Real-World Examples of Conversational AI, From Smart Speakers to Phone Lines

Conversational AI is an intelligent technology that understands what a person is saying and the intent behind it, then responds naturally, ideally resolving the problem without involving a human agent.

Chances are good that you interact with some kind of conversational AI tool every day: Siri, Alexa, Claude, Google Assistant and ChatGPT are the most well-known conversational AI tools. On the business side of things, Bank of America’s Erica, Klarna’s AI Assistant, and Capital One’s Eno are voice and text conversational AI agents designed specifically for customer service.

The differences between conversational AI and a scripted IVR phone menus are vast: conversational AI can have back-and-forth conversations, understands accents and slang words, actually sounds like a human, is multi-lingual, and evaluates conversational context.

IVR systems are little more than glorified, keyword-based phone trees that can only respond to questions phrased in a specific way. Their scope is much more limited than conversational AI, and they often trap humans in endless loops of unhelpful menu options.

Conversational AI is the clear choice for a business looking to automate and expedite customer experience interactions. Which conversational AI  examples are worth knowing, how do they work, and which ones should your business seek to emulate?

 

What Counts As Conversational AI

Conversational AI understands the language and intent behind natural speech or text and responds with relevant, personalized, and genuinely helpful information.

The technology works by using speech-to-text to turn spoken words into text, so it can understand what the caller is saying. Natural Language Processing (NLP) then reads and interprets that text to determine the caller’s goal, intent, and mood (sentiment.) It picks up on details that matter, like customer names or order numbers. The AI agent then generates an appropriate response, using machine learning to improve with every interaction.

 

Conversational AI vs Rules-Based Chatbots 

Rules-based chatbots and IVR systems are far less advanced than conversational AI agents.

Rules-based chatbots work using keyword matching, canned responses, and scripted responses. So, when a customer asks a question outside of a chatbot’s pre-programmed knowledge, it’s unable to help – meaning it either ends the call or routes them to a human representative for resolution.

Conversational AI reads and understands the entire message, keeps track of what was said earlier, and can easily adjust when a customer goes off-script or uses colloquialisms. Instead of following a decision tree and script, conversational AI follows the natural flow of conversation.

If you’re trying to find out of the virtual agent you’re interacting with is conversational or rules-based, try:

• Rephrasing your question or request (a conversational AI agent understands and responds to the question the same way, no matter how you phrase it)
• Change your mind or switch topics midway through the conversation (a scripted bot will force you to completely restart the interaction)
• Refer back to an earlier part of the conversation by saying something like "Actually, can you go back to the first option?" or "Can you go back to what you said earlier?" (a conversational AI agent will be able to handle the request, a scripted agent won’)
• Ask for a human (ideally, both options could route you to a human, but often, IVR forces you to leave a message or call back)

 

Familiar Conversational AI Examples

If you’re still a bit confused by the concept of conversational AI, check out the samples in the table below. We’ve outlined what each conversational AI agent is, how to reach it, and what it does best.

Example	Channel	What it handles	Best illustrates
Apple Siri	Voice or typed requests on iPhone, iPad, and Macs	Directions, timers, device/app actions, basic Q&A	Hands-free mobile assistance
Amazon Alexa	Amazon Echo smart speakers and other devices, Alexa app Alexa-compatible and devices	Smart home control, music, reminders, shopping, reservations, basic Q&A	Voice control at home
Google Assistant	Voice and text across Google Nest devices, Google Home/Gemini integrated or compatible smart home devices, Google Nest	Questions, smart home control, media updates, routines, timers	One assistant across many devices
Samsung Bixby	Voice, text, or tap on Galaxy phones, tablets, watches, Buds, and other compatible devices	SmartThings devices, device settings, IoT, image analysis, routines	Device-level phone control
ChatGPT	Web and mobile app via voice or text	Open-ended questions, media generation, research, analysis	Open-ended generative conversation
Claude	Voice and text via Web, desktop, and mobile apps	Writing, research, data analysis, open-ended questions	Generative writing and analysis
Bank of America Erica	In-app via Banke of America’s mobile banking app, using text and voice	Account information, card lock, suspicious activity alerts, balance updates, bill reminders, Zelle	Banking self-service
Klarna AI assistant	In-app, web chat	Payments, refunds, cancellations, disputes	High-volume customer service
Humana	Phone call	Provider qualifications, benefit claims, authorization, referrals	Phone-based health benefits help
Phreesia VoiceAI	Inbound/outbound voice calls	Scheduling, referrals, records requests, refills, billing	AI phone answering for practices
KLM Miss Blue	Facebook Messenger, Google Assistant, Whats App, KLM website or mobile app via voice or text	Flight searching, booking, cancellations, requests, rewards management, packing tips	Travel booking and changes
Sephora Virtual Artist	Text in-app/on Sephora website, Facebook Messenger, has AR try-on	Virtual makeup try-ons with shade matching, product suggestion s	Retail product recommendations
Domino's AI assistant	Website, mobile app, store phone lines via voice and text	Placing orders and checking order status	Ordering by chat or voice
Khanmigo	Messaging in Khan Academy or via website	Guided student tutoring, lesson planning for teachers	AI tutoring

