Chatbots Vs Conversational AI - What's the Difference?

Conversational AI is becoming a popular technology for businesses looking to automate customer interactions. The global chatbot market is expected to grow to $10.5 billion by 2026 as more companies adopt conversational agents. However, there is often confusion about the difference between a chatbot and conversational AI.

While chatbots and conversational AI can both understand language and respond through natural conversations, conversational AI delivers more advanced capabilities. Chatbots follow predefined scripts and rules, allowing limited flexibility based on the scope of their training data. In contrast, conversational AI leverages machine learning to handle more complex interactions and continue conversations contextually with some human-like capabilities. Conversational AI can understand intents, emotions, and relationships between conversations, enabling more meaningful, impactful dialogues.

As businesses consider leveraging automated conversational technology, it’s important to understand these core distinctions. Although chatbots serve purposes like basic customer service, choosing an advanced conversational AI solution brings greater possibilities for smoothing and personalizing interactions. Let’s take a look at chatbots vs conversational AI.

1What is a Chatbot?

A chatbot is an artificial intelligence-powered piece of software designed to simulate human-like conversations through text chats or voice commands. Chatbots interact with users and assist them with tasks like answering frequently asked questions, performing simple service requests, scheduling appointments, or even providing entertainment in a conversational format.

The main types of chatbots are rule-based chatbots and AI chatbots:

Rule-Based Chatbots

Rule-based chatbots rely on a set of coded rules to match user inputs to predefined conversational pathways and responses. They extract keywords and phrases from user messages and then pull the appropriate predefined scripts to construct seemingly natural replies.

The benefits of rule-based chatbots include faster, more consistent response times and easier quality control. Additionally, they perform well handling common repetitive inquiries within limited domains like customer service FAQs. However, they lack the flexibility to handle complex questions or continue conversations contextually.

Here are the main features of rule-based chatbots:

AI Chatbots

AI chatbots incorporate artificial intelligence to deliver more dynamic conversations. They apply natural language processing (NLP) to understand full sentences and paragraphs rather than just keywords. By leveraging machine learning, they can expand their knowledge and handle increasingly complex interactions.

AI chatbots possess greater versatility in responding appropriately across a wide range of potential conversational pathways. Their capabilities provide a lifelike bot experience with contextual responses, personalized recommendations, sentiment analysis, and more. However, AI chatbots require substantial data training and quality testing to achieve the desired sophistication.

Here are the main features of AI chatbots:

Rule-based chatbots follow coded rules for limited use cases, while AI chatbots leverage AI for advanced natural conversations supporting a broader domain. As chatbots vs conversational AI technology continues maturing, AI-powered solutions gain increasing preference for their capabilities providing more meaningful bot interactions.

2What is Conversational AI?

Conversational AI refers to advanced artificial intelligence systems that can engage in natural, meaningful dialogue with humans. Instead of just executing simple commands or providing static information like more basic chatbots, conversational AI leverages machine learning and language processing to understand full contexts and respond dynamically with unique, intelligent responses.

The key goal of conversational AI is to simulate human-like conversation, identifying intents and entities to determine optimal responses on the fly. This allows for truly intuitive communication across a breadth of domains, powering everything from smart assistants like Siri and Alexa to specialized customer service chat agents.

Unlike rigid chatbot scripts, conversational AI algorithms continue to evolve and improve through ongoing machine learning, analyzing real dialogues to sharpen response relevance and mimic human logic patterns. With massive datasets and computational power, leading systems can address an incredible variety of conversational needs, replacing simplistic menu-based bots with advanced worlds of intelligent interaction powered by AI.

3Chatbot vs Conversational AI

While chatbots and conversational AI solutions are often used interchangeably, there are several key differences in their capabilities. Here are some of the conversational AI vs chatbot factors:

Understanding

The key distinction for conversational AI vs chatbot in capabilities stems from the level of understanding. Chatbots rely on keywords and preset rules, allowing only superficial understanding. Conversational AI uses advanced natural language processing to analyze complete sentence structure and paragraphs deeply to comprehend full contextual meaning.

Responses

Related to understanding, chatbots can only respond via their preset scripts and programmed rules, resulting in inflexibility. They cannot recognize or respond appropriately to questions that fall outside of these narrow sets of rules.

In contrast, the machine learning foundations behind conversational AI allow for vastly more versatile responses. By analyzing datasets of millions of conversational examples, the AI can learn to formulate new logical responses appropriately adapted to novel input questions.

Personalization

Chatbots have very restricted personalization capabilities, as they lack the contextual understanding of each user’s needs. Their personalization is limited to filling in data like names into predefined scripted responses.

However, conversational AI tracks context to deliver truly tailored responses. For example, understanding a customer's priorities from past conversations allows one to respond to a new question by referencing those priority areas first.

By tracking user profiles, conversation history, preferences, emotional state, location, and more, conversational AI can personalize each exchange to match the individual.

