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Artificial intelligence in customer service has moved from a nice-to-have to a core expectation for modern support teams. AI in customer service is no longer just about basic chatbots; it is about AI customer service automations that can deflect tickets, assist agents, surface customer insights, and personalize every interaction at scale.

AI and customer service tools now help teams respond faster, keep costs under control, and make data-driven decisions about where to invest in the customer experience.

This guide breaks down the benefits of AI in customer service, how AI customer service software works, which tools to consider, and how to use AI responsibly to support both customers and agents.

Table of Contents

• What is AI in customer service software?
• AI Customer Service Tools: A Comparison
• 13 Customer Service Tools With AI
• AI in Customer Service
• Benefits of AI in Customer Service
• Top Ways Customer Service Teams Use AI [New Data]
• How to Use AI in Customer Service
• Factors to Consider Before Implementing AI for Customer Service
• Examples of How to Use AI in Customer Service
• Frequently Asked Questions About How to Use AI in Customer Service

What is AI in customer service software?

AI in customer service software refers to the use of artificial intelligence inside help desks, CRMs, and support tools to automate routine tasks, assist agents, and deliver faster, more personalized support across channels.

At a basic level, AI for customer service uses machine learning, natural language processing, and generative AI to understand customer intent and respond with relevant actions or content. 贬耻产厂辫辞迟’蝉 State of Service Report found that 86% of CRM leaders say AI helps make customer interactions feel more personalized — a key reason why 84% view AI as essential for engaging today’s customers.

Artificial intelligence for customer service can show up in many places: AI-powered chatbots that resolve simple requests, AI agents that route tickets and suggest responses, AI that summarizes conversations, and AI that identifies customer patterns and churn risk from large volumes of interaction data.

When AI is built into customer service software instead of bolted on, support teams get consistent, omnichannel experiences instead of fragmented point solutions.

AI Customer Service Tools: A Comparison

The chart below compares leading customer service tools with AI capabilities based on key features, pricing, and “best for” guidance. With 75% of CRM leaders reporting that AI reduces response times, tool selection becomes critical for balancing automation with customer satisfaction.

Note: Always check each provider’s pricing page for current details; many tools use tiered, per-seat, or usage-based pricing.

13 Customer Service Tools With AI

Modern support teams have more options than ever when it comes to AI customer service software, from all-in-one platforms to specialized automation tools. Read on for 贬耻产厂辫辞迟’蝉 list of the top 13 tools to satisfy teams of all sizes.

1.

Best for: Growing companies that want an all-in-one, AI-powered customer service platform connected to their CRM.

HubSpot Service Hub is an AI-powered customer service platform built on 贬耻产厂辫辞迟’蝉 , so every support interaction ties back to a complete customer record.

It helps teams scale 65% more efficiently than adding staff, combining omnichannel shared inbox, AI-powered ticketing, knowledge base, customer feedback, and robust analytics in one CRM-connected workspace, which helps teams reduce response times and improve customer retention while maintaining a single source of truth

Key capabilities include:

• Unified workspace. Omnichannel shared inbox for email, chat, forms, and social that lets agents collaborate on conversations and see full customer history without switching tools.
• AI-powered ticketing. Automatic ticket creation, routing, and classification based on topic, priority, and customer profile, plus AI-assisted replies and summaries to speed up handle times.
• ?Knowledge base and self-service. Integrated knowledge base for help articles and FAQs that can power both human-assisted and AI-assisted support experiences.
• Customer feedback and insights. CSAT, NPS, and customer feedback tools that are tied directly to tickets and contacts, enabling closed-loop follow-up processes.
• Reporting and analytics. Dashboards for SLAs, response and resolution times, conversation volume, and agent performance, all connected to CRM data for deeper insight into customer health.

What Stands Out

Service Hub’s biggest strength is how deeply it connects service data with CRM data, letting teams see the full customer journey — from marketing and sales to support — in one timeline instead of separate systems.

Another standout is that its AI capabilities are embedded directly into everyday workflows (like routing, summarizing, and suggesting replies), which can make it easier for teams to adopt AI without overhauling their existing processes.

Pricing: Free tier available. Starter: $9 per seat/month. Professional: $90 per seat/month. Enterprise: $150 per seat/month.

2.

