Support teams often deal with long queues, repetitive tickets, and frustrated customers waiting for answers. How to use AI in customer service comes down to automating simple tasks, personalizing responses, and being available 24/7. AI helps you give customers faster support and frees agents for the work that needs a human touch.
In this article, we’ll cover:
- What is AI in customer service?
- Its benefits
- How companies use AI
- Examples across industries
- Risks and challenges to look out for
- Best practices and top tools to consider
What is AI in customer service?
AI in customer service is using machine learning and natural language processing to handle support requests across chat, email, voice, and other channels. It can answer common questions, assist agents with replies, or even act on behalf of customers, like updating records or booking appointments.
Teams often call it AI-powered customer service because the system interprets the intent and adapts its answers.
Earlier, AI customer support consisted of basic chatbots that relied on keywords or rigid flows. They could only manage simple FAQs, and often frustrated the customers when questions didn’t match their programmed paths.
Over time, these tools evolved into virtual assistants and now into more advanced AI agents that can take multi-step actions, integrate with CRMs, and summarize interactions for human agents.
Benefits of AI in customer service
AI enables faster replies, lower costs, and more consistent experiences. Businesses use artificial intelligence customer service tools to improve both customer satisfaction and internal efficiency.
Here are a few benefits:
- Faster response times: AI agents and chatbots can reply instantly to common questions, so customers no longer wait in long queues. This reduces pressure on human agents, who can then focus on more complex issues.
- 24/7 availability: Unlike human teams, AI customer support tools work around the clock, covering nights, weekends, and holidays without extra staffing.
- Personalization: By pulling context from CRMs, order systems, or past tickets, it can tailor replies to the individual. This makes interactions feel relevant rather than generic.
- Cost savings and efficiency: With automation handling a significant portion of requests, companies reduce their cost per contact and scale operations without hiring aggressively.
- Accuracy: AI reduces human error and engages consistently with customers across channels. This matters most in industries like finance, healthcare, or retail, where accurate information builds trust.
10 practical ways to use AI in customer service
There are many applications of AI for customer service, but the most valuable use cases balance efficiency with better customer experiences. Below are ten proven ways teams apply AI customer support in their daily workflows.
AI-powered chatbots and virtual assistants
Chatbots can now do more than answering simple FAQs. Advanced assistants use conversational AI to authenticate users, check order status, issue refunds, and escalate when needed. They give customers instant answers and let agents focus on harder cases.
Automated ticket routing and escalation
AI automatically classifies tickets by intent, sentiment, or language. It then routes them to the right queue or agent. This speeds up first-response times and prevents bottlenecks, especially in busy AI customer service teams.
AI for customer sentiment analysis
By analyzing tone in chats, emails, or calls, AI highlights when a customer is frustrated or at risk of churn. Managers can prioritize these tickets for quick recovery and coach agents using these examples.
AI-powered self-service knowledge bases
AI improves knowledge base search by matching customer queries with relevant answers. It can also generate draft articles based on ticket trends, making customer service more scalable over time.
AI for personalized support recommendations
During live chats or emails, AI can suggest tailored responses, troubleshooting steps, or upsell options. This creates more personal interactions without slowing down response times. Salesforce and Zendesk highlight this as a top AI feature for customer service.
Predictive support
AI scans historical data and real-time usage to flag potential problems. For example, it might warn of login issues affecting multiple users. Teams can then proactively notify customers, reducing ticket spikes and improving trust.
Voice AI and speech recognition in tech support
Voice AI handles routine phone calls like appointment booking, password resets, or account checks. It also transcribes calls for record-keeping and can escalate to a human when needed. This helps businesses with high call volumes.
AI-powered email and message triage
AI sorts incoming emails and messages into categories, drafts replies, and assigns urgency levels. This keeps inboxes manageable and ensures time-sensitive issues get answered first. It’s a common feature in modern AI customer support platforms.
AI for multilingual customer service
AI translates messages in real time, bridging gaps between agents and customers who speak different languages. This extends global reach without requiring separate teams for each market.
AI for post-interaction analysis and feedback
After an interaction ends, AI can review transcripts, tag quality issues, and track recurring problems. This helps managers refine workflows and train agents, closing the loop on continuous improvement.
Let’s look at some real-world examples of these applications.
Real-world examples of AI in customer service
The best way to see the impact of AI in customer service is through companies already using it. Here are three examples:
Klarna: Containing two-thirds of chats with AI
Klarna introduced an AI assistant that now handles about two-thirds of its customer service chats, equal to the work of 700 human agents. Customers get immediate responses for common requests, while agents focus on exceptions.
DHL: Scaling voice AI for millions of calls
DHL deployed a voice AI system to manage over one million calls each month. It handles inquiries like parcel status, then transfers to agents when calls are complex. AI tech supports high-volume customer queries and can route to humans for empathy.
Zendesk AI: Structured automation with analytics
Zendesk rolled out AI agents that work across messaging, email, and forms. The Essential tier handles routine interactions, while Advanced adds conversation flows and analytics. For companies already using Zendesk, this brings faster triage and deeper insights.
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Challenges and risks of AI in customer service
Ignoring these risks of having AI in your processes can fail your rollout or frustrate the customers. Here’s what to look out for:
- Privacy and data security: AI will handle sensitive details like payment info, medical records, or account data. Companies must ensure compliance with standards such as SOC 2 or HIPAA, and choose providers that encrypt data and log activity.
- Lack of human empathy: AI customer support tools cannot replicate human understanding in emotional or high-stakes situations. Clear escalation paths to agents are necessary so that automation does not feel like a barrier.
