What is conversational AI?

TL;DR

• Conversational AI processes speech or text to identify a user's intent, checks the request against your business data, and generates a relevant response in real time without relying on fixed scripts or decision trees.
• Businesses use conversational AI to resolve support tickets, qualify sales leads, book appointments, and recover dormant accounts.
• Look for platforms with low-latency responses, realistic voice quality, and enterprise-grade security controls. These determine whether a conversational AI agent feels natural to customers and can be trusted with real business interactions. 

Conversational AI is a type of artificial intelligence that enables machines to understand and respond to human language through voice or text.

Powered by multiple underlying technologies, including natural language processing (NLP), machine learning, and generative AI, conversational AI identifies the intent behind a user's words, remembers context throughout a conversation, and connects with business systems to resolve complex requests.

This technology is available in both voice and chat formats, each suited to different types of customer interactions. The table below breaks down how each works and where each tends to fit best.

With ElevenAgents, you can build an agent once and deploy it as both a voice and chat agent, so customers can interact however they feel most comfortable.

Interested in seeing what it’s like interacting with an AI agent? Try ElevenAgents' AI receptionist below.

How does conversational AI work?

Conversational AI brings several technologies together to enable natural-sounding, low-latency conversations. Here is how a voice interaction works from start to finish.

1. A customer calls your business and begins to speak. 
2. The system filters out background noise to isolate the caller’s voice.
3. The caller's speech is converted into text by a Speech to Text (STT) model, which is then passed to a large language model (LLM) for processing.
4. The LLM interprets what the customer said, assembles the conversation history, relevant documents, any available tool outputs, and the system prompt, then generates a response.
5. The response is passed through a Text to Speech (TTS) model and delivered in a preselected voice.
6. The agent pauses, listening for when the customer begins speaking again, and the exchange continues.

For text-based interactions, the process works in much the same way, just without the STT and TTS layers. The customer's message goes directly to the LLM for processing, and the response is returned as text - making the overall exchange faster and simpler, while relying on the same underlying intelligence.

The steps above reflect a straightforward interaction, but conversational AI is built to handle real conversations, which rarely go in a straight line. This includes interruptions, mid-conversation topic changes, and customers switching languages.

To handle all of this nuance, conversational AI relies on a series of underlying systems, all working together to allow for natural, intelligent conversations:

• LLMs: Processes what the user said, decides how to respond, and determines whether any tools or actions need to be triggered.
• RAG (Retrieval-Augmented Generation): Retrieves relevant documents from your own knowledge base to ground its answers in your business's content.
• STT (Speech to Text): Converts spoken audio into text so the LLM can process it. ElevenLabs uses Scribe, its own STT model, which transcribes audio in under 150 ms.
• TTS (Text to Speech): Converts the LLM's response back into spoken audio. ElevenLabs uses Eleven v3, its latest voice model, to deliver responses that sound natural rather than robotic.
• Turn-taking model: Detects when a user has finished speaking so the agent knows when to respond, making the conversation feel like a natural back-and-forth.
• Guardrails: Keeps the agent on script, compliant, and within the boundaries you set, regardless of where the conversation goes.
• VAD (Voice Activity Detection): Separates the primary speaker's audio from background noise, improving transcription accuracy and filtering out sounds that aren't part of the conversation.
• Voicemail detection: Identifies when a call has reached voicemail rather than a live person, so the agent can respond appropriately.

Across all of this, the goal is the same - responses that are fast, natural-sounding, and helpful enough that the customer never feels like they're talking to a machine.

What real-life use cases does conversational AI have?

Businesses can now use conversational AI for conversations that go beyond simple FAQ answers. With platforms like ElevenAgents, voice and chat agents can use approved knowledge, follow a defined workflow, and connect to existing tools like CRM, ticketing, payment, and telephony systems to move the conversation toward resolution.

The list below, while not exhaustive, gives you an idea of some of the ways conversational AI can be used.

This list is just a starting point. Outside of these common applications, businesses are also using conversational AI for things like employee training, internal helpdesks, and onboarding. New use cases continue to emerge as teams test voice and chat agents across more of their operations.

What benefits are companies seeing from adopting conversational AI?

The benefits of conversational AI are best understood through what it makes possible in practice. Across industries, businesses are using conversational AI to handle work that was previously too time-consuming, too repetitive, or too costly to scale. Here is a closer look at how that plays out in real-world scenarios.

