ChatGPT for Customer Service: Top 10 Use Cases
ChatGPT has become one of the most talked-about AI tools in business. While it’s primarily known for text generation, companies are finding practical ways to use it in customer service – with varying degrees of success.
Here’s what actually works, what doesn’t, and how businesses are implementing ChatGPT to handle customer support.
10 Use Cases of ChatGPT for Customer Service
1- Automated 24/7 Customer Support
ChatGPT can answer customer questions at 3 AM just as easily as at 3 PM. This matters for businesses with international customers or anyone who’s ever gotten a support request at midnight.
The AI handles common questions – password resets, order status checks, return policies – without human intervention. This frees up your support team to deal with problems that actually need human judgment.
Real example: Intercom implemented AI-powered responses that handle 33% of support conversations without human help. Their “Fin” AI agent resolves straightforward queries instantly, while routing complicated issues to human agents.1
2- Multilingual support
ChatGPT can switch between languages mid-conversation. A customer writes in Spanish, gets an answer in Spanish, then switches to English – the AI keeps up without missing context.
This is cheaper than hiring multilingual support staff and faster than using human translators. But it’s not perfect – the AI sometimes misses cultural nuances or uses awkward phrasing.
Real example: Zendesk implemented AI agents that automatically detect the language of incoming customer messages and respond in the same language. Their system supports over 40 languages and can handle customers who switch languages mid-conversation.
For instance, a customer might start a conversation in German asking about a product feature, then switch to English when discussing technical specifications. Zendesk’s AI maintains context throughout the conversation regardless of language changes.
3- Sentiment analysis
ChatGPT can analyze message tone to flag frustrated customers before they explode. When someone writes “I’ve been waiting THREE WEEKS for a response,” the AI recognizes the anger and can either escalate immediately or adjust its response accordingly.
This works for spotting patterns, too. If 50 customers complain about shipping delays in one week, you’ll know before it becomes a social media crisis.
Here is an example using GAN-based models for synthetic text generation:
Real Life Example: Instacart
Instacart, a popular online grocery delivery platform, integrated OpenAI’s ChatGPT to improve its users’ shopping experience. The AI tool was utilized to address a broad range of customer service inquiries, including product availability and nutritional information, and it even provided meal planning suggestions based on available items.
For instance, if a user asked for meal ideas, ChatGPT for customer service could recommend recipes using products available on Instacart, making the shopping process more engaging and personalized. The AI’s ability to quickly and accurately respond helped reduce the load on human customer service agents, enabling faster resolution of customer issues and providing a more seamless experience.
According to their blog, this integration increased customer satisfaction, as users could get instant answers to their queries and a more personalized experience. It also helped the company manage large customer inquiries more efficiently, especially during peak times.2
Check out our article on the top sentiment analysis tools and a buyer’s guide, if interested.
4- Personalized responses
Instead of treating every customer the same, ChatGPT can pull from past interactions, purchase history, and preferences to customize responses.
Someone who bought running shoes three months ago gets different product recommendations than someone who just browsed once. A customer who’s contacted support five times about the same issue gets routed to a senior agent automatically.
This requires integrating ChatGPT with your CRM, order management system, and support database. That’s not a simple afternoon project.
Real Life Example: Octane AI
Octane AI helps Shopify stores create personalized shopping quizzes using ChatGPT. When customers answer questions like “What’s your skin type?” or “What’s your fitness goal?”, the AI recommends specific products based on their answers combined with browsing history.
5- Quick responses to customer inquiries & complaints
When someone’s upset, waiting 24 hours for a response makes things worse. ChatGPT can acknowledge complaints immediately, provide status updates, and offer initial solutions while a human agent reviews the case.
The AI won’t fix every problem, but it prevents the “nobody’s listening” feeling that escalates minor issues into major ones.
Real Life Example: Shopify
Freshworks integrated GPT into its customer service platform. When a customer complains about a billing error, the AI immediately confirms receipt of the complaint, explains the next steps, provides a ticket number, and estimates the response time. For straightforward billing issues (wrong charge amount, duplicate charges), it can process refunds automatically up to a certain threshold.
6- Creating customer emails
ChatGPT can draft follow-up emails, order confirmations, apology messages, and update notifications. Feed it customer details and the situation, and it generates appropriate email text.
If a customer has recently experienced an issue with a product or service, ChatGPT can generate an email that addresses their concerns and offers potential solutions (see Figure 3).
Use case examples:
- “Your order shipped” emails with tracking links and delivery estimates
- Apology emails when something goes wrong, adjusted based on issue severity
- Proactive updates when delays occur, before customers ask
7- Replying to customer reviews
Negative reviews need responses, but writing individual replies for hundreds of reviews eats up time. ChatGPT can generate responses that address specific complaints while maintaining your brand voice.