Best for: Teams that want AI to resolve a large share of repetitive tickets while keeping humans focused on complex work.

is 贬耻产厂辫辞迟’蝉 AI customer service agent that autonomously handles common requests and escalates complex issues to human reps with full context.

It uses generative AI, knowledge base content, and CRM data to deliver instant, consistent, and on-brand responses across channels, acting as a first line of support that scales without adding headcount. In fact, service professionals save more than 2.2 hours a day using the Breeze Customer Agent.

Key capabilities include:

• Repetitive ticket deflection. Automatic answers to FAQs like order status, password resets, billing questions, and account changes that significantly reduce ticket volume and first-response times.
• Context-aware responses. History pulled from past tickets and CRM records to tailor responses to each customer and avoid asking for the same information repeatedly.
• Graceful escalations. Hand-offs of nuanced issues to human agents with a complete conversation history and an AI-generated summary, so customers do not need to repeat themselves, and agents can move directly to problem-solving.
• On-brand messaging. Learning from approved content and knowledge base articles so automated responses remain accurate and aligned with brand voice.

What Stands Out

Breeze is particularly effective at targeting repetitive, low-complexity tickets that tend to consume a large portion of support capacity, freeing agents to focus on higher-value customer conversations.

Because Breeze is tightly integrated with Service Hub and Smart CRM, its responses can be grounded in approved knowledge base content and real customer data rather than operating as a standalone chatbot.

Pricing: Included in HubSpot Service Hub plans (usage-based limits apply).

3.

Best for: Support organizations already on Zendesk that want to add AI capabilities to a mature help desk.

Core features:

• Ticketing workspace for email, chat, phone, and social.
• AI chatbots and agents assist to handle routine queries and suggest responses.

What Stands Out

Zendesk’s AI features build on an established ticketing ecosystem, making it a pragmatic choice for teams that want to improve efficiency without replacing their existing platform.

Pricing: Starts at $55 per agent/month (Suite Team); AI add-ons extra.

4.

Best for: Enterprises standardizing on Salesforce across sales, service, and marketing.

Core features:

• Omnichannel service console integrated with Salesforce CRM.
• Einstein AI for routing, intent detection, and recommended responses.

What Stands Out

Service Cloud keeps service operations tightly aligned with broader CRM data, which is valuable for large organizations that need consistent visibility across all customer-facing functions.

Pricing: Starts at $25 per user/month (Essentials). Einstein AI: $50+ per user/month add-on.

5.

Best for: Small to midsize teams that want an approachable help desk with growing AI features.

Core features:

• Cloud-based ticketing with SLAs and multichannel support.
• Freddy AI for basic bots, suggestions, and workflow automation.

What Stands Out

Freshdesk offers an accessible entry point for teams that want straightforward ticketing and starter-level automation without a steep learning curve.

Pricing: Free tier. Growth: $15 per agent/month. Pro: $49 per agent/month. Enterprise: $79 per agent/month.

6.

Best for: Product-led companies that rely on in-app messaging and proactive outreach.

Core features:

• AI chatbots and flows embedded in web and in-app experiences.
• Unified inbox with AI assistance for support, onboarding, and lifecycle messaging.

What Stands Out

Intercom excels at meeting users inside the product, which is especially useful for SaaS businesses that want AI to power both support and engagement in the same interface.

Pricing: Starts at $74/month (flat rate). Custom pricing for larger teams.

7.

Best for: Teams looking for a configurable, no-code AI chatbot platform.

Core features:

• No-code bot builder for creating complex self-service flows.
• Integrations with CRMs and support tools for context-aware automation.

What Stands Out

Ada enables non-technical teams to design and iterate on AI-powered support journeys without relying heavily on engineering resources.

Pricing: Custom pricing (typically $1,000+/month minimum).

8.

Best for: Revenue teams that want conversational AI for website chat and lead qualification.

Core features:

• Chatbots for website conversations, lead qualification, and basic support.
• Real-time routing to sales or service based on visitor behavior.

What Stands Out

Drift excels at blending sales and support conversations on websites, reducing friction for visitors who move between buying questions and support needs.

Pricing: Starts at $2,500/month (custom quotes).

9.

Best for: B2B teams that need intelligent routing for high-value website conversations.

