Conversational AI for customer service: Top platforms and benefits

Conversational AI for customer service has become the fastest way to scale support without scaling payroll. Customers want answers now, and they increasingly accept automation as long as it works.

Hear this: 78% of service leaders say their customers prefer to solve issues independently, and AI chatbots are now the most preferred customer service channel.

So let’s explore how conversational AI works, where it delivers real spend and speed gains, and the highest-impact use cases.

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What is conversational AI?

Conversational AI refers to technology that enables computers to simulate human-like conversations using natural language processing and machine learning. It powers tools like chatbots and virtual assistants that can understand intent, respond intelligently, and continuously improve through interactions.

Conversational AI for customer service automates customer interactions across chat, email, messaging, and voice. It understands what a customer is asking in natural language and responds instantly, escalating to a human when needed.

Unlike simple rule-based bots, modern conversational AI platforms learn from past conversations, connect to CRM data, and improve over time.

Businesses of all sizes use conversational AI to scale support and reduce costs without lowering service quality. HubSpot’s Customer Agent can help you calculate your own potential time and cost savings based on your team’s size and ticket volume — see what your ROI could look like.

How does conversational AI work?

Conversational AI for customer service uses natural language processing (NLP), machine learning, and large language models (LLMs) to understand and generate responses. Here’s what’s happening under the hood in simple terms.

1. Conversational AI interprets language. NLP breaks down what the customer says — whether typed or spoken — and identifies intent. For example, “I was charged twice” is recognized as a billing issue.
2. It predicts the right action. Machine learning models use historical data to determine what typically resolves that type of issue — refund flow, policy explanation, or escalation.
3. It generates a response. LLMs craft a natural reply using a company’s tone, knowledge base, and CRM data.
4. It learns from outcomes. If customers reopen cases or escalate, the system adjusts confidence thresholds and routing logic.

Conversational AI like Customer Agent enables 24/7 customer service availability by processing thousands of interactions simultaneously — giving your team the capacity to focus on the cases that need a human touch. That’s why the conversational AI market, valued at $14.3 billion in 2025, is projected to reach $78.9 billion by 2033 — growing at nearly 24% annually.

Types of Conversational AI Technology

Not all conversational AI solutions operate the same way. Most fall into four categories with concrete use cases.

Chatbots

Chatbots handle structured FAQs and basic workflows on websites or messaging apps. Modern AI chatbots use intent recognition instead of decision trees.

Voice Assistants

Voice assistants automate phone-based interactions using speech recognition and text-to-speech systems. Voice assistants answer calls instantly, authenticate users, and complete tasks without long hold times.

Virtual Agents

More advanced than chatbots, virtual agents can manage multi-step workflows, access backend systems, and resolve common issues end-to-end.

Example: A customer calls to update their shipping address. The virtual agent verifies identity, pulls up the order from the CRM, checks shipment status, updates the address in the system, confirms the change, and sends a follow-up email — without transferring to a human.

AI Agents

Autonomous AI solutions that combine conversation with reasoning and automation across systems. They understand context, trigger backend workflows, and adjust behavior dynamically.

If a SaaS customer says, “Our integration stopped syncing data,” the AI agent will execute a thorough issue analysis and guide a fix, doing the following:

1. Checks system status logs.
2. Identifies a recent API error.
3. Guides the customer through a fix.
4. Opens an internal engineering ticket if the error persists.
5. Notifies the account manager automatically.

How can conversational AI benefit service teams?

The pace at which conversational AI is penetrating the market is truly amazing. Gartner and EMARKETER have both reported increased use of AI for customer service across industries — and with 91% of service leaders now under executive pressure to implement AI, adoption is only accelerating.

1. Measurable Cost Savings

AI-powered digital assistants and bots handle large volumes of interactions simultaneously. They allow service leaders to cut costs on labor, office space, operational overhead, administrative tasks, utility bills, and resource allocation.

HubSpot reports that Customer Agent resolves an average of 70% of conversations, with teams using it alongside Help Desk seeing 50% more tickets resolved and 29% faster resolution rates. That shift translates into fewer required hires as volume grows.

