Voice AI v/s Conversational AI: Are They the Same Thing?

Updated on May 19, 2025

If you’ve spent some time on the AI side of X lately, you would have been barraged by a vast array of terms:

1. AI agent
2. Voice AI
3. Conversational AI
4. Phone AI
5. ChatGPT

Usually, these conversations are not relevant to usual customer service workflows. But now that most customers expect instant and personalized messages, these AI agents are also becoming a constant part of customer service. 

These terms, “Voice AI” and “Conversational AI,” can be more confusing. When voice AI answers a call, it is having a conversation, and conversational AI platforms sometimes offer voice options. 

This article will do a small technical deep dive and explore these terms in particular. We’ll cover:

1. What is Voice AI?
2. What is Conversational AI?
3. What are the Differences Between Voice and Conversational AI?
4. Which AI Should You Use for Customer Service?

What is Voice AI?

Voice AI refers to machine learning products designed to understand and respond to human speech. It can talk like a human being and respond to your customers’ questions. Voice AI uses core technologies to process the incoming voice on the phone, translate it to text, and generate a human-like response. 

To do this, the AI agent uses three technologies:

1. Automatic Speech Recognition (ASR) – This technology captures your customer’s voice over a phone call, recognizes the words, and translates them into text. 
2. Natural Language Processes and Understanding (NLP & NLU) – Once you have the text, the NLP and MLU algorithms understand the meaning behind the text. This helps voice AI understand a customer’s underlying issues. 
3. Text-to-Speech (TTS) – After the AI agent has understood the meaning of the phone call, it responds with text. TTS algorithms then use that text to create a human-like voice to send to your customer over the phone. 

Before the recent wave of AI innovations, voice AI was limited to voice assistants like Alexa and Siri. With modern LLM technology, voice AI can be programmed to answer customer questions using your company’s knowledge bases and SOPs. Modern voice AI agents can also handle complex tasks like resolving tickets, order cancellations, and address updates during the call. 

Also, while answering a phone call might sound long, it occurs within seconds. So, when you start using voice AI for contact center automation, it helps you improve your average handling time and first contact resolution rates. 

But, if this is voice AI, what is conversational AI, and how does it work? Let’s tackle that in the next section. 

What is Conversational AI?

Now, conversational AI is a bit broader than voice AI. It refers to any AI technology that has been built to understand and respond to humans, whether it’s text or speech. This includes anything from chatbots to sophisticated AI agents that talk on the phone. 

This AI technology can talk like a human being and helps you interact with technology better:

1. Input Generation/Processing: This is how the system receives your customer’s query, which can be through typed text or spoken words. If the input is voice-based, Automatic Speech Recognition (ASR) converts it to text.
2. Natural Language Processing (NLP) & Natural Language Understanding (NLU): As with Voice AI, NLP and NLU are crucial. These technologies analyze the grammatical structure and semantics of the input (text or ASR-converted text) to decipher the user’s intent – what they are trying to achieve or discover.
3. Dialogue Management: This component is responsible for managing the flow of the conversation. Based on the understood intent and the interaction context, it decides what the AI should say or do next.
4. Natural Language Generation (NLG): Once a response is determined, NLG crafts a human-like reply in text form.
5. Machine Learning (ML): Underlying all of this is machine learning. ML algorithms allow Conversational AI systems to learn from vast amounts of data and improve their understanding and responses over time without being explicitly reprogrammed for every scenario.

You’ve likely interacted with Conversational AI in various forms. Some of the most common applications in customer service include:

• Website Chatbots: These are frequently used to answer FAQs, guide users through a site, or help with simple transactions.
• Messaging App Bots: AI integrated into platforms like Facebook Messenger or WhatsApp to provide automated customer support.
• Basic IVR Systems: Early voice-enabled menus allow callers to navigate options using simple commands.
• Virtual Assistants: Broader AI personalities like Siri, Alexa, or Google Assistant, which can perform tasks and answer questions based on conversational input.
• Automated Ticketing and Routing: Systems that can understand a customer’s issue (via text or basic voice) and automatically create a support ticket or route them to the appropriate department.

Conversational AI has become a go-to solution for businesses aiming to provide 24/7 support, automate routine tasks, and offer self-service options to customers. It’s designed to handle many interactions and is foundational to automated customer engagement strategies.