Core features:

• Qualified routing to sales or service queues based on visitor intent and behavior.
• Dynamic chat experiences that adapt to account type and engagement signals.

What Stands Out

Qualified is particularly strong for account-based strategies where routing high-value accounts to the right rep quickly can drive revenue impact.

Pricing: Custom pricing (typically enterprise-focused).

10.

Best for: Email-first support teams that want a clean shared inbox experience.

Core features:

• Shared inbox and assignment workflows for email conversations.
• AI-powered tagging, response suggestions, and conversation organization.

What Stands Out

Help Scout offers a streamlined experience for teams that prefer email as their primary channel but want modern collaboration features.

Pricing: Standard – $20 per seat/month. Plus – $50 per seat/month.

11.

Best for: Gmail users who want shared inbox functionality without leaving Google Workspace.

Core features:

• Shared inboxes built directly inside Gmail.
• AI suggestions for tagging, assigning, and drafting responses in email threads.

What Stands Out

Hiver's Gmail-native approach makes it ideal for teams that want to add collaboration and light AI without changing their email workflow.

Pricing: Free tier. Lite – $19 per user/month. Growth – $49 per user/month.

12.

Best for: Teams building custom voice and conversational AI experiences.

Core features:

• No-code builder for voice-enabled support flows and IVR systems.
• Integration with telephony providers and conversational platforms.

What Stands Out

Voiceflow enables non-technical teams to design sophisticated voice AI experiences without relying on engineering resources.

Pricing: Free tier. Pro – $50/month. Enterprise – Custom.

13.

Best for: Teams that need conversation intelligence across sales and service calls.

Core features:

• Call transcription and analysis for quality assurance.
• AI-powered insights on talk track, sentiment, and coaching opportunities.

What Stands Out

Gong provides deep visibility into conversation patterns, making it valuable for teams focused on improving agent performance and customer experience.

Pricing: Custom enterprise pricing (typically $100+ per user/month).

AI in Customer Service

Now that support leaders have had the chance to evaluate the above AI customer service software and tools, it’s time to understand how artificial intelligence and customer service fit together more broadly — including key benefits, use cases, and best practices.

When determining how to use AI in customer service, teams often turn to technologies such as AI agents, natural language processing (NLP), and machine learning (ML) that automate and enhance support interactions. These tools enable customer support software to understand intent, analyze large volumes of tickets, and deliver accurate responses in real time.

In particular:

• AI agents handle routine inquiries through chat, email, WhatsApp, or voice channels.
• NLP powers chatbots and virtual assistants, allowing them to interpret context and sentiment in customer conversations.
• ML algorithms detect patterns in customer behavior, predict issues, and recommend next steps to agents or customers.

Teams that learn how to use AI in customer service see faster average resolution times. Service quality also becomes more consistent.

Benefits of AI in Customer Service

When teams understand how to use AI in customer service, outcomes improve. Customers have a smoother experience. Shoppers can get around-the-clock support and faster resolutions to challenges. Internally, service teams see operational benefits, including better ticket routing workflows and increased efficiency.

Here are all of the benefits AI can unlock for your team.

??Customer Experience Benefits

贬耻产厂辫辞迟’蝉 found that 65% of service leaders expect AI to improve customer satisfaction scores within the next year. AI-driven personalization and instant self-service options help customers resolve issues on their own terms. Furthermore, AI-driven sentiment analysis helps flag negative experiences early, preventing escalation.

To illustrate it, here’s how to use AI in customer service to improve the customer experience.

Faster Response Times

Of customer success leaders, 92% claim that AI has helped improve customer service response time. This speed increases CSAT and prevents ticket backlogs. Using chatbots as an example, businesses can automatically respond to a customer‘s live chat message within seconds.

Without automation, response times stretch, and customers turn to competitors.

24/7 Availability

AI can work beyond headquarters’ time zones. Customers won’t need to wait for a human employee to respond to their inquiry. Instead, a chatbot can gather all the basic information needed to address their problem.

Personalized Experiences

Natural language processing allows AI systems to understand intent and tailor responses. According to HubSpot, 47% of teams already use AI for personalization. The payoff is higher retention and loyalty. It’s no wonder, since customers expect more personalization in interactions than ever before.