Use Cases

In terms of use cases of conversational AI vs chatbot, chatbots sufficiently serve limited single-turn information lookup queries, like FAQs and transactional requests. They follow decision trees in narrow domains with reasonably high accuracy.

But only conversational AI can facilitate complex multi-turn conversations spanning a breadth of potential topics. It enables real natural dialogue without strict domain or question-type limitations. Conversational AI provides users with an engaging experience like chatting with a human.

Improvement

Lastly, chatbots have largely static understanding and responses. Although rules can be added to expand their scope, it requires ongoing manual coding work. In contrast, the machine learning foundations of conversational AI allow it to continuously self-improve through new conversation datasets. So, these were the main difference between chatbots vs conversational AI.

Without any human input needed, its performance automatically strengthens over time to handle new question types and conversation flows.

4How Chatbots Relate to Conversational AI

While conversational AI aims to truly understand conversations and users with context-aware machine learning models, chatbots pioneered early fundamental elements enabling natural language interactions.

The development of chatbots established building blocks like linguistic analysis, response templating, dialog frameworks, and integration methods that conversational AI solutions build upon in an expanded and enhanced way. So advancements in chatbot technology accelerated capabilities now seen in sophisticated conversational AI.

Though chatbots remain viable for narrow use cases, they can be considered a precursor to modern AI-powered conversational solutions. In some cases, combining chatbots that efficiently handle common simple questions with a conversational AI agent for complex interactions creates an optimal approach.

So, while conversational AI goes beyond chatbot capabilities, early chatbot innovations remain relevant in laying the groundwork and filling roles within AI assistant ecosystems. The evolution from basic chatbots continues progressing through advanced conversational AI systems.

5The Use Case of Chatbot and Conversational AI

Chatbots follow coded rules around limited use cases like FAQs and transactions. In contrast, conversational AI leverages machine learning on language and customer data to deliver flexible conversations, personalizing support across virtually any customer service scenario at scale.

The AI-powered solution can replace calls with a high degree of human touch for exceptional customer experiences.

Chatbot Use Cases in Customer Service

Chatbots serve more limited yet valuable use cases, including:

Conversational AI Use Cases in Customer Service

Conversational AI handles advanced use cases by conducting complex free-formed multi-turn conversations:

6The Benefits of Conversational AI

While basic chatbots provide limited capabilities constrained to simple flows, conversational AI unlocks truly productive automated experiences and broadened self-service capabilities.

Advanced algorithms empower conversational AI solutions to facilitate meaningful, naturally flowing multi-turn conversations spanning across an array of potential discussion threads.

Specifically, some of the valuable capabilities gained through AI-powered conversational platforms include:

Natural Language Processing

Conversational AI leverages much more advanced natural language processing techniques like morphological, grammatical, syntactic, and semantic analysis to deeply parse sentences. This allows accurate comprehension of anything ranging from casual chats to complex domain-specific questions without reliance on basic keywords.

Customers engage naturally without having to restrict their vocabulary or phrasing. Additionally, algorithms can continuously self-improve language processing through deep learning.

Contextual Understanding

A core limitation of chatbots is fragmented, isolated responses due to a lack of historical and profile awareness. However, conversational AI tracks identity, past interactions, preferences, sentiment, and more as persistent context.

This knowledge shapes responses to follow-up questions and allows recommendations tailored to what that specific customer cares about per previous chats. It enables coherent, logical multi-turn conversations instead of independent, disjointed single exchanges. Persistent context transforms effectiveness.

Improved User Experience

The fusion of language capabilities and context facilitates seamless, frictionless discussions emulating human interactions. There’s no need to reexplain background or redirect conversations since the AI handles open-ended multi-turn dialogues.

Customers feel heard and understood and receive deeply personalized guidance. These smoother, more satisfying automated experiences increase usage, containment rates, and customer loyalty in the long term.

Scalability

Bots maintain consistent throughput without wearing out or getting overwhelmed like human reps. Instantly scaling to handle 100 or 100,000 customers concurrently poses no capacity challenges. Help centers can reliably meet spikes from promotions or outages while reducing concerns of understaffing.

The parallel automated processing also frees up humans to focus on complex niche issues the AI routes them. This is paramount for cost efficiency at scale.

Integration Capabilities

Integrating the conversational layer with backend systems amplifies service quality and customization. Real-time access to customer order data, transaction history, entitlements, etc., allows the AI to provide precise responses and tailored recommendations vs generic guesses. Deeper personalization further solidifies productive experiences.

Some platforms even offer APIs to orchestrate intelligent workflows, kicking off relevant business events tied to conversation outcomes. This level of customization suits more intricate use cases.

7Examples of Conversational AI Chatbot

As these solutions demonstrate, conversational AI applies across sectors for natural discussions that accomplish business goals from sales to service. Continual advances in language processing and machine learning further expand possibilities for assisting customers conversationally.