Resolves customer support inquiries faster

High-volume support queues are a natural fit for conversational AI because many customer questions need fast and accurate answers. Conversational AI agents can identify the customer’s issue, answer from approved knowledge sources, and pass the conversation to a human representative when complex or sensitive cases are detected. 

Klarna shows what this looks like in customer support. It uses voice AI as first-line phone support for 35 million US customers, resolving queries up to ten times faster than traditional methods. 

Accelerates sales follow-up and lead qualification

Sales and business development teams use conversational AI to respond faster to inbound leads and keep outbound follow-up consistent. Agents can qualify inbound leads, ask screening questions, collect account details, and book meetings. For outbound workflows, agents can call prospects and log outcomes without losing conversation history. 

In mortgage lending, Better deploys an AI voice assistant to handle repetitive qualification calls, run live eligibility checks, and execute rate locks over the phone, doubling its lead-to-lock conversion rate. 

Automates high-volume outbound conversations

High-volume outbound conversations require consistency, clear records, and a reliable way to capture outcomes. This includes collections calls, payment reminders, and account reactivation. Agents can be used to securely authenticate callers, explain outstanding balances, deliver direct payment links, and log structured outcomes into internal accounting systems.

Razorpay uses outbound voice agents to re-engage dormant accounts and identify why they stopped transacting. By automating these win-back conversations, they've reached connection rates that match the performance of their human call centers.

Streamlines appointment scheduling and intake

Appointment scheduling and intake often involve repeated outreach, eligibility checks, and booking steps. Agents can proactively reach out to members, check eligibility, and schedule appointments directly over the phone or via chat. 

Everlywell uses multilingual voice agents to handle outreach for health screenings, resulting in 3.5x higher conversion rates among Spanish-speaking members compared to traditional automated phone systems. 

Reduces missed calls and improves front desk coverage

Businesses with phone-based reception needs use conversational AI to answer routine inbound calls and reduce missed inquiries. This includes clinics, local service providers, public offices, and other organizations where callers expect quick routing or basic information. Agents answer incoming lines, route callers to the correct department, take accurate messages, and handle after-hours appointment requests so customers get a faster response. 

The City of Midland, Texas, uses an AI "civic concierge" to handle overflow calls and provide instant, multilingual assistance to residents 24/7. 

What to look for in a conversational AI platform

Evaluate a conversational AI platform for production readiness, not just demo quality. A short test conversation can sound impressive, but real deployments need to handle customer variation, system integrations, compliance requirements, and updates over time.

Look for these capabilities when evaluating platforms:

• Voice quality and latency: Sounds natural and responds quickly enough to keep a live conversation moving. A robotic voice or delayed response can make customers lose trust early in the interaction.
• Language support: Detects and switches languages during a conversation while maintaining natural voice quality and accurate responses.
• Integration depth: Reads from and writes back to systems like your CRM, ticketing platform, telephony stack, scheduling tools, and payment systems.
• Security and compliance: Supports the certifications, privacy controls, and deployment requirements your industry needs, such as SOC 2, HIPAA, GDPR, PCI DSS, or regional data residency.
• Ease of deployment and iteration: Allows non-technical teams to update knowledge, adjust responses, and test changes without waiting on engineering for every edit.
• Support model: Offers responsive support during setup and after launch, especially when troubleshooting production behavior, scaling to a new market, or adding a new use case.
• Guardrails and testing: Lets teams define what the agent can say, what actions it can take, when it should escalate, and how conversations are tested before launch.
• Knowledge base controls: Grounds answers in approved company content and makes that content easy to update over time.

For technical teams, the orchestration engine is also worth evaluating because it determines how models, tools, workflows, and business rules work together during a conversation. 

How to create your first conversational AI

Building a conversational AI agent with ElevenAgents starts with the web platform or the API. Most agents can be up and running in under an hour, while more complex deployments - those involving in-depth integrations, approval workflows, or custom requirements - may take a few days.

Whether you're ready to build now or still figuring out the right approach, there are a few ways to get started. Talk to our sales team if you're planning a more demanding deployment and want help scoping it out, or get started on the platform today and have an agent running in minutes. If you want to see the process before diving in, this video walkthrough covers how to build your first agent step by step.

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