Operational Benefits

AI delivers measurable efficiency gains. Customer service leaders implement AI to scale support without increasing headcount, with 75% of service teams already using AI reporting faster ticket resolution compared to manual processes. These improvements translate directly into cost control and scalability.

??Streamlined Ticket Routing

Automated classification and routing ensure tickets reach the right rep or knowledge base in seconds. HubSpot data shows that 66% of teams using AI report improved routing accuracy. Manual routing risks misdirected tickets, wasted time, and customer frustration.

Lower Service Costs

AI enables teams to reduce customer service costs by automating FAQs, routing, and repetitive tasks. Service leaders cost savings of up to 45% within the first year of AI adoption. Without it, operational costs climb as demand outpaces staffing.

Improved Scalability

HubSpot found that CS leaders see AI as essential for scaling service without expanding headcount. For example, knowledge-base assistants reduce repetitive inquiries by suggesting help articles. Teams that delay adoption risk growth bottlenecks and reduced competitiveness.

Agent Benefits

AI also improves the agent experience, which often gets overlooked. HubSpot research shows that support reps say AI in customer service automates repetitive support tasks and reduces burnout.

Reduced Workload for Agents

AI handles repetitive tickets, cutting volumes in early implementations, and takes the load off human agents. Reps can then work on complex interactions. Ignoring this balance drives burnout and attrition.

Increased Efficiency

Real-time AI assistants suggest answers, summarize cases, and surface context. PwS’s reports that 66% of reps say AI improves their productivity. Without these tools, agents waste time digging through CRM data or knowledge base articles.

Real-Time Support and Recommendations

AI provides live guidance during conversations by surfacing next-best actions or flagging compliance risks. Customer support teams expect these tools to directly improve first-contact resolution rates.

Now, let’s review how to put some of these benefits into practice.

Top Ways Customer Service Teams Use AI [New Data]

It’s probably no surprise that AI is one of the leading priorities among success leaders. Service teams everywhere are trying to decide how to use AI in customer service. But 贬耻产厂辫辞迟’蝉 sheds new light on how AI is reshaping customer service teams.

Here are some of the highlights to know:

• 77% of customer experience teams already use AI with great results. Benefits include improved customer service response time, improved CSAT, and lower spend on customer service.
• Leaders see the future of customer service being driven by AI-powered reps.
• 72% of CS leaders say AI can provide better customer service than a human can.
• 86% of leaders using AI say it improved their ability to scale customer service operations as their company grows.

Looking for inspiration? Here are some of the highest ROI strategies for CS teams using AI:

• Using AI chatbots.
• Offering omnichannel customer support.
• Leveraging AI/automation throughout the customer experience.
• Collecting and analyzing customer feedback.
• Using generative AI for crafting communications with customers.

This is just a glimpse. Keep reading to learn practical tips for how service teams can add AI to their customer experience strategy.

How to Use AI in Customer Service

AI adoption is moving quickly, with saying their companies plan to up their AI-related budgets this year due to agentic AI.

The methods below illustrate how to use AI in customer service. That includes tips for approaching implementation in stages, starting with quick wins, moving into advanced applications, and scaling toward full transformation.

Quick Wins: Fast-Track AI Applications

Quick wins are entry points for AI customer service implementation. They require minimal integration, deliver results within weeks, and build early confidence across teams. Here’s how to use AI in customer service to unlock value faster.

1. Use AI chatbots.

AI-powered chatbots handle FAQs, track orders, and reset passwords without human intervention. Chatbots are programmed to interpret a customer’s problem and then provide troubleshooting steps to resolve the issue. This saves time for reps and customers because responses are instant, automatic, and available 24/7.

With , customer support teams report 39% faster ticket resolution versus teams not using the agent.

Tools like 贬耻产厂辫辞迟’蝉 , Intercom Fin, or Zendesk AI can be deployed in days.

Average timeline: 2–4 weeks for full deployment on web and mobile channels.

Note: 贬耻产厂辫辞迟’蝉 is a conversational CRM bot that service professionals can connect to HubSpot to maximize productivity.

2. Automate ticket routing.

AI can automatically categorize and route customer inquiries to the appropriate department or agent based on the nature of the request. This reduces wait times and ensures customers are connected with the right person to resolve their issue.

贬耻产厂辫辞迟’蝉 are practical ways to start. Workflow implementation ranges from one day to three weeks, including training on historical ticket data.