H&M

H&M implemented a conversational AI-powered chatbot to engage customers and guide them in selecting outfit options from the fashion retailer’s extensive catalog. The natural conversations create an easy, enjoyable shopper experience that builds loyalty and sales.

Domino

Domino's chatbot lets customers place delivery orders through popular messaging apps using natural voice or text conversations. By integrating the conversational interface within messaging platforms customers already use daily, ordering is extremely convenient without phone calls or complex apps.

HDFC Bank

HDFC Bank created “EVA", an AI-powered banking chatbot that assists customers with various transactions and inquiries. EVA uses natural language processing to comprehend requests and analyze sentiment to strengthen connections, with continuous learning to improve over time. It handles millions of customer interactions, reducing calls to human agents significantly.

KLM Royal Dutch Airlines

KLM Royal Dutch Airlines introduced the AI chatbot “BB” to simplify travel-related conversations. Available 24/7 in multiple languages, BB provides flight information, reservation assistance, and customer support through natural dialogue. As it handles hundreds of thousands of passenger queries, BB drives operational efficiencies.

IBM Watson

IBM Watson Assistant helps enterprises deploy conversational interfaces, understand the true intents behind inquiries, and guide users through even complex topics naturally. It learns unique terminology and workflows to optimize assistance across industries from banking to healthcare. All within highly secure and scalable enterprise environments to drive omnichannel customer satisfaction.

Babylon Health

Babylon Health built an AI-powered chatbot focused on healthcare conversations, allowing people to describe their symptoms before providing suggestions around possible conditions and next steps to discuss with doctors. The medically trained solution can identify risks early and guide patients through vital health decisions and difficult diagnoses using empathetic dialogues.

8The Future of Chatbots vs. Conversational AI

While chatbots remain viable for niche basic conversations, conversational AI continues advancing to power more meaningful and productive dialogues. As language processing and machine learning models mature, conversational AI will take on increasingly complex use cases with greater personalization and automation capacities.

Gartner predicts that by 2025, 50% of medium and large enterprises will have deployed conversational AI chatbots, up from less than 2% in 2020. The global conversational AI market is forecasted to grow from $4.2 billion in 2019 to $15.7 billion by 2024.

As one example, ChatInsight offers an AI-powered chatbot leveraging advanced natural language capabilities that learn from custom-uploaded training data. This allows it to understand intents and maintain context across conversations spanning from IT support to customer service and more.

The human-like bot provides 24/7 availability to address frequent questions or routine task conversations, freeing teams to focus on higher-level work.

Over the next decade, chatbots will continue handling basic repetitive queries while machine learning propels conversational AI abilities to facilitate increasingly complex multi-turn natural dialogues and integrate deeper into business operations.

Blending chatbots’ efficiency for simple use cases with conversational AI’s versatility around advanced engagement empowers businesses to sustain exceptional automated experiences.

FAQs

1. Chatbot vs. Conversational AI – Which is best for your business?

Conversational AI is generally more advanced and beneficial for most businesses rather than a basic chatbot. Conversational AI delivers greater personalization, resolving customer issues faster and even handling complex needs a chatbot couldn’t address.

The intelligent capabilities amplify customer satisfaction and may deliver ROI gains through conversion rate optimization. However, conversational AI also requires greater initial development investments.

Evaluate your precise needs and priorities regarding capability, budget, and use case before deciding which is optimal.

2. What are the types of conversational AI?

There are three main types of conversational AI:

3. How Chatbots are impacting conversational AI?

While earlier chatbots followed simple conversational scripts, they set the stage for more advanced AI systems focused on natural language processing. The mass adoption of these limited bots revealed consumer demand for intuitive conversational interfaces. This fueled intense innovation in the AI underpinning more contextual, dynamic dialogue.

Modern conversational AI leverages massive datasets and neural networks to understand words in relationship to full meanings and respond appropriately. Unlike rigid chatbots, leading systems display logic, personalization, and versatility surpassing human staff at times.

In essence, the chatbot revolution demonstrated the substantial value conversational AI can provide across industries from customer service to entertainment. Although basic chatbots remain limited, they inspired machine learning breakthroughs empowering AI to master human-like dialogue at scale today.

4. How much does conversational AI cost?

Adopting conversational AI necessitates upfront investments in design and development costs. The total expenditure varies enormously based on the system’s complexity and the degree of customization needed for specific use cases. Elaborate AI with personalized functionality requires more extensive natural language modeling - demand that commands higher price tags.

Entry-level chatbot solutions might run less than $10 per month, while robust, tailored enterprise applications could demand millions in initial investments plus ongoing costs. Most solutions fall between, with totals generally scaling up in proportion to factors like platform capabilities, data requirements, and continuous improvement needs.

Maintenance, monitoring and iterative upgrades also enable sophistication over time - but raise lifetime costs. Evaluating particular features against business priorities and potential ROI can clarify sensible budgets.