Customers expect instant responses 24/7, or they’ll move on. But of course, your team can’t be available around the clock. Meanwhile, repetitive questions eat up valuable time your reps could spend on more complex tasks.
Instead of losing another customer to your competitor, conversational AI takes care of basic questions and tasks. That way, customers get help faster, around the clock. And your team can focus on more meaningful interactions that build stronger relationships.
Here’s a closer look at conversational AI and how to easily implement it in your business.
What is conversational AI?
Conversational AI is software that can understand and respond to people through text or voice, much like you’d talk to a real person. It can hold basic conversations, answer questions, and handle simple tasks.
Before conversational AI, pre-programmed or traditional AI could only understand keywords and triggers. If a customer didn’t use a word or phrase exactly, the AI couldn’t respond accurately.
With conversational AI, you can ask questions naturally, like: “Can I book a haircut for next Friday afternoon?” The AI understands your intent, checks availability, and responds conversationally.
With older chatbots, you had to click through preset options. There was no flexibility to ask in your own words or have a real conversation.
There are five main types of conversational AI tools:
- Chatbots. These live on websites and apps to answer FAQs, gather customer info, and help users with self-service tasks. Tidio’s chatbots, for example, can answer routine questions and transfer complex ones to human reps.
- Virtual assistants. Use your voice to complete tasks on smart devices. You can set reminders, find info, or search emails with tools like Alexa and Siri.
- Generative AI bots. These can write content, brainstorm ideas, or create charts and tables with datasets. Popular generative AI tools include ChatGPT and Claude.
- AI assistants and copilots. These are virtual assistants baked into proprietary apps. For example, you can use Microsoft Copilot to automatically write Outlook emails. Or you could use Notion AI to search for key information in your uploaded documents.
- AI agents. Agents can complete complex tasks without human intervention, like scheduling, lead qualification, and customer service. For example, Sona is an AI voice agent built into Quo, formerly OpenPhone. It takes calls 24/7, answers questions, and logs every customer interaction. It’s like having an always-on answering service.
What’s the difference between conversational AI and generative AI?
Generative AI creates new content like text, images, or audio based on your prompts. For example, you could ask ChatGPT to help you generate a logo for your business.
Conversational AI often uses generative AI to produce more natural, context-aware responses. Let’s say a customer asks, “What’s your return policy for services booked with a coupon?” Conversational AI uses generative AI to respond in a way that’s specific to their question. It won’t just retrieve a generic, pre-written answer.
So to summarize: Conversational AI often uses generative AI. But not all generative AI is conversational.
How does conversational AI technology work?
Conversational AI works by using natural language processing to understand prompts and questions. It analyzes intent, then generates relevant responses based on the information you’ve trained it on. The more conversations it has, the better it gets at spotting patterns and providing accurate answers.
The AI model also remembers the entire conversation. That way, customers can ask follow-up questions or clarify details without repeating themselves.
Here’s an example of a conversational AI chatbot on a travel company’s website:
Conversational AI relies on four core technologies:
- Natural language processing, or NLP, interprets human language. This includes natural language understanding or NLU. It’s the technology that determines the meaning and intent behind what someone says.
- Machine learning, or ML, helps improve responses over time. ML tech relies on deep learning algorithms to help identify patterns and decide which responses work best.
- Natural language generation, or NLG, creates natural-sounding text responses based on the conversation context.
- Speech recognition and text-to-speech convert spoken language into text the AI can process. It also converts the AI’s responses back into natural-sounding speech for voice conversation.
Worried this sounds too complex? Good news: you don’t need technical expertise to use conversational AI. Platforms like Quo help businesses create conversational flows in seconds. You don’t need prior experience with artificial intelligence to get started.
What are some business use cases for conversational AI?
Here are a few common ways most businesses use conversational AI:
- Customer support: Answer FAQs, troubleshoot issues, and route complex questions to human reps. Since conversational AI helps you do this quickly, it can also help boost customer satisfaction scores.
- Lead qualification: Set up a missed call service to take messages or let chatbots engage website visitors. For example, financial services might use AI to qualify their leads. With conversational AI for sales, teams can schedule demos or consultations more easily.
- Appointment scheduling: Handle bookings, cancellations, and reminders so your team doesn’t have to. Plus, you won’t miss potential customers when you’re off the clock or busy.