While many Conversational AI platforms can handle voice input by incorporating ASR and TTS, the key distinction we’ll explore next is how deeply they are optimized for the unique complexities and real-time demands of sophisticated voice-centric customer service, where the specialization of Voice AI truly comes into play.

What are the Differences Between Voice and Conversational AI?

While conversational AI is the workhorse behind all recent AI innovations, voice AI is more specialized for contact center automation. Their core differences are:

Feature/Aspect	Conversational AI	Voice AI (especially in advanced CS applications)
Primary Modality	Often text-first, it can include basic voice options.	Voice-first, explicitly engineered for spoken language.
Core Technologies	NLP, NLU, Dialogue Management, NLG, (often ASR & TTS as components).	ASR, NLP, NLU, Dialogue Management, TTS (all highly optimized for voice).
Real-time Processing	Can be asynchronous or turn-based (e.g., chatbots).	Optimized for synchronous, real-time analysis and response during a live call.
Depth of Understanding	Primarily focuses on what is said (intent extraction from text or simple voice).	Focuses on what is said, how it’s said (emotion, tone), and the evolving context of a live spoken conversation.
Key Specialized Capabilities	General intent recognition, FAQ answering, and basic task completion.	Advanced real-time speech recognition (handling accents, noise), sophisticated emotion detection & sentiment analysis, contextual memory for dynamic dialogue.
Typical Use Cases in CS/CX	Website chatbots, messaging bots, email automation, and simple IVR menus.	Real-time agent assist, advanced AI phone agents, complex IVR navigation, voice biometrics, rich call analytics.
Interaction Complexity Handled	Best for structured, predictable interactions; can struggle with conversational nuances.	Designed to handle more dynamic, nuanced, and lengthy spoken conversations effectively.
Main Goal	Automate dialogue across various (often text-based) channels.	Master and enhance live voice interactions for superior customer experience and operational efficiency in voice channels.
Impact on Phone Support	Can automate basic voice responses or route calls.	Aims to transform phone support through personalized interactions and real-time support for human agents.

While conversational AI is a part of voice AI, it is a much broader term. So, conversational AI platforms can automate many of the moving parts in your customer service workflow, and you will need voice AI when you automate your contact center. 

With this knowledge, we can decide which technology you should adopt for your customer service process. 

Which AI Should You Use for Customer Service?

So, when choosing between Voice AI and Conversational AI for your customer service, the good news is they aren’t mutually exclusive – in fact, they are often complementary. Conceptually, this looks like a Venn Diagram:

So, deciding “which AI to use” isn’t an either/or. Instead, it comes down to the specific customer service workflow you want to enhance or automate:

• For broad, multi-channel automation (text-heavy, simpler tasks): If you’re looking to deploy chatbots on your website, automate responses on messaging apps, handle email inquiries, or implement straightforward FAQ bots across various digital touchpoints, a broader Conversational AI platform is likely your starting point.
• For optimizing your phone channel (complex voice interactions, real-time needs): If your priority is to improve customer experience on voice calls, provide real-time assistance to your human agents, implement sophisticated AI phone agents that can handle nuanced conversations, or gain deep insights from call audio (like emotion detection), then investing in or ensuring your platform has strong Voice AI capabilities is crucial.

Most of the time, AI is used for customer service. For customer service, you might use a Conversational AI platform for your digital and text-based interactions, while employing specialized Voice AI solutions to transform your AI-powered call center and elevate the crucial experience over the phone. The key is to match the technology’s strengths to the specific demands of each customer service channel and workflow.

Conclusion

Voice AI and Conversational AI represent different points on the spectrum of artificial intelligence solutions for customer engagement. While conversational AI is a broader umbrella technology that handles multiple communication channels, voice AI optimizes voice-based interactions with sophisticated speech processing capabilities. 

Rather than choosing between them, forward-thinking businesses should consider implementing both technologies strategically—using Conversational AI for text-based touchpoints and Voice AI to transform phone-based customer service. 

If your business wants to create a full-feature AI strategy, you will likely use both these technologies together.. The future of customer service lies not in choosing one technology over the other, but in their thoughtful integration to create seamless experiences regardless of how customers choose to connect.

If you need help in using AI to automate your customer service workflows, talk to us!