AI tools allow teams to analyze the customer’s problem, sentiment, and perceived urgency. Customer success leaders can also program AI models to tag issues for escalation.

Pro tip: Automated ticket routing means experienced agents handle complex cases. Meanwhile, newer reps deal with the simple tickets. Leaders should also consider having reps who are solely dedicated to time-sensitive requests.

3. Use AI for knowledge base management.

While building out a robust knowledge base or FAQ page can be time-consuming, self-service resources are critical. AI can help maintain and optimize a company’s knowledge base by analyzing which articles are most helpful to customers and suggesting updates to content.

Knowledge base AI can also recommend relevant articles to customers in real time, reducing the need for human intervention.

Pro tip: Use 贬耻产厂辫辞迟’蝉 to capture recurring queries from live support into draft articles for review and publication. The configuration is done within one day.

Advanced Applications: Smarter AI Capabilities

When learning how to use AI in customer service, advanced applications go beyond automation basics. They rely on deeper data and modeling, which means slightly longer timelines, but they unlock higher-value insights and personalization for both customers and agents.

4. Make sentiment analysis smart.

Today, many bots have sentiment analysis tools, like natural language processing, that help them interpret customer responses. Chatbots can then assess how to respond when a customer behaves in a certain way.

The configuration of sentiment analysis tools may take six to eight weeks. However, successful teams can opt for a simpler and faster solution, such as . These AI-powered workflows have rules to trigger human escalation when certain keywords are detected. The deployment time takes just one day.

Pro tip: Tools like in the image below can analyze CS agents’ text and tell them how their message may be perceived by their users before they hit send.

5. Leverage voice analysis.

Some customers prefer support via phone call. Voice AI has made it easier to automate call center interactions. These tools can be trained in predictive call routing and interactive voice response to serve as the first line of defense for customer inquiries.

For example, when you call your favorite company and an automated voice leads you through a series of prompts, that’s voice AI in action.

Pro tip: Use voice AI technology to resolve routine customer service tasks (like hours of operation requests and account access troubleshooting). Then, agents remain available for more complex issues.

6. Automate call transcription.

AI call transcription gives service teams a searchable, objective record of every customer conversation. Within 贬耻产厂辫辞迟’蝉 (CI), calls are automatically transcribed, summarized, and tagged for key topics. This enables faster quality reviews, targeted service agents coaching, and identification of recurring service issues.

Transcripts also support compliance and dispute resolution, since managers can reference exact wording. Success teams can even share clips or add calls to coaching playlists to create real customer scenarios for onboarding.

A full rollout of CI is typically complete within two to four weeks, depending on the number of CS agents and historical calls to process.

7. Offer multilingual support.

AI-powered chatbots and conversation tools can now detect a customer’s language, translate incoming messages in real time, and generate responses in the same language. This makes global service delivery seamless, even with limited multilingual staff.

AI also accelerates translation and localization. It produces variants of knowledge base articles and maps them to a customer’s language preference stored in the CRM. The result is a consistent, localized experience that prevents international customers from being forced through English-only help content.

Pro tip: Create language variations directly in HubSpot Service Hub, configure chat targeting, and map language preferences in .

Transformation Initiatives: Scaling AI Across Service

Transformation initiatives require deeper integrations across channels and the CRM. They take longer to deploy, but set the foundation for a fully AI-enabled customer service.

8. Provide omnichannel AI service.

AI-driven omnichannel support unifies customer conversations across email, live chat, phone, and social channels in one inbox. With 贬耻产厂辫辞迟’蝉 , every interaction is logged in the CRM, so agents see the full history and context no matter where the customer reaches out.

This approach eliminates repetitive “catch-up” questions and ensures consistent, personalized service across touchpoints. Customers can move seamlessly between channels — for example, starting in chat and finishing on email — without losing context.

To deploy a fully functioning system, prepare to invest 12 to 16 weeks, depending on the number of channels and integrations.

Pro tip: When success agents are unavailable for callbacks or chat, 诲辞苍’迟 give customers the option to select those channels. That way, customers aren’t stuck in a party-of-one conversation, wondering why the team hasn’t responded.

9. Automate data management.

Some of the most valuable AI use cases are embedded in analytics and internal workflows. AI paired with a CRM can recall customer history, flag risks, and surface engagement data for service reps.

