Conversational AI: The ultimate guide

Conversational artificial intelligence (AI) transforms the way you interact with technology, engage with customers, and manage information. Whether through chatbots or virtual assistants, this branch of AI can empower your businesses to reduce operational costs and gain valuable insights about your audience.
In this guide, let’s explore what conversational AI is and how it differs from generative AI. Discover use cases and learn how this technology can save you time and drive your enterprise AI strategy implementation.
What is conversational AI?
Conversational AI technology is a subset of artificial intelligence that enables computers to communicate with humans in a way that feels natural and similar to human interactions. It combines a set of technologies to analyze input, extract meaning, and formulate appropriate responses to users.

The core components of conversational artificial intelligence include:
- Natural language processing (NLP): As the foundation of this form of AI, NLP enables machines to understand and interpret human language by breaking it down into components such as words, phrases, and syntax
- Machine learning (ML): This technology allows the AI system to learn and improve over time by analyzing vast amounts of data, identifying patterns, and predicting user behavior
- Dialog management: This component handles the flow of the conversation by understanding user intent, tracking conversation progress, and determining appropriate responses
- Natural language generation (NLG): This process converts structured data into human-readable text or speech in response to user input
How conversational AI works
When combined in a conversational AI platform, this set of technologies responds to your prompts with insights. Here’s how:
- Input: The user typically initiates communication by typing, speaking, selecting options, or sharing visuals
- Processing: The conversational AI software uses NLP to understand the input, identify intent, and extract relevant data
- Response generation: Based on the processed information, the system generates a suitable response using NLG
- Output: The response is presented to the user as text, speech, images, or a combination of these elements — depending on the application’s capabilities
- Learning: The system analyzes the interaction and uses machine learning to improve future responses
Increasing integration of AI chatbots in messaging services is the main driver of this industry’s growth. The conversational AI market size is expected to expand from $13.2B in 2024 to $49.9B by 2030. But if you’re thinking about adopting one of these systems in your enterprise, there’s another type of AI you should get to know before choosing a platform: generative AI (also known as GenAI).
Discover what’s driving increased artificial intelligence adoption in enterprises.
Conversational AI vs. generative AI: What’s the difference?
Conversational AI and generative AI (also known as GenAI) are often intertwined in advanced applications, as they complement each other. While conversational AI systems interpret human language and manage dialogues, GenAI tools understand the context of your queries and provide a more detailed response, resulting in more engaging conversations.
Let’s take a look at the differences between both technologies.
Technology | Conversational AI | Generative AI |
---|---|---|
Definition | Enables interaction between machines and humans in a natural language, focusing on understanding and responding to dialogues | Generates answers and content based on given prompts, using advanced models to create contextually relevant responses |
Purpose | Facilitates structured and effective interactions between users and systems, such as answering questions or providing customer support | Develops diverse and contextually accurate responses, enabling more fluid and informative interactions |
Approach | Uses predefined rules and models to interpret and respond to user inputs, often relying on scripted interactions and decision trees | Uses large-scale neural networks to understand context and generate responses dynamically |
Applications |
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Applying generative AI to conversational AI tools helps reduce the use of predefined scripts. Based on the command and the conversation’s context, AI generates answers in text, speech, or other media formats.
The integration of GenAI is a game-changer for customer service, enabling people to interact with businesses in real-time conversations at any time. A Zendesk survey reveals that 69% of consumers expect to interact differently with chatbots and AI in the coming years.
Benefits of conversational artificial intelligence

You can deploy conversational AI to:
- Gather data-driven insights: Imagine a system where you can ask questions about documents or spreadsheets and find answers instantly. By integrating this AI into your cloud data storage, you can extract valuable information that helps you make informed decisions about products, operations, or processes.
- Enhance customer experiences: With a virtual agent, you can offer 24/7 support, eliminating long wait times and allowing clients to resolve issues independently. AI handles a massive amount of customer interactions in real time, empowering organizations to offer omnichannel support with more personalized conversations.
- Reduce costs: Gartner predicts that conversational AI’s role in contact centers will cut agent labor costs by a staggering $80B in 2026. In addition to instantly handling a higher volume of customer inquiries, AI enables your team to find information in seconds, significantly reducing the time spent on research.
- Increase productivity: By automating time-consuming tasks like summarizing or creating AI content, this technology streamlines workflows and boosts team productivity. For instance, you can ask an AI chatbot to transform a complex document into an easily digestible training material, adapting the content to suit beginner-level understanding.
Learn how to measure the impact of AI on your business.
Conversational AI: Examples and use cases
Customer support chatbots are the most popular application of enterprise conversational AI platforms, but these solutions can also be valuable for managing content and fostering collaborative workspaces across different industries.
Finance
A review by the Consumer Financial Protection Bureau shows that the top 10 largest commercial banks in the United States deploy chat assistants as a cost-efficient replacement for human customer service. As this technology has evolved, these banks are moving from simple, rule-based chatbots to AI-powered options. Consider the following use cases for financial services:
- Onboarding: New customers can use chatbots to understand account setup and verification processes
- Easy collaboration on financial documents: Agents can use AI to streamline the content review process of documents by extracting insights or clarifying their content.
- Market insights: Financial advisors can ask AI to summarize complex reports or analyze trends for clients, fostering transparency and alignment.
Retail
AI enhances both customer and employee experience in retail by providing personalized interactions and facilitating efficient problem resolution. Examples include:
- Virtual shopping assistants: Customers receive help finding products, getting recommendations, and completing purchases
- Customer service: Agents can easily integrate AI with their knowledge base management system to search content and personalize answers for clients
- Segmented materials: Marketing teams can use AI to adapt campaign messages to specific customer needs while keeping the content consistent across channels
Healthcare
Conversational AI is a valuable tool to foster collaboration in healthcare, as it allows teams to communicate and share information efficiently. Use cases within this industry include:
- Streamlined insurance insights: Individuals can ask AI-specific questions about their insurance policies to obtain fast and accurate answers based on the content
- Meeting agenda automation: AI generates agendas for internal meetings and patient consultations
- Information research: Healthcare services use AI assistants to retrieve medical research and clinical guidelines
Telecommunications
A study by IBM among telecommunication organizations shows that 97% of participants reported improved customer satisfaction after implementing conversational AI. This industry leverages AI in:
- Virtual assistants: Through voice-activated control, customers get assistance with services and technical issues
- Competitive analysis: AI can extract insights from industry trends and market research reports, helping teams stay informed about emerging technologies and market shifts

Integrate your favorite conversational AI solutions with Box
Box AI integrates both conversational and generative AI into a secure platform to create, organize, and share your information in one place. With the Intelligent Content Cloud, you transform your content strategy by extracting powerful insights from unstructured data.
Box empowers you to:
- Ask questions across multiple files in a Box Hub and get instant answers
- Draft documents, write emails, and create meeting agenda templates with Box Notes
- Easily summarize content, search key points, and extract information
- Analyze reports, meeting notes, and any other textual assets
- Extend AI capabilities to other platforms you use, such as Slack and Microsoft 365
- Have complete control of your use of artificial intelligence — our AI principles give you peace of mind that your data remains secure and compliant with industry regulations
Contact us and discover all you can do with enterprise conversational AI.

While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.