 

Examples of Conversational AI Assistants and Chatbots You Already Use

If you’ve ever told your smart home device to turn the lights off, asked ChatGPT about your medical symptoms, or told Siri to play your favorite song, congratulations: you’ve used a conversational AI tool.

Here’s a closer look at some of the most well-known conversational AI assistants and chatbots out there.

 

Apple Siri

Apple’s Siri was one of the first accessible conversational AI agents, answering to one simple prompt from users: "Hey, Siri." Siri started as a mobile voice agent for only iPhones, but is now included on Macbooks, iPads, and other Apple devices.

What it does: Siri handles hands-free tasks across Apple devices via Voice AI. It can send text, get directions, read text messages, set timers, and answer quick questions. Since iOS 18, questions to Siri may also be typed.

What makes it conversational: Siri maintains context between requests, so you can refer back to previous questions. It also follows along when you stumble over your words, cough or pause, or change your mind mid-sentence.

Limitations: As of this writing, Siri only acts inside compatible applications from Apple’s App Intents framework.

Best example of: Mobile-first conversational voice AI agent accessible from multiple Apple devices

 

Amazon Alexa

Alexa is a smart speaker that uses conversational voice AI to help run households. It’s an Amazon product compatible with numerous IoT devices like Echo, Alexa speakers, smart lightbulbs, thermostats, and more.

What it does: Alexa now works across over 600 million compatible devices, sending out shopping reminders, sharing random facts, setting timers and alarms, answering basic questions, and coordinating routines between smart home items.

What makes it conversational: Can have two-way, back-and-forth conversations with users and take action based on permissions and user input (booking appointments, turning on devices, making Amazon purchases, etc.)

Limitations:  Intelligence is not as powerful as other options here, frequently pauses, and often misunderstands user inputs.

Best example of: Smart device control via voice AI

 

Google Assistant

Google Assistant spans phones, smart home devices, Google Nest devices, smartphones, and Google TV – making it one of the broadest consumer-facing conversational AI tools. It can be activated with the phrase, "Hey Google."

What it does: Answers user questions, sets reminders and timers, turn TVs on/off, manages smart home devices, plays media across devices, and more.

What makes it conversational: Google Assistant carries context from one question to the next, so follow-up questions don’t require repeating yourself. Gemini is set to understand the same commands as Google Assistant, while allowing you to speak more naturally.

Limitations: Assistant’s capabilities depend on what device you’re interacting with/controlling. Google has started replacing Assistant with Gemini, its intelligence tool, on Android phones and tablets, some smart watches, and headphones. As of this writing, Google has retired over two dozen Assistant features.

Best example of: A single assistant that controls multiple home devices 

 

ChatGPT

ChatGPT is a generative AI model, meaning it writes and reasons through open-ended questions instead of completing a series of tasks.

What it does: ChatGPT drafts content, answers questions, analyzes data, works with your existing tools, and continually improves via machine learning

What makes it conversational: ChatGPT remembers the entire conversational context and continually refines itself based on user activity, meaning each reply is shaped by earlier interactions. ChatGPT can add details you forgot, revise its answers, and pick conversations right up where they left off.

Limitations: A model can only hold so much text at once, so in a very long chat, earlier details may be lost. Additionally, generative models can be confidently wrong – fabricating answers from hallucinated data and providing false information that shouldn’t be acted on.  Businesses using generative AI with customers should ground responses in approved sources like company policies, product data, knowledge base information, etc. Always verifyAI generated information.