2. Free Agents to Focus on Complex Issues

When AI handles repetitive inquiries, agents spend more time on escalations, negotiations, and emotionally sensitive cases. Service leaders also notice increased agent productivity, job satisfaction, and quality of complex case resolution. For complex or technical inquiries, AI ensures customers get help fast by sorting and routing queries to the right specialists.

3. Answer Every Call Immediately, 24/7

One of the most attractive features of any AI tool is its consistency. Conversational AI enables 24/7 customer service availability across chat and voice. No rest or breaks required.

This is especially helpful for businesses with a global reach. Human staff don’t need to be working around the clock. Instead, AI can handle matters at all hours of the day, ensuring customers get assistance whenever needed. On top of that, voice assistants eliminate hold times by answering calls instantly, handling authentication, and routing in seconds.

For example, Customer Agent supports customers across every major channel and works continuously in the background, so teams can provide instant, consistent support without expanding headcount.

4. Gain Deeper Customer Intelligence

Conversational AI structures every interaction into usable data. CX teams get visibility into recurring intents, escalation triggers, sentiment shifts, and knowledge gaps in real time. That data also surfaces product friction, billing confusion, onboarding weaknesses, and early churn signals long before they appear in traditional reports.

When conversational AI platforms integrate with CRM and support systems, conversations become analytics. Sales, marketing, and customer service teams can automate and monitor cross-selling and upselling campaigns, or simply manage client accounts more efficiently.

5. Personalized Support Without Limits at Scale

Modern conversational AI platforms integrate with CRM software, allowing responses to reference purchase history, subscription tier, open cases, and prior conversations. For that, conversational AI for customer service uses NLP and ML to adapt its responses to context and memory. Personalization happens in real time, even at scale.

Example: A returning customer asks about a delayed shipment. The system already knows the order shipping method and customer tier and adjusts tone or compensation accordingly.

“AI can help highlight product issues before they’re vocalized by users — meaning you can solve problems before they even arise,” says Gero Keil, co-founder and CEO of Levity, in a blog post.

Conversational AI tools can recognize positive and negative sentiments. Customer service reps can then act on the data to provide customized recommendations or solutions to each customer.

Featured Resources

• Future of AI in customer relationship management: What’s coming next
• 5 companies using AI for customer service

Real-world Service Use Cases for Conversational AI

Most customer service operations can be efficiently executed using AI-powered chatbots, virtual assistants, AI-powered interactive walkthroughs, and voice assistants. This minimizes costs and allows service teams to focus on more important things.

Voice-based Authentication and Fraud Prevention

Authentication used to slow everything down. Customers answered static security questions or waited for manual verification before any real support could begin.

Conversational AI now integrates voice biometrics and contextual authentication. A voice assistant can verify identity through speech patterns while continuing the conversation naturally. If risk signals appear — unusual login location, billing anomaly — the system escalates automatically.

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HSBC UK once announced that its voice biometrics system prevented almost £249 million of customer funds from being fraudulently accessed, resulting in a 50% decrease in attempted fraud compared to the previous year.

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Intelligent Self-service and Account Management

Conversational AI platforms allow customers to complete tasks directly through natural conversation. A customer can say, “Change my shipping address,” or “Upgrade my plan,” and the system retrieves CRM data, applies changes, and confirms in seconds.

Self-service becomes task completion, so conversational AI reduces agent workload and response times by resolving structured issues instantly.

For example, McAfee, a company that provides anti-virus services, has deployed AI-powered voice bots that allow for customer self-service. These voice bots offer seamless interaction, leading to a 74% auto-resolution rate through self-service and a 75% boost in productivity for customer assistants.

Likewise, Lemonade’s insurance chatbot, Maya, is a helpful companion who guides users when they’re buying insurance. Maya makes the process easier and more understandable for people.

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HubSpot’s Customer Agent follows this same model by turning existing knowledge base content into instant, conversational answers. This allows customers to complete tasks or resolve issues without waiting for a human.

