Conversational AI vs Chatbots: Understanding the Key Differences

Overview

Digital communication is rapidly evolving, and enterprise chatbots have emerged as indispensable tools for businesses aiming to streamline their operations and enhance customer experiences. As we navigate the complexities of the technological ecosystem in 2024, understanding the intricacies of these sophisticated conversational agents becomes crucial for organizations seeking to maintain a competitive edge.

In this article, we delve into the concept of enterprise chatbots‌, exploring their pivotal role in revolutionizing customer service, internal operations, and more. From the integration of chatbots with existing enterprise systems to practical use cases, this exploration aims to equip readers with a comprehensive understanding of how enterprise chatbots are reshaping the 2024 business landscape.

What is conversational AI?

Conversational AI technology enables machines to engage in human-like conversations with users. It integrates NLP, machine learning, and other technologies to interpret and respond to user prompts in a more conversational manner. By mimicking human interactions and conversations, AI systems aim to provide a more intuitive experience across various platforms, including chatbots, virtual assistants, and voice interfaces.

For instance, a customer service bot will utilize NLP to understand customers’ queries, such as "Where is my order?" or "Can you provide me with an update on my shipment?" The chatbot then accesses relevant data from the company's backend systems to retrieve accurate information about the order status. Using this data, it formulates a tailored response, offering real-time updates on the shipment's location, expected delivery date, or any other relevant details.

Key features of conversational AI

In order to engage in more human-like conversations, chatbots utilize many distinct features to analyze interactions and the sentiment behind messages. Through machine learning algorithms, bots only continue to improve their natural language understanding the more that they communicate with users.

Natural language processing/Machine learning

NLP enables conversational AI to comprehend various aspects of language, including syntax, semantics, and context. Through techniques like named entity recognition and language understanding models, these systems can decipher the meaning behind user queries, discern intent, and extract relevant information to formulate appropriate responses.

Chatbots are constantly refining their NLP capabilities through machine learning algorithms, which allow them to learn from large volumes of data to iteratively improve their communication. Supervised learning, unsupervised learning, and reinforcement learning all help conversational AI adapt its language models based on real-world usage and feedback loops.

Sentiment analysis

Sentiment analysis is used to analyze text and assess the emotions expressed, whether it's positive, negative, or neutral. By integrating sentiment analysis into their frameworks, conversational AI systems can better comprehend the emotional context of user queries and craft their responses accordingly.

For example, when a customer expresses dissatisfaction with a product or service, sentiment analysis can help a chatbot identify the negative sentiment conveyed in the message. In response, the bot can acknowledge the user's feelings empathetically and offer solutions to address their concerns. Conversely, when a user expresses satisfaction, the system can respond with positive reinforcement or gratitude.

Text to speech

Text-to-speech (TTS) converts written text into spoken language, allowing AI systems to communicate with users through natural-sounding speech. By leveraging TTS, conversational AI can deliver responses in a variety of voices and accents, mimicking the nuances of human speech and enhancing the overall user experience (UX).

Furthermore, TTS promotes accessibility by providing alternative communication channels for users with visual impairments or literacy challenges. By converting text-based content into speech, conversational AI systems can effectively convey information to users who may have difficulty comprehending written text. This democratization not only broadens the reach of conversational applications but also ensures that all users can communicate with AI-powered assistants. 

68.5% of companies now utilize TTS to enhance user accessibility, demonstrating an increasing commitment to inclusivity across today’s business landscape.

Contextual interpretation

In real-world communication, context plays a crucial role in shaping the meaning of words and phrases. Similarly, contextual interpretation in AI involves analyzing various contextual cues, such as previous dialogue exchanges, user preferences, and situational factors to derive the most appropriate interpretation of user input.

Consider a scenario where a customer is interacting with a chatbot to resolve an issue with a recent purchase. The user initially contacts the bot to inquire about the status of their order. After receiving an update, they express dissatisfaction with the delivery timeframe, mentioning that they need the item urgently for an upcoming event. The chatbot, equipped with contextual interpretation capabilities, recognizes the urgency conveyed by the customer's statement and adapts its response accordingly.

What are chatbots?

There are two main types of chatbots: rule-based and AI-based. 

Rule-based bots are generally more suitable for scenarios where inquiries are limited and well-defined, while AI-based bots may be a better fit for those seeking greater flexibility, scalability, and adaptability.