Best example of: Generative conversational AI

 

Business Examples of Conversational AI in Banking, Payments and Healthcare

Within a business context, conversational AI is essential for customer service, content generation, and taking over repetitive, mundane tasks that can easily be automated (scheduling, account updates, Tier-1 support cases, etc.)

The below business conversational AI examples plug into existing systems to fully resolve interactions and tasks.  

 

Bank of America Erica

Erica is Bank of America’s mobile banking app for handling everyday account tasks. Erica has surpassed 3 billion client interactions and averages over 58 million interactions per month.

What it does: Checks balances, provides fraud alerts, locks and replaces cards, monitors recurring charges, sends money with Zelle, provides bill reminders, searches transactions, schedules payments. (Does not provide investment advice.)

What makes it conversational: Erica also serves the bank's own workforce. Erica for Employees is used by more than 90% of Bank of America employees and has cut calls into the IT service desk by 50%. In the contact center, EricaAssist supports more than 18,000 customer service representatives during live calls, surfacing relevant guidance in real time.

Limitations: Erica only works in English and is text-only. When it doesn't recognize a question or doesn't have a good answer, it connects the client to a specialist by phone or chat.

Best example of: Banking self-service

 

Klarna AI assistant

Launched in partnership with OpenAI, Klarna's assistant leverages conversational AI to complete day-to-day banking customer service tasks.

In its first month live, Klarna reported that the assistant held 2.3 million conversations (two-thirds of its customer service chats) doing the equivalent work of 700 full-time agents. Customers resolved their errands in under 2 minutes, compared to 11 minutes previously, and repeat inquiries dropped 25%. It runs 24/7 across 23 markets in more than 35 languages.

What it does: Klarna's AI assistant handles refunds, returns, payment-related issues, cancellations, disputes and invoice inaccuracies.

What makes it conversational: The assistant pulls each customer's own account into the conversation, offering real-time updates on outstanding balances and upcoming payment schedules.

Limitations: Klarna's story is also the industry's best-known cautionary tale. In May 2025, CEO Sebastian Siemiatkowski told Bloomberg that AI-led cost cutting had resulted in lower-quality service, and that Klarna was hiring human agents again so customers would always have the option to talk to a person.

Best example of: High-volume customer service automation

 

Humana

Humana's Provider Services Conversational Voice Agent, built with IBM Watson, is a phone-first example, helping healthcare providers check patient insurance coverage without waiting for a live agent.  Humana receives more than one million provider calls every month, and has handled inquiries at about a third of the cost of the previous system

What it does: Humana’s voice agent handles routine pre-service questions with well-defined answers: think questions on member eligibility, benefits, claims, authorizations and referrals. Users can complete an inquiry in about two minutes.

What makes it conversational: When building their agent, Humana trained seven language models and two acoustic models tuned to the kinds of things callers actually say. Humana’s voice agent understands the intent of each call, verifies that the caller is allowed to access the member's information, and answers with a specific benefit, like the co-pay for a chiropractic visit.

Limitations: Humana’s customer service agent is limited to voice only, cannot complete more complex tasks without involving a human, and is bound by strict compliance standards and regulations.

Best example of: Voice self-service in healthcare

 

Phreesia VoiceAI

Launched in September 2025, Phreesia VoiceAI answers phones for medical practices and handles the most common questions healthcare organizations get every day. Phreesia says nearly 50% of self-scheduled appointments happen outside business hours, and that VoiceAI handles 80% of calls without staff involvement.

What it does: Phressia VoiceAI handle scheduling and referrals, prescription refills, clinical questions and medical records requests. Most importantly, it handles after-hours calls

What makes it conversational: Phreesia VoiceAI identifies why a patient is calling from what they actually say: no rigid phone trees, no button-pushing. During the call, it works directly with the practice's scheduling and records systems. It integrates with leading practice management and EHR systems, matching patients on inbound calls, surfacing their information mid-call, and writing everything back to the chart automatically.

Limitations: Phressia VoiceAI is limited by strict regulatory healthcare requirements, so cannot complete more complex tasks and must route the caller to a human.

Best example of: AI phone answering for a practice

 

Examples of Voice Agents and Conversational AI on Business Phone Lines

Conversational AI lets callers discuss their problems and desired outcomes in their own words, often on their preferred channel. They no longer need to "Press 1 for sales" or "Briefly state the reason for calling" in three-word "keyword speak" like "need screen replacement" or "refund broken purchase." Unlike traditional chatbots or IVR, conversational AI can handle more complex, long, or multi-intent questions.