Automated Order Management, Booking, and Transactions

Voice assistants now confirm appointments, reschedule bookings, track shipments, and process returns in real time.

For those going on a vacation, conversational AI can easily reserve, book, and pay for a hotel. For example, Resorts World Las Vegas uses Red to help its guests. Amelia is a digital concierge skilled in providing excellent guest service.

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Red can help guests with dinner reservations, show ticket purchases, room service orders, wake-up calls, and more. Red handled over 223,000 guest interactions in 2024 and adeptly resolved over 59% those interactions without escalation, making CX easier and guest experiences smoother.

Proactive Engagement and Retention Triggers

Conversational AI solutions monitor behavioral signals such as repeated contact, failed payments, cancellation attempts, or negative sentiment. When thresholds are triggered, the AI initiates outreach via voice or messaging.

Multilingual and Global Support Expansion

Conversational AI platforms detect language automatically and switch mid-conversation. Voice assistants adapt pronunciation and tone. Messaging bots provide consistent multilingual support without region-specific staffing spikes.

Airbnb uses multilingual support in its chatbot to assist users in 31 languages with booking accommodations, addressing inquiries, and offering assistance. Enterprise conversational AI allows companies to expand globally without multiplying operational complexity, which is a huge advantage.

Accessibility and Inclusive Service Delivery

AI can also improve the quality of customer support for people with disabilities, ensuring inclusive experiences. Examples include text support for the hearing-impaired, voice support for the visually impaired, language translation, and simple language for cognitive disabilities.

Bank of America uses advanced AI to improve accessibility for all customers, including those with disabilities. Its virtual assistant, Erica, provides tailored financial guidance to users, including those with accessibility needs.

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To support visually impaired users, Erica includes Americans with Disabilities Act (ADA) tags, helping them navigate different app sections. Erica’s voice is automatically disabled so that they don’t conflict. Over 32 million customers have engaged with Erica since its release, making it one of the most popular AI tools in the U.S.

AI-powered Agent Productivity

Conversational AI for customer service improves agent productivity when it removes repetitive work inside the support workflow, then turns every interaction into learning signals.

HubSpot’s Customer Agent does both. It handles routine conversations autonomously and escalates cleanly when confidence drops, so human time goes to issues that require judgment.

Youth on Course reported that even with a 75% increase in support tickets, response time improved 16% and CSAT rose 7% after adopting Customer Agent.

“We were shocked,” says John Mothershead, director of member success at Youth on Course. “We had more volume than ever — but we were faster, and our members were happier.”

If service leaders are serious about cutting response times without burning out their team, they can explore HubSpot’s Customer Agent and test how much of their real support volume it can handle automatically.

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Keep track of customer requests in one unified help desk that connects to your CRM database.

• Provide personalized, AI-powered support to your customers
• Save time, reduce errors, and streamline service processes
• Improve rep efficiency & time to resolution
• And more!

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Conversational AI Best Practices

Maintain transparency and choice.

Disclose AI involvement at the start of customer interactions, in plain language, and keep it consistent across chat and voice. Customers like AI automation when the system is honest and helpful.

Allow the “Talk to a person” option that actually escalates. In voice, that means natural phrases (“agent,” “representative,” “call me back”) that work every time. Conversational AI enables 24/7 customer service availability, but it earns trust only when customers retain control.

Pro tip: Route human escalations with context since 74% of customers find it frustrating to repeat their story to different agents.

HubSpot Service Hub keeps the conversation history and CRM context attached in the Help Desk so the agent can start midstream.

Design for emotional intelligence.

Conversational AI should not “handle” emotionally charged moments. It should recognize them early and escalate fast. Billing disputes, fraud concerns, cancellations, and repeat-contact frustration belong with humans once sentiment turns negative or the customer repeats themselves. Write escalation rules that reflect real behavior:

• Two failed attempts to complete a task.
• Repeated questions.
• A negative sentiment signal.

Prioritize data privacy and security.

Conversational AI platforms touch sensitive data like identity details, payment context, health or legal requests, and account access.

To ensure security, service teams have to understand what data the AI can access, what it must never store, and what gets logged for audit. Then align retention and access controls across channels.