Rule-based

A rule-based chatbot operates on a set of predefined rules, which determine how they respond to user prompts. These rules usually involve matching keywords in the user's message to specified responses stored in a knowledge base. If the input doesn't match any of the prespecified criteria, the bot will either prompt the user for more information or offer a default response indicating it doesn't understand the query.

Rule-based chatbots are commonly used for handling FAQs or providing basic customer support in scenarios where inquiries don’t vary much. They’re relatively simple to set up and maintain, as they don’t require sophisticated machine learning algorithms or extensive training data. 

Ai-based

Alternatively, AI-based chatbots are more sophisticated, as they’re powered by NLP and machine learning. Unlike rule-based chatbots, AI-based bots are capable of interpreting user input in a more human-like manner. They’re able to analyze context, semantics, and user intent, enabling them to generate more contextually relevant responses.

Conversational AI chatbots utilize machine learning algorithms to process large volumes of data and identify patterns in user behavior. Through this dynamic process, they can adapt their responses based on feedback and real-world interactions. They may also integrate with external data sources and APIs to access up-to-date information and provide more personalized assistance.

Conversational AI vs. chatbots: key differences

Chatbots and conversational AI bots both serve as tools for automating interactions with users, but they differ in terms of specificity and functionality.

Chatbots are more specific in functionality

Chatbots typically focus on performing specific tasks within a narrow domain. For example, a bot in the banking sector might assist users with basic account inquiries, such as checking their balance, transferring funds, or locating nearby ATMs. Chatbots excel at providing quick solutions within their designated scope, but may struggle to handle more complex queries and requests.

When used optimally, chatbots offer a significant return on investment (ROI) – they can increase sales by 67% on average.

Conversational AI, on the other hand, is broader in functionality and aims to simulate more natural conversations across a wide range of domains. They can handle a greater variety of customer queries, adapt to different conversation styles, and learn from user interactions. For instance, virtual assistants like Siri, Alexa, or Google Assistant are capable of performing a diverse array of tasks like setting reminders, playing music, providing weather updates, and answering general questions.

Conversational AI provides a more dynamic service

Conversational AI systems can incorporate elements of personality into their interactions, promoting a stronger sense of connection. By equipping machines with human-like traits such as empathy, humor, or friendliness, these systems can create more engaging interactions with users. For example, a virtual assistant designed with a warm and conversational tone can establish a more personal relationship with users, leading to increased user satisfaction and customer loyalty.

Furthermore, conversational AI can leverage advanced design principles to create more reciprocal experiences for users. By incorporating elements such as rich media content, interactive prompts, and gamification mechanics, it can make interactions more engaging and entertaining. For instance, an AI-powered learning assistant might use interactive quizzes, multimedia content, and personalized feedback to facilitate a more dynamic learning experience for users.

Other key distinctions include:

• Integration: Chatbots are often standalone applications or integrated into specific platforms, while conversational AI can integrate with multiple systems, databases, and APIs to perform more complex tasks.

• Learning: Manual updates are typically required for chatbots to expand their capabilities, whereas conversational AI continuously learns from user interactions, feedback, and data to improve its performance without human intervention.

• Scalability: When faced with increasing complexity or volume of interactions, chatbots may struggle to scale effectively. Conversely, conversational AI is more capable of handling large volumes of interactions and extending its capacity to support evolving requirements.

• Personalization: Most chatbots are designed to provide generic responses based on predetermined rules, while conversational AI systems can personalize interactions based on user history, preferences, and context.

Chatbots vs. conversational AI: customer service examples'

Due to these variations, customer support interactions can play out quite differently depending on which software your business chooses to utilize.

Suppose a customer has an issue logging into their account for a software subscription they’ve recently purchased from a provider. The individual begins a conversation with a chatbot to resolve the issue, explaining the problem and requesting assistance. The bot will leverage predefined scripts and/or knowledge bases to run through possible solutions. If the user simply requires a password reset or a link to a troubleshooting guide, the chatbot may be able to rectify the issue entirely by itself. But, if the problem is overly complex or the customer is using unclear language, it may need to escalate the ticket to a live representative.

Conversational AI offers a greater capacity to handle nuanced requests, while also comprehending slang and context. Also, when integrated with search engines, it’s able to perform web searches based on user queries, expanding its scope far beyond prespecified scripts and knowledge repositories. Thus, if the customer’s issue is more complicated than a simple password reset, conversational AI possesses the ability to run through several other possible solutions before escalating to a human agent.