- Sales assistance: Many ecommerce businesses use AI to answer questions about their products and help customers find what they need more quickly. Some AI can also guide customers through the checkout process.
- Internal operations: Give employees instant answers to common workplace questions without waiting on HR, IT, or other support teams. For example, AI can answer questions about PTO policies, reset passwords, or guide new hires through onboarding steps.
6 benefits of conversational AI for customer service
There are six key benefits of conversational AI for customer service:
- Ensure faster response times. According to Salesforce, 81% of customers expect faster service as technology improves. AI agents provide instant responses, improving the customer experience.
- Keep 24/7 availability. Away from the office or out on holiday? Conversational AI can instantly respond to incoming questions, preventing customers from exploring competitors.
- Maintain scalability. Conversational AI can handle dozens of questions or requests at once. That way, you don’t need to hire more people to handle high call volumes. You can stay responsive even during peak hours or business growth spurts.
- Boost customer service efficiency. Instead of bogging down reps with every call, let AI handle your routine inquiries. Salesforce also found that 81% of service reps using AI are free to work on more complex cases.
- Reduce operational costs. Conversational AI is substantially cheaper than hiring more reps. But it can also help you make money, too — the same Salesforce survey found it could boost upsell revenue by 15%.
- Create omnichannel service. You may not have the resources to support website chat, social media, email, phone, and text. By offloading some work to AI tools, like web chatbots or voice agents, you can be present across more channels.
What should you look for in a conversational AI platform?
With so many conversational AI tools on the market, narrowing down your shortlist can feel overwhelming.
Here’s a quick guide on what to look for:
- Ease of use. You may not want AI tools that require coding or tech expertise. In that case, search for a platform with no or low-code tools, like preset templates or visual call flow builders.
- Integration capabilities. Look for a platform that connects with the tools you already use, like your CRM, phone system, or website. For example, Sona integrates with CRMs like HubSpot. It also lives right inside Quo, so all your customer info and conversations stay in one place.
- Customization options. Your AI should be trainable on your products, FAQs, and brand positioning. That way, customers aren’t given inaccurate, generic, or conflicting info.
- Security and compliance. Look for transparency in how the tool protects customer data. It should also meet industry regulations like SOC 2 compliance.
- Pricing. Usage-based pricing can help you avoid overage charges or bloated enterprise plans. Since you only pay for what you use, it’s easier to scale over time.
- Scalability. Choose a platform that can handle growing conversation volumes as your business expands. It should scale without needing costly add-ons or new infrastructure.
- Support. Look for vendors that offer responsive customer support and comprehensive documentation. These will serve you well later — you’ll have plenty of resources to help troubleshoot issues.
👀 Bonus: You can also use this checklist to evaluate other AI tools. Check out our guide to AI tools for small businesses.
How to implement conversational AI for your business
With the right tool, setting up conversational AI is easier than you think. Here’s a step-by-step process to get started, with examples from Quo.
1. Identify your customers’ frequently asked questions
Once you know what your customers need, you can train your AI to support them better.
Start by asking your team what questions they hear most often. If you’re a Quo user, you can streamline this by reviewing patterns in call recordings or transcripts and summaries.
Common questions to train your AI on could include:
- “What are your business hours?”
- “What’s your cancellation policy?”
- “Do you offer [specific service]?”
- “How much does [specific service] cost?”
2. Decide where your AI should fit in
Decide where conversational AI will add the most operational efficiency. This could be a chatbot on your website answering common questions, in your phone system handling calls, or both.
For phone calls specifically, decide the types of calls your AI should handle. For example, you might set Sona up to handle after-hours calls, missed calls, or general inquiries.
You can roll this out quickly with Quo’s visual call flow builder. To get started, simply drag and drop Sona into your call flow. You’ll also have the Sona Wizard to guide you through each step with an in-app tutorial. Even if you’re not tech-savvy, you can get up and running in minutes.
3. Train your AI with your business information
The more information you train your AI on, the better it’ll perform. This means adding key info like:
- Industry jargon and terminology
- Company policies
- FAQs
- Product or service details
- Team member names
On Quo, you can train Sona by adding links to your site, uploading a file, or adding pages for further information. Test its knowledge with mock calls. Then refine your training materials based on Sona’s response quality.
You can get more specifics in our guide to Sona knowledge management.