Pro tip: Redact or mask sensitive fields before they reach the model layer, even if the rest of the stack can view them.

Optimize continuously.

Conversational AI drifts as products change, policies update, and customers invent new phrasing. To keep a close watch, run weekly or biweekly review failures, retrain content, tighten intent definitions, and adjust escalation thresholds. Use a simple loop:

• Start with the top intents by volume.
• Check containment rate, reopen rate, and escalation reasons.
• Fix the knowledge gaps that cause repeats.
• Expand coverage.
• Measure success with conversational AI by KPIs like resolution time, CSAT, and cost per contact.

Customer Agent helps teams improve performance metrics by automatically resolving common requests and capturing insights from every interaction to continuously refine performance.

Preserve your brand voice.

Customers notice when the tone shifts between channels, and AI can magnify inconsistencies quickly. To preserve brand voice, define a short voice guide for AI:

• How it greets.
• How it apologizes.
• How it asks for clarification.
• How it closes.

Train on examples of your best real replies. In HubSpot Service Hub, use the Knowledge Base and past ticket responses as the source of truth, so AI learns how a company’s team actually communicates, not how a generic assistant sounds.

Plan for scale.

Design the system to handle more conversations, channels, and intents without constant rebuilding. Start by separating automation into clear service domains — billing, orders, account access, or troubleshooting — instead of training one large assistant to answer everything.

Then connect the AI to your service infrastructure. When conversational AI integrates with a company’s CRM, help desk, and knowledge base, new automation scenarios can be added without rewriting the whole system.

The 6 Best Conversational AI Tools for Service Teams

1. HubSpot Service Hub + Customer Agent

HubSpot’s Customer Agent is a CRM-native conversational AI solution embedded directly inside Service Hub. It automates routine support conversations, summarizes tickets, and captures structured insights from every interaction.

Unlike with standalone bots, AI connects to HubSpot Smart CRM records, enabling context-aware automation. HubSpot reports that customers using Customer Agent resolve over 50% of support tickets and spend nearly 40% less time closing them.

Key Features

• Omnichannel support across chat, email, and messaging in a shared inbox.
• AI-generated ticket summaries and conversation insights.
• Native CRM integration for personalized, context-aware interactions.
• Help desk and ticketing automation with routing, prioritization, and SLA tracking.
• Knowledge base integration to power self-service and faster resolutions.
• Reporting dashboards for performance, customer satisfaction, and team productivity.
• Seamless handoff from AI to human agents when needed.

Best for: Teams that want conversational AI tightly integrated with CRM data, knowledge base content, ticket routing, and reporting.

Pricing: Customer Agent is included in Professional ($100 per user a month) and Enterprise ($150 per user a month) subscriptions and runs on HubSpot Credits.

What I like: AI is deeply embedded within HubSpot’s CRM, so every interaction is automatically enriched with customer context — no need for third-party integrations. The combination of AI automation, knowledge base content, and ticketing in one platform makes it easy to scale support while still delivering personalized experiences.

2. Zobot by Zoho CRM

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Zobot is a part of Zoho SalesIQ’s chatbot platform and is a user-friendly, codeless, drag-and-drop chatbot builder. It makes it easy to create AI chatbots to automate live chat interactions. For those with coding skills, Zobot also offers a programming interface to enable advanced automation possibilities.

Zobot is compatible with various AI technologies, including IBM Watson, Dialogflow, Microsoft Azure, Haptik, and Zia Skills, enabling seamless integration. By using Zobot, service teams can answer customer queries, automate responses, and provide instant assistance.

Key Features

• Self-service platform.
• Codeless drag-and-drop chatbot builder.
• Integration with top AI technologies.
• Hybrid chatbot capabilities (combining guided flow and AI).
• Programming interface for custom bot development.

Best for: Retail, ecommerce, real estate, financial businesses, and technology companies.

Pricing: Starts from $10 per month.

What I like: Users can create AI chatbots effortlessly using a codeless chatbot builder and integrate them with their existing AI technologies like IBM Watson, Dialogflow, and more. Develop hybrid chatbots by combining guided flow and AI capabilities. Use the company knowledge base for chatbot training.