Use cases for Conversational AI & chatbots

Conversational AI and chatbots have become so sophisticated and standardized that many of us don’t even actively consider that we’re interacting with machines anymore. Let’s take a look at two popular examples of these systems to see how users might interact with them in other live scenarios.

Conversational AI: Siri

Siri is a virtual assistant that’s powered by conversational AI; it was introduced by Apple as a feature of its iOS operating system in October 2011. Siri is designed to provide users with voice-activated assistance for a wide range of queries on Apple devices.

Nearly 98% of smartphone users have tried Siri at least once before, demonstrating users’ increasing willingness to interact with conversational AI systems.

When used as a customer service tool, a user may prompt Siri to find a specific product at an affordable price. 

For example, the user might activate Siri on their iPhone and say, "Hey Siri, I'm looking for a new laptop within my budget. Can you help me find one at an affordable price?" Siri would then interpret the query and initiate a dialogue to gather more information about the user's needs and budget constraints.

Siri may respond by asking follow-up questions to refine the search criteria, such as the user's preferred brand, specifications, or desired features. It might inquire, "What specific features are you looking for in a laptop, and do you have any preferred brands?" 

Based on the user's reply, Siri would dynamically generate search queries and retrieve relevant product listings from online retailers. It’ll then present a curated list of affordable laptops that meet the user’s criteria, along with pricing information, product reviews, and purchasing options.

Chatbot: Amazon

Amazon's Virtual Customer Service chatbot is designed to assist customers with common inquiries and issues related to Amazon products and services. It was introduced in 2017 and provides immediate support across various channels, including the Amazon website and mobile app.

Consider a scenario in which a customer has recently placed an order for a new jacket through Amazon, but hasn't received any shipping updates and is curious about the delivery timeline. 

The user accesses the VCS chatbot through the Amazon website or mobile app and initiates the conversation by typing a message such as, "Hello, I'm checking on the status of my order. Can you provide me with an update?" 

The VCS chatbot quickly interprets the customer's inquiry and prompts them to provide the order number or other relevant details to retrieve the necessary information.

Once the customer provides the required information, the VCS chatbot gathers the order details from Amazon's database and offers the customer real-time updates. If there are any issues with the order, the bot may offer assistance, such as initiating a refund or contacting the carrier for further information.

Why companies are switching to conversational AI

Firstly, conversational AI offers unparalleled opportunities to enhance customer engagement. 

It enables companies to provide instant, personalized support to users across whichever channels they’re utilizing. These conversational interfaces can handle a wide range of inquiries, thereby improving response times, reducing customer effort, and ultimately enhancing customer satisfaction.

Additionally, AI can help automate routine processes, freeing up human agents for more strategic activities. 

By delegating repetitive tasks such as answering FAQs, scheduling appointments, or processing orders to AI-powered chatbots, businesses can streamline operations, reduce costs, and improve overall productivity. This automation not only accelerates workflows but also minimizes errors.

Even more, conversational AI enables companies to stay ahead of customer preferences and market trends by offering scalable solutions. 

With the ability to continuously learn from its interactions, AI systems can quickly adapt to ever-changing organizational needs. This agility allows companies to deliver more responsive offerings, seize new opportunities, and maintain a competitive edge in the fast-paced business landscape.

The best time for your business to adopt conversational AI was yesterday and the second best time is today – 84% of marketers worldwide utilized it as part of their digital strategy in 2020, up from just 29% in 2018.

How Freshworks can help your business leverage conversational AI and chatbots for better customer service

Freshworks’ Freshdesk Omni includes our Freshchat conversational AI interface, offering the most extensive chatbot capabilities of any software on the market. Powered by Freddy AI, Freshworks’ bots are fast, versatile, and accurate, offering intelligent automation, polyglot capabilities, and integration with most popular third-party applications. Utilize their omni-channel potential to engage in meaningful conversations on your customers’ preferred platforms, whether that be your website, mobile app, messenger applications, or elsewhere.

One of our satisfied clients, Gregor G., praises our bots’ flexibility and ease-of-use, saying, “It integrates with all major chat aps [sic] from Facebook Messenger and Whatsapp... No matter the app that is plugged in the chat offers full functionality. A chatbot can run on WhatsApp, Messenger, or a website-placed chatbot. It is fairly easy to use and create chatbot conversational flows and transform data with functions that are supported.”