scenarios are crucial for times when chatbots fail to understand user input, ensuring that users receive consistent and coherent responses throughout the interaction.

By building your chatbot experience around the user, you’ll make sure that it adds value to the customer experience and contributes positively to customer satisfaction.

2. Ensure seamless transitions between bots & human agents

It’s important to remember that chatbots are not a customer service cure-all.

Even with advanced, enterprise-level AI chatbots, there will still be cases that require human intervention.

So, it’s crucial that your chatbot can carry out seamless escalations to a human agent whenever necessary.

Case in point, 86% of consumers expect chatbots to always have an option to transfer to a live agent.

Neglect to offer this, and your customer experience and adoption rate will suffer - preventing you from gaining the increased efficiency and other benefits that automation can provide.

You’ll also risk annoying customers and damaging your brand image with poor customer service.

Thankfully, with platforms like Talkative, you can integrate a chatbot with your other customer contact channels - including live chat, web calling, video chat, and messaging.

By doing this, you’ll enable effortless transitions between them, creating a cohesive and seamless customer experience across all digital touchpoints.

3. Consider a hybrid chatbot approach

Hybrid chatbots combine elements of rule/intent-based and conversational AI models to utilise the strengths of each approach. 

These systems aim to provide a versatile and effective solution that can handle a broad spectrum of user interactions. 

Hybrid chatbots typically use predefined rules/intents for specific tasks but also incorporate AI technologies like LLMs and generative AI to expand their adaptability, capabilities, and natural language understanding.

This might mean that the bot uses a decision tree structure to answer customer FAQs but leverages AI when faced with more complex issues.

The combined approach to their design and programming makes hybrid chatbots an extremely versatile tool that can be easily scaled to handle diverse tasks and industry-specific requirements.

4. Continuously monitor & improve performance

For a chatbot to remain relevant and effective in the ever-evolving digital landscape, continuous improvement is crucial.

You can refine a chatbot’s performance over time with the following strategies:

• Metrics tracking: Identify the performance metrics and chatbot analytics that align with your business objectives. Key metrics for chatbot performance include- error and escalation rate, resolution rate, handling time, CSAT score, and abandonment rate.
• Customer feedback: Implement customer feedback tools, such as post-interaction feedback forms or ratings, within your insurance chatbot’s interface. Use this feedback to make targeted improvements to the chatbot’s training and capabilities.
• Quality assurance testing: Quality assurance involves testing your chatbot’s functionality, ensuring it operates seamlessly across different platforms and devices while maintaining consistency in performance. This process helps uncover any technical glitches, compatibility issues, or inconsistencies in responses.

The proactive maintenance and performance management of chatbots and AI systems helps ensure that they remain a help to your business and customers, not a hindrance.

The takeaway

A chatbot or conversational AI solution can be an invaluable tool for many businesses.

When used effectively and alongside human-powered support, these technologies can boost efficiency, cut costs, and enhance your customer service experience.

But for any chatbot or AI system to succeed, it needs to be powered by the right technology.

That’s where Talkative comes in.

Our platform provides a scalable and flexible chatbot solution that can be tailored to your specific business needs.

With Talkative, 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 chatbot with your own AI knowledge base to create virtual assistants that are experts in your brand, products, and services.
• Meet and serve customers across your website, app, and messaging channels.
• Seamlessly escalate to human agents when needed.
• Leverage AI-driven analytics and insights reporting.
• Build multiple chatbots in-house (if you prefer to take the wheel with bot design).

In addition to chatbots and conversational 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.