3. CloudTalk

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CloudTalk is a cloud-based call center software and conversational intelligence platform that helps sales and customer support teams automate phone conversations, improve agent performance, and gain deeper insights from customer interactions. Designed for small and mid-sized businesses, CloudTalk combines AI-powered voice automation with advanced call analytics to help teams reduce response times, handle higher call volumes, and deliver more consistent customer experiences.

CloudTalk’s conversational intelligence capabilities automatically transcribe, summarize, and analyze customer calls, making it easier to identify customer needs, track sentiment, monitor agent performance, and uncover recurring issues. The platform also includes AI Voice Agents and a Virtual Receptionist that can answer routine questions, qualify leads, route callers to the right department, and provide 24/7 support without human intervention. With support for over 100 integrations, 160+ international virtual phone numbers, and AI voice interactions in more than 60 languages, CloudTalk helps businesses scale customer communication across global markets.

Key Features

• AI-powered conversational intelligence.
• Automatic call transcription and AI-generated summaries.
• Sentiment analysis and conversation insights.
• AI Voice Agents for inbound and outbound call automation.
• AI Virtual Receptionist and intelligent call routing.
• Agent performance monitoring and call analytics.
• 100+ CRM, help desk, and business tool integrations.

Pricing: CloudTalk offers flexible pricing plans based on business size and feature requirements. Plans start at $25 per user per month when billed annually. 

Best for: Customer support teams, sales organizations, SaaS companies, ecommerce businesses, and growing SMBs looking to automate phone support while gaining actionable insights from customer conversations.

What I like: CloudTalk goes beyond traditional business phone systems by combining calling capabilities with AI-powered conversational intelligence. Teams can automate routine interactions with AI Voice Agents, reduce call handling times with Virtual Receptionists, and use automatic transcription, summaries, and sentiment analysis to uncover customer insights without manually reviewing every call.

4. Revenue Digital Assistant (RDA) by Conversica

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Conversica’s intelligent virtual assistants (IVAs) engage with leads, prospects, customers, and employees through email, SMS text, and website chat.

With language support in multiple languages, including English, French, German, Spanish, Portuguese, and Japanese, Conversica’s technology mimics honest human dialogue to drive engagement and revenue growth.

Key Features

• Insights-driven conversations.
• Feedback collection and analysis.
• Proactive customer engagement.
• Renewal initiation.
• Personalized email marketing.

Pricing: Quote-based; demo required.

Best for: Sales, marketing, and customer success teams in most verticals, including technology, telecommunications, insurance, automotive, and hospitality.

What I like: Conversica’s IVAs integrate with over 50 business tools, including CRM and marketing platforms. It uses data enrichment and context generation for personalized exchanges and engaging contacts through email, SMS, and website chat. Its multi-language support helps companies that need to support diverse customer interactions.

5. Netomi

Netomi is an advanced platform designed to provide enterprise-level businesses with an innovative solution for automating and upgrading their customer support operations. It provides automation of repetitive customer questions through chatbots and immediate access to hyper-relevant articles from the knowledge base.

Unlike other chatbots, Netomi uses smart AI and deep learning technology to deliver an enhanced customer experience. Its real-time support is available on various support channels.

Key Features

• Generative AI.
• Email resolution.
• Webform automation.
• Omnichannel integration.
• Auto-pilot and co-pilot modes.

Pricing: Prices are available upon request.

Best for: Ecommerce, entertainment, fintech, SaaS, telecom, travel & hospitality businesses.

What I like: Netomi offers multi-channel support, including web chat, messaging, SMS, and voice, with native integration with leading platforms like Zendesk Chat. It offers customer support in over 100 languages for a global reach.

6. AI Chatbot by Haptik

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With the evolution of AI chatbots into Intelligent Virtual Assistants (IVAs), Haptik offers contactless shopping experiences and interactions via web, app, WhatsApp, and other channels.