4. Define rules for the AI’s behavior
Next, tell your AI what to do in specific situations based on your customer’s actions. For example, if a caller says their request is urgent, it might offer to transfer their call to your team.
With Quo, these instructions are called “jobs.” You can create your own from scratch or start with templates for common tasks.
When adding instructions in free-form, you can use the Optimize feature to standardize jobs. If you aren’t used to writing for AI, Optimize gives you a strong baseline you can trust instead of guessing whether it’s good enough.
One such job is qualifying leads. Christopher Sands, CEO of Hannon De Palma law firm, added Sona to a dedicated phone number used in their Google Ads. This way, Sona automatically qualifies every caller who saw the ad.
When someone calls that number, Sona asks about their name, active cases, state, and how best to reach them. Then Sona passes this info off to Chris’s team so they never miss out on clients that are a good fit.
📚Learn more: Best practices for setting up Sona jobs
5. Test and deploy your AI
Test your AI to see how it reacts to different customer scenarios. You need to make sure that:
- The tone matches your business
- The tool answers questions accurately
- The AI follows your rules for handling specific situations
- Conversations flow naturally without awkward pauses or the AI getting stuck on unclear requests
Make adjustments based on test calls and early customer interactions. This could be updating knowledge articles, tweaking job instructions, or refining greetings.
Sona makes testing easy with a “Test Sona” button right in your call flow builder. This lets you conduct mock calls before going live. Once you’re happy, publish your changes and monitor how it performs with real customers.
Even after deployment, you can still keep an eye on Sona’s performance. With call recording in Quo, every Sona call gets a recording, transcript, and summary after it ends. This gives you clear oversight into every conversation.
💡 Pro Tip: You can use Quo’s Model Context Protocol, or MCP, to connect Sona with ChatGPT or Claude. This can help you pick out larger trends in your data. For example, the kinds of questions people ask most, what they’re confused about, and common themes.
Understanding the limitations of conversational AI
Artificial intelligence is powerful, but it still has some technical restrictions.
Conversational AI has limitations like:
- Inaccurate responses. AI can sometimes generate incorrect or “hallucinated” answers. This means stating things that aren’t true, even when trained on quality data. This is an inherent limitation of how AI models work. Regular testing and improving the AI’s knowledge base can help reduce these.
- Language limitations. Audio-based AI may struggle with accents, dialects, and background noise. Text-based conversational AI can also trip up on slang or complex phrasing. So choosing a quality AI provider will make a huge difference in how much your tool can understand.
- User apprehension. Not everyone will be instantly on board with AI tools. Your team may worry it’ll replace them, while customers might feel uneasy chatting with a bot. The fix here is transparency. For your team, clarify that AI is there to assist, not replace them. For customers, make it obvious when they’re talking to AI. Plus, make it easy to reach a real human when needed.
- Privacy and security. Studies show AI and machine learning are more susceptible to hacking than other software: around 68% of organizations have accidentally leaked data through AI tools. You can mitigate this by choosing platforms with strong encryption and compliance certifications. You should also ensure the vendor provides transparent data handling policies.
Boost customer engagement with conversational AI from Quo
Conversational AI can’t replace human reps. But it can make a difference in how quickly they help customers. Having 24/7 responsiveness takes the pressure off missed calls and long wait times. Plus, it can improve customer satisfaction by answering repetitive questions quickly.
Quo makes it easy to use conversational AI to build better relationships with your customers. Just drag Sona into your call flows, train it on your business information, and start answering calls 24/7.
You can get started within 15 minutes or less. Sign up for a seven-day trial of Quo and test your first 10 calls with Sona.
FAQs
Conversational AI solutions like virtual agents must be HIPAA-compliant in healthcare settings. But you can still use AI with safeguards in place, like preventing it from accessing Patient Healthcare Information or PHI.
Large language models, or LLMs, are the AI systems that power many conversational AI tools. They’re trained on massive amounts of text to understand and generate human-like language. Conversational AI is the broader application that uses LLMs to hold conversations with users.
Traditional chatbots follow scripts and rules, so they can’t respond to unexpected user inputs. But conversational AI chatbots can understand context, allowing for more natural, human-like conversations.
You can try conversational AI tools like ChatGPT’s free version or Hume’s voice AI. But keep in mind, you get what you pay for — they might be underwhelming or pose security risks to your business.