Conversational AI can also handle turn-taking, but note that it’s much harder to master in voice calls as opposed to chat. On a call, the AI has to figure out if the customer is finished speaking or pausing, and handle interruptions.

 

Open-Ended Questions

Instead, customers are asked an open-ended question like "What are you calling about today?" They can respond naturally, saying something like, "I need to return last Thursday’s purchase, but I can’t find my order number" or "I have a question about your refund policy, the suitcase I bought from you broke immediately."

 

Intent Detection and Smart Routing 

Once the caller makes their request, conversational AI evaluates what they’re trying to accomplish, the data it needs to make that happen, whether or not a conversational AI agent is allowed to handle their request, steps they should take to resolve the issue, and the best human agent to route the caller to if need be. Conversational AI also monitors customer sentiment to adjust the tone, volume, or pacing in their responses.

 

Handoffs and Escalations

The handoff between an AI agent and a human agent is where most interactions fall apart. A conversational AI agent, however, can retain complete conversational context and history when transferring the call to a live agent. Before a live agent accepts the call, the conversational AI shows them what’s been said, the actions it’s already taken, and suggested next steps to resolve the issue quickly without repetition.

 

Sample Conversational AI Call 

Voice Agent: Thanks for calling Medical Avenue, how can I help you today?

AI Opens with a question, not a menu tree 

Caller: Hi, I need to move my appointment to Tuesday.

AI Detects intent to reschedule, agent matches caller to her patient record by her phone number. 

Voice Agent: I can help with that, I see you’re booked Thursday, June 10 at 10:00 AM with Dr. Roy. Is that correct?

AI confirms details before changing anything

Caller: Yes. Do you have anything next week in the afternoon?

AI checks live scheduler tool for availability

Voice Agent: I have Tuesday at 2:30 or Wednesday at 4:00 PM. Which works better?

AI gives multiple options

Caller: Tuesday – oh, and I got a bill I don’t understand.

AI books Tuesday, sends confirmation text, then detects a new intent: a billing question. 

Voice Agent: You’re all set for Tuesday at 2:30 with Dr. Roy, you should get a confirmation text in a moment. For the billing, let me connect you directly to a member of our billing team.

AI confirms booking is complete, then routes the caller to a live agent while providing warm transfer information

Billing Staff: Hi there, I saw you rescheduled your recent appointment to Tuesday and have a question about a bill – what did it say?

Human agent takes over the call right where it left off 

 

How To Choose the Right Conversational AI Use Case

Conversational AI can be applied to a wide variety of use cases, but getting the proper value out if it requires careful planning and continued monitoring. Look for use cases where volume, data access, risks, and channels all align. Before you talk to a vendor, you should be able to answer:

• Which interactions are high-volume, repeatable, and low-risk?: The conversations you have dozens of times each week, customer questions with well-defined answers, and Tier-1-level support tasks are great candidates for automation
• Can the virtual agent find the data it needs to complete the task? Your conversational AI agent should be connected and fully integrated with all the applications it needs to provide the right answers (like your CRM system, your knowledge base data, contact center software, sales applications, etc.) If it can’t pull data from these systems, it’s no better than a standard chatbot
• What does a wrong answer cost, and is it clear who owns the error? The top barrier to a successful conversational AI deployment is a lack of clear ownership when the virtual agent does something wrong or fails to provide assistance. Define who is in charge when the virtual agent fails, exact steps to take, and always make it easy for customers to reach a human when needed
• Which channel(s) do your customers already use? You don’t need to deploy across all channels all at once, but know where your customers are: phone, chat, or messaging. Context preservation across channels is essential, so adopt an omnichannel strategy to prevent customer repetition

If Most Contacts	Start With
Come from phone calls and mostly deal with booking, appointment management, or basic account updates	A conversational voice AI agent with customizable templates for easy set-up and faster deployment, integrated with CRM, internal docs, and sales tools
Come via website contact forms and usually relate to policies, order updates, account information, or basic FAQs	A conversational messaging agent embedded directly in your website and/or mobile app, connected to your CRM and knowledge base tools
Are internal and come from employees asking about PTO, HR policies, device setup requests, and standard IT issues	An internal messaging tool integrated with your helpdesk and internal documentation

 

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