Key Features

• Drag-and-drop interface for creating chatbots.
• 24/7 functionality.
• Real-time issue resolution.
• Generative AI.
• Hybrid AI & live agent system.

Pricing: Haptik pricing starts at $5,000 per year.

Best for: Ecommerce, retail, travel and hospitality, telecom, mortgage, and financial services.

What I like: Haptik’s chatbot is scalable and offers personalized solutions using sentiment analysis. It measures the effectiveness of AI chatbot interactions and improves accordingly.

Also, it integrates with Messenger, WhatsApp, Gila CMS, Freshdesk, Meta for Business, Instagram, Shopify, and Zendesk Suite.

G2 score: 4.5

Capterra score: 4.4

How to Choose the Right Conversational AI Platform

Choosing a conversational AI platform is about features and fit. The wrong decision usually fails due to integration issues or channel mismatches, not because the AI “wasn’t smart enough.”

1. Start with your primary channel: voice or digital.

If support volume is phone-heavy, prioritize voice-native conversational AI. Platforms like Haptik and Netomi support voice automation directly, while some tools are chat-first and extend to voice through integrations.

Voice automation changes architecture. It requires latency control, call routing logic, and escalation design that differ from chatbots.

If support traffic is primarily chat, email, and web forms, a CRM-native solution like HubSpot Service Hub with Customer Agent often delivers faster ROI because it works inside the existing support workflows.

Decision rule: Match the AI to where 60% to 70% of support volume already lives.

2. Evaluate omnichannel depth.

Inspect three critical scenarios for choosing the right conversational AI platform:

• Does AI retain context if a chat moves to email?
• Escalate from bot to human without resetting the conversation?
• Sync with CRM records automatically?

If you gathered three “yes,” move to the next step. If not, drop the tool.

3. Align with your technical reality.

Some platforms require API-heavy implementation. Others offer no-code or low-code builders. Enterprise conversational AI often requires audit logs, permission controls, and compliance layers. Simpler tools may move faster but lack enterprise safeguards.

Think about the business’s case and answer these questions:

• Do you have internal technical resources to implement a tool?
• Do you need governance controls?
• Are you operating in a regulated industry?

4. Define automation goals before budget discussion.

Conversational AI automates customer service interactions — but only when the scope is explicit. If the target is vague, implementation becomes vague.

Are you trying to remove FAQ tickets from the queue? Handle booking and order updates automatically? Replace part of inbound call handling with voice AI? Or reduce agent handling time inside tickets by auto-drafting responses and summarizing conversations? Each goal requires a different architecture.

5. Model pricing against real resolution volume.

Pricing models differ. Some platforms charge per seat. Others charge per automated resolution. Some run on usage credits. Enterprise vendors may offer annual contracts tied to projected volume.

Start by estimating the monthly conversation volume. Then define the target containment or deflection rate. Finally, project the percentage of interactions that will escalate to human agents.

Step into the future: Empower your service team with conversational AI.

Conversational AI is quickly becoming the backbone of modern customer service. Teams that automate routine conversations, surface better insights, and personalize interactions at scale consistently reduce costs while improving customer satisfaction.

The opportunity now is to move from experimentation to implementation. Start by identifying your highest-volume support requests, define what success looks like (faster resolution, lower cost per ticket, higher CSAT), and choose a platform that fits your channels and workflows. Then iterate. The teams seeing the biggest gains are the ones continuously refining how AI and humans work together.

If you want a fast path to value, look for a solution that’s already connected to your customer data, help desk, and knowledge base. Tools like HubSpot’s Customer Agent, built inside Service Hub, make it easier to automate real support volume from day one without stitching together multiple systems. Teams can resolve more than half of incoming tickets automatically, reduce handling time, and give their team space to focus on the conversations that actually require human judgment.

Editor's note: This post was originally published in November 2023 and has been updated for comprehensiveness.

Free Help Desk & Ticketing Software

Keep track of customer requests in one unified help desk that connects to your CRM database.

• Provide personalized, AI-powered support to your customers
• Save time, reduce errors, and streamline service processes
• Improve rep efficiency & time to resolution
• And more!

Get started free