With 贬耻产厂辫辞迟’蝉 , teams can track trends in ticket volume, CSAT, and agent performance. AI identifies the issues that drive the most cases and which customers require proactive outreach.

For example, if a loyal customer hasn’t engaged recently, AI can trigger a notification for follow-up, ensuring no relationship slips through the cracks.

Pro tip: Use AI-generated insights to identify opportunities for proactive service, like recommending content updates or creating targeted coaching for reps handling recurring issues.

10. Make the most of machine learning and predictive analytics.

Machine learning is at the heart of predicting customer behavior through AI. This technology analyzes large quantities of data and pulls out actionable insights. AI can then forecast trends, anticipate customer sentiment, and solve future problems.

Pro tip: Use predictive analytics to inform predetermined customer communication responses (e.g., via chatbots or over the phone). Machine learning can help improve the intended output based on FAQs and customer history.

Factors to Consider Before Implementing AI for Customer Service

AI delivers measurable ROI, but adoption isn’t risk-free. Service leaders often underestimate the cultural, operational, and ethical factors that determine whether implementations succeed or stall.

The following considerations outline the most common challenges when learning how to use AI in customer service.

Maintaining the Human Touch

AI automates routine interactions, but it cannot replace empathy, judgment, or nuanced communication. Over-automation risks alienating customers. For example, a client may get frustrated if they’re trapped in a chatbot loop without escalation.

Prevention:

• Set up clear rules for human escalation when AI detects negative sentiment during a conversation with an AI agent.
• Use AI for triage and FAQs to ensure high-value or sensitive cases reach human agents.

Data Privacy and Security Concerns

With access to large amounts of customer data, AI systems make privacy and compliance central concerns. Poor data governance can lead to regulatory fines (e.g., GDPR violations) or reputational damage if personal data is mishandled.

Prevention:

• Audit all AI tools for data encryption, storage practices, and compliance certifications.
• Limit access to sensitive transcripts and ensure retention policies are enforced.

Change Management

Resistance from frontline staff is one of the biggest barriers to AI customer service implementation. If reps feel AI will replace them rather than support them, adoption falters.

Prevention:

• Involve service agents early in the tool selection and rollout process.
• Position AI as an assistant that removes repetitive work, not as a replacement.
• Provide ongoing training.

Measuring Success

Define success metrics upfront, so leaders can prove ROI or secure further investment for AI tools. Don’t measure success with one metric. For example, focusing solely on ticket deflection risks overlooking improvements in CSAT or agent efficiency.

Prevention:

• Establish KPIs across customer, agent, and operational outcomes (CSAT, NPS, average handle time, cost per ticket).
• Conduct quarterly reviews to adjust workflows and retrain models where needed.

Customer Expectations and Needs

Understanding what customers actually want is critical. Deploying AI that adds friction — for example, long verification flows or irrelevant responses — can backfire.

Prevention:

• Map the AI customer journey before the rollout.
• Survey customers to understand channel preferences.

Quality of Data

AI is only as good as the data it’s trained on. If ticket data is inconsistent, outdated, or siloed, AI recommendations will be inaccurate.

Prevention:

• Run regular CRM hygiene audits to clean and de-duplicate records.
• Standardize tagging and categorization in service systems.

Feedback and Monitoring

AI isn’t a “set-and-forget” tool. Models degrade over time as customer language, product lines, and service expectations evolve. Without systematic monitoring, response accuracy drops, bots deliver irrelevant suggestions, and customers lose trust.

For instance, chatbots trained on outdated FAQs may give incorrect information that will lead to customer frustration.

Prevention:

• Establish a monitoring routine: weekly checks for chatbot responses and monthly audits of routing accuracy.
• Involve both agents and customers in the loop: Gather frontline input on where AI helps or fails, and survey customers to gauge their satisfaction with AI-driven interactions.
• Create a closed feedback loop. Retrain models with new tickets, update knowledge base content monthly, and flag misclassifications for continuous improvement.

Examples of How to Use AI in Customer Service

Delta Airlines

in a variety of ways to make customer interactions with the company “more intuitive, simpler, and less complex for customers.” I use their virtual assistant often to help change flights, monitor flight changes, and process refunds when needed.