- Over-reliance on automation: Companies that push too many interactions through bots risk frustrating customers when answers are wrong or incomplete. Balanced oversight keeps AI effective without creating friction.
- Integration complexity: AI only works well when it connects with CRMs, ticketing systems, or voice platforms. Teams should scope integrations carefully before launch.
The next step is knowing how to implement AI to support your customer service operations.
How to get started with AI in customer service
You can start implementing AI for customer service with small, well-scoped projects that prove value quickly. This roadmap can help:
- Identify repetitive tasks: Look at ticket categories with high volume but low complexity, such as order tracking, password resets, or return requests. You can automate these to deliver quick wins without major risk.
- Map current workflows: Document how requests move through your help desk, which systems are involved, and where agents spend the most time.
- Find the right tools: Once you know the workflows, choose tools that integrate with your existing stack. For example, Zendesk users can add AI agents, Salesforce customers can use Einstein and Agentforce, and Intercom users can deploy Fin.
- Start one pilot project: Define clear KPIs such as automated resolution rate, average handle time, or customer satisfaction (CSAT) on bot-handled tickets. Train agents alongside AI so they learn when to step in and how to provide feedback.
Start small, measure results, and then expand AI confidently across more customer support workflows. Next, some best practices to keep in mind.
Best practices for AI-powered customer service
Following best practices ensures automation complements people instead of replacing them. These ones will help you out:
- Maintain human oversight: AI should handle repetitive queries, but sensitive or complex cases must go to agents. Build escalation rules so customers don’t feel trapped in automation.
- Train AI on new customer data: Update knowledge bases regularly and use agent feedback to improve responses. This avoids outdated or irrelevant answers in AI customer support.
- Monitor performance and customer satisfaction: Track metrics like automated resolution rate, transfer rate, average handle time, and CSAT. Regular reviews help teams refine how artificial intelligence customer service operates day to day.
- Design collaboration between AI and humans: Use AI to summarize tickets, suggest replies, or handle triage, while agents focus on problem-solving and empathy. This blended model delivers better results than either working alone.
- Evaluate channels separately: Voice AI needs tighter testing for call transfer quality and transcription accuracy than chatbots or email triage.
With these practices in place, the next step is choosing the right tools.
Which AI tools work best for customer service
Lindy, Zendesk, Salesforce, and Intercom are some of the top tools for customer service. Choosing among them depends on your current stack, channels, and budget. Let’s see how they compare:
These options cover both AI help desk tools and flexible agent platforms. Zendesk and Salesforce suit companies that already use their ecosystems. Intercom works for teams wanting outcome-based pricing for their Fin AI solution, which charges per resolved conversation.
Lindy stands out for AI support agents that can communicate with customers via text, email, and voice, and automate cross-app business workflows.
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Try Lindy: An AI assistant to handle your customer support, and more
Lindy uses conversational AI that handles not just chat, but also lead gen, meeting notes, and customer support. It handles requests instantly and adapts to user intent with accurate replies.
Here's how Lindy adds value:
- Fast replies in your support inbox: Lindy answers customer queries in seconds, reducing wait times and missed messages.
- 24/7 agent availability for async teams: You can set Lindy agents to run 24/7 for round-the-clock support, perfect for async workflows or round-the-clock coverage.
- Support in 30+ languages: Lindy’s phone agents support over 30 languages, letting your team handle calls in new regions.
- Add Lindy to your site: Add Lindy to your site with a simple code snippet, instantly helping visitors get answers without leaving your site.
- Integrates with your tools: Lindy integrates with tools like Stripe and Intercom, helping you connect your workflows without extra setup.
- Handles high-volume requests without slowdown: Lindy handles any volume of requests and even teams up with other instances to tackle the most demanding scenarios.
- Lindy does more than chat: There’s a huge variety of Lindy automations, from content creation to coding. Check out the full Lindy templates list.
Try Lindy free and automate your first 40 tasks today.
Frequently asked questions
What are the benefits of AI in customer support?
Faster responses, 24/7 coverage, lower costs, and more consistent experiences are some of the benefits of AI in customer service. It also helps agents work on complex cases instead of repetitive ones.
Can AI replace human customer service agents?
No, AI cannot replace human agents. AI customer support handles repetitive requests, drafts replies, and assists agents, but humans are still essential for sensitive or complex inquiries.
What is the best AI tool for customer service?
Zendesk, Salesforce, Intercom, and Lindy are some of the best tools for AI customer service. Zendesk fits Zendesk users, Salesforce suits enterprises, Intercom works well for messenger-first teams, and Lindy offers multi-channel automation.
How does AI improve personalization in support?
AI improves personalization by pulling customer history and context from CRMs. This allows you to tailor responses instead of giving generic answers.
What challenges come with using AI in customer service?
Data privacy, lack of empathy, over-reliance on bots, and integration complexity are some of the challenges of AI in customer service.
How do I start using AI in my customer service team?
You start by automating repetitive tasks, choosing tools that fit your systems, and running a pilot project. Expand after measuring results.
Is AI customer service cost-effective for small businesses?
Yes, AI customer service is cost-effective for small businesses when it deflects a large share of requests. Intercom’s per-resolution model is an example of usage-based pricing.
Can AI handle multilingual customer support?
Yes, AI can handle multilingual support through translation and localized knowledge bases. Complex issues may still need native speakers.
What’s the difference between AI chatbots and AI customer agents?
AI chatbots answer simple questions. AI customer agents go further by completing actions, updating systems, and escalating with context.
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