Eric Phillips, the chief digital officer, are creating a better customer experience at the company. It goes far beyond just chatbots. AI is helping the company in all parts of its business, both internally and customer-facing.

For example, Delta is using AI to parse through to help with reservation inquiries and pricing. This act alone could help increase the total value of the company by 2%.

The company sets a great example for how the customer service experience and employee experience work together.

“It’s not just revenue, and it’s not just efficiency. All those things have to come together. In the end, investing in systems has to make the employee experience better when they’re addressing and interacting with customers. That makes the customer experience better. The revenue play is: ‘I had a good experience, I’m coming back,’” Phillips says.

What you can learn: of Delta’s CX is to communicate to its customers that “we know you, and we value your time.” By applying these same principles and using AI to deliver efficient customer service, you’ll be sure to make a positive impression on your customers.

惭补肠测’蝉

惭补肠测’蝉 is another company that has found a unique way to incorporate AI into its customer service offerings.

Through cognitive AI technology, 惭补肠测’蝉 helps its clients navigate its stores and locate associates in a much faster way. This smartphone-based assistant, “惭补肠测’蝉 on Call,” can offer customers personalized answers to their queries. It can help them find certain products or brands in a store and help them find any services or facilities that they’re looking for.

What you can learn: Use AI to handle the most routine inquiries that you receive so that your human assistants can dedicate more time to solving complex issues.

Netflix’s Machine Learning Strategy

Netflix’s use of to curate personalized recommendations for its viewers is pretty well known.

The real value that AI plays here is being able to analyze mass sums of data and use that information to curate a unique customer experience. tracks viewing habits, ratings, searches, and time spent on the platform to serve viewers content that they’re most likely to enjoy.

The streaming giant is also using AI in a variety of ways to enhance the customer experience, from chatbots to steady streaming.

What I like: I’m guilty of my fair share of Netflix binges. What I love most about Netflix is that I feel like the platform sometimes knows me better than I know myself. I save a ton of time not having to browse through endless choices, and instead most often choose something off my “Recommended list.”

As you can tell below, I’m a sucker for a female-led drama. Overall, this creates such a positive experience for me that I’m much more likely to return to Netflix instead of perusing a variety of other streaming services.

What you can learn: Again, personalization is key. How can you create an experience for customers where it feels like you know them? How can you anticipate their preferences and concerns and curate a unique experience around that?

Kaplan’s Service Evolution

Kaplan Early Learning’s support desk was stretched thin as ticket volume climbed across phone, email, and chat. The team needed a quick scale without sacrificing consistency.

Kaplan centralized on HubSpot Service Hub and , such as:

• An AI customer agent for common questions.
• Reply recommendations in the Help Desk.
• Early sentiment analysis.
• Transcripts and AI-assisted summaries.

Within weeks, low-complexity inquiries shifted to self-service, and agents refocused on nuanced issues. Measurable changes followed: 25–30% of inquiries resolved via AI chat, ~30% faster average response times (even as volume grew), and a 63% year-over-year improvement in customer service staff retention.

What you can learn: Why Kaplan achieved such impressive results is that they fixed the foundation first. Kaplan unified channels and history in the CRM, then layered automation on top. They chose quick-win intents (order status, billing, basic product questions) and set clear escalation to humans.

Payoneer

Payoneer’s global support team uses AI-driven sentiment analysis to escalate angry chats to a human agent. When frustration surfaces — repeated escalation requests, negative language, or urgency cues — the bot skips decision trees and routes the conversation straight to a live agent.

The system also passes along a condensed transcript so the human sees context at a glance. Within this flow, sensitive cases move faster, and agents need not re-ask verification questions.

What you can learn: Build escalation logic first, not last. Define the sentiment signals, set clear thresholds for handoff, and ensure transcripts/summaries travel with the case.

Road

Road’s 20-person support team was fielding ~10,000 conversations a month across a fast-growing EV-charging customer base of 150,000+. Volume outpaced capacity, but leadership refused to trade speed for culture or quality.

They (clear subheads, simpler language), then put Intercom’s Fin AI Agent in front as first-line support. Fin resolved repetitive questions and, crucially, followed explicit guidance rules — when customers asked for phone support, Fin handed off immediately to a human.

A daily review loop lets agents flag shaky answers and fix the source (KB article, snippet, or rule) the same day.

Within weeks, the team scaled without adding headcount. Their effort resulted in 150% ROI, 20%+ lift in Fin CSAT, and inbox load down by 70%.

What you can learn: Treat the help center as AI training data: Structure it first, then automate. Bake “human now” intents into guidance rules to avoid dead ends. Close the loop daily.

Frequently Asked Questions About How to Use AI in Customer Service

1. How can we use AI in customer service?

AI augments core workflows such as:

• Self-service.
• Case triage and routing.
• Knowledge retrieval.
• Sentiment detection.
• Reply assistance.
• Post-interaction summaries.
• Forecasting.

Start with low-risk automation (FAQs, order status), then extend to sentiment-based escalation. Use to add AI chat for self-service, automated ticket routing, and an AI-searchable knowledge base.

2. What are the best AI tools for customer service?

Top categories of AI tools in customer service are:

• AI chat/agents (deflection, intake).
• Help-desk copilots (drafts, summaries).
• Voice intelligence (transcription, coaching).
• Knowledge AI (semantic search, article suggestions).

Choose tools that integrate natively with the business’s CRM and support guardrails, auditability, and handoff logic. For example, HubSpot Service Hub centralizes channels in a shared inbox, layers AI chat and reply suggestions, and auto-links every conversation to CRM records.

3. Can I use ChatGPT or generative AI for customer support?

Yes, within guardrails. Use gen-AI for drafting replies, summarizing long threads, re-writing in brand tone, and proposing next-best actions. Keep humans in the loop for refunds, policy exceptions, compliance, and edge cases. Feed models approved knowledge only.

Built-in AI in Service Hub drafts responses from knowledge base/CRM context, logs summaries to the ticket, and routes sensitive intents to humans. Admin controls, permissions, and content sources keep responses on-policy.

4. Is AI good at customer service?

AI is effective at speed, consistency, and scale. The best outcomes pair automation for repetitive tasks with clear escalation to people for nuanced issues. Configure chatbots to handle FAQs and escalate on sentiment or intent. Customer success teams can do so in HubSpot Service Hub.

5. What’s the ROI of implementing AI in customer service?

ROI comes from ticket deflection, faster handling, fewer transfers, and better agent throughput. Second-order gains include improved retention and upsell from faster, more accurate support. Typical payback windows depend on volume and content quality.

For instance, Kaplan achieved an average response time that was ~30% faster (even as volume increased) within weeks after implementing HubSpot tools. Plus, the team had a 63% year-over-year improvement in customer service staff retention.

6. What are the risks of using AI in customer service, and how can I mitigate them?

Key risks include:

• Wrong or outdated answers.
• Over-automation without human escape hatches.
• Bias in training data.
• Privacy/compliance gaps.
• Change-management failure.

Mitigate these risks with approved knowledge sources, escalation rules, and agent training. Success leaders can achieve all with HubSpot Service Hub and 贬耻产厂辫辞迟’蝉 Conversation Intelligence for targeted quality assurance and coaching.

7. How do I get my team to adopt AI in customer service?

To get teams involved in the AI process, work with agents to craft intent lists, macros, and bot replies. Position AI as a copilot that removes repetitive work. Publish clear “human-now” rules, and recognize wins with data (e.g., minutes saved, escalations avoided). Provide ongoing training using real transcripts.

8. How complex is it to integrate AI with my existing customer service systems?

Complexity depends on a team’s channel mix, CRM hygiene, and authorization flows. Low-code deployments (chat, routing, summaries) typically take weeks. Voice/calling systems take longer because they require call recording permissions, user provisioning, and data mapping for transcripts and summaries.

Customer service leaders often choose HubSpot Service Hub for its native AI capabilities across chat, email, and calling, with a CRM-first configuration to reduce integration overhead and speed time to value.

The Future of AI in Customer Service

AI in customer service uses technologies like chatbots and predictive analytics to automate support.

To get started, identify quick-win use cases (like chatbots and automated routing), select the right AI tools, and plan for change management and data privacy. Even little changes will bring a high ROI to teams and ensure that happy customers keep returning.

Transform your team with the HubSpot Breeze AI Suite, which enables end-to-end customer service automation.

Editor's note: This article was originally published in April 2023 and has since been updated for comprehensiveness.

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