Real Life Example: Reputation.com
Reputation.com uses AI to help multi-location businesses respond to reviews at scale. Their system analyzes each review, identifies the primary complaint or compliment, and drafts a response that addresses the specific issue.
8- Answering FAQs
Every business gets asked the same questions repeatedly. “What’s your return policy?” “Do you ship internationally?” “How do I reset my password?”
ChatGPT can handle these instantly by pulling from your FAQ database, help docs, or knowledge base. No human needed.
Real Life Example: Notion
Notion trained an AI assistant on their entire help documentation, community forum posts, and internal knowledge base. When users ask “How do I share a page with someone outside my workspace?”, the AI provides step-by-step instructions with screenshots, links to relevant docs, and even suggests related features they might find helpful.
9- Internal Support for Customer Service Teams
ChatGPT doesn’t just help customers – it helps the people assisting customers to.
Support agents can ask the AI to find policy information quickly, summarize long customer histories, suggest response templates, or explain technical issues in simple terms.
How this helps:
- Complex technical issues get translated into customer-friendly language
- New employees get up to speed faster
- Agents spend less time searching internal docs
- Response quality stays consistent across the team
Real Life Example: Salesforce
Salesforce built “Einstein GPT” for its Service Cloud. When an agent gets a complex ticket, they can ask the AI: “What’s our policy on refunds for enterprise customers?” or “Summarize this customer’s past 10 interactions.” The AI pulls information instantly from their knowledge base, case history, and policy docs.
10- Feedback Collection and Analysis
After resolving an issue, ChatGPT can follow up with conversational surveys that feel less robotic than traditional forms.
Instead of “Rate your experience 1-10,” it asks “How did we do?” and can follow up based on the response. If someone says “It was okay but took too long,” it might ask “What would’ve made it faster?”
The AI then analyzes responses to identify patterns. If 30 customers mention the confusing checkout process this week, that insight surfaces automatically.
Real Example: Forethought
Forethought’s AI analyzes support ticket resolution and automatically sends personalized follow-up messages. If a customer had a shipping issue, it asks specifically about the delivery experience. If they had a technical problem, it confirms the fix worked. The system then categorizes feedback by theme (shipping, product quality, website bugs) and generates weekly reports showing trends.
Benefits of Using ChatGPT in the Customer Service Industry
Handles Repetitive Work Automatically
Password resets, order tracking, basic troubleshooting – these tasks don’t require human intelligence, just accurate information delivered quickly. ChatGPT handles them reliably, freeing your team to focus on cases that need problem-solving skills.
The math: If each simple inquiry takes 5 minutes and you get 100 per day, that’s 8+ hours of staff time. Automate those, and your team can focus on the 20 complex cases that actually need their expertise.
Always Available
Customers don’t only have problems during business hours. ChatGPT doesn’t sleep, take breaks, or need vacation days.
Reality check: “24/7 support” doesn’t mean 24/7 problem resolution. The AI can handle straightforward issues anytime, but complex problems still wait for business hours unless you staff human agents around the clock.
Scales Without Linear Costs
Hiring scales linearly: 2x the inquiries means 2x the staff. ChatGPT scales differently – handling 1,000 conversations costs marginally more than handling 100.
The caveat: Initial setup, training, and integration costs can be substantial. This makes more sense for businesses with high inquiry volumes than small operations with 20 tickets per week.
Key challenges using ChatGPT for customer service and mitigation strategies
Effective ChatGPT implementation raises several key issues, including:
- Hallucinations: One of the biggest challenges is preventing ChatGPT from producing false or misleading responses to customer questions. The AI may provide incorrect pricing, policy details, or product specifics if proper safeguards and training are not implemented.
- Technical integration complexity: It requires significant development expertise and resources to integrate ChatGPT into existing websites, develop custom widgets, connect to CRM systems, and ensure smooth handoffs to human agents.
- Training and fine-tuning: Achieving accurate responses demands a deep understanding of company-specific data, regulations, and procedures, which can be both time-consuming and technically challenging.
- Cost and resource management: Using ChatGPT for customer support at scale can be expensive.
Ready-to-deploy alternatives
To mitigate the challenges of developing a custom ChatGPT integration, which might not be practical or cost-effective for some companies, especially small and medium-sized ones, ready-to-use alternatives are available. These options enable businesses to access many of the features discussed earlier, such as scalability, automated responses, and 24/7 availability, without facing the difficulties of integrating ChatGPT.
Real-life example: Tidio
Tidio provides companies with a comprehensive customer care solution that addresses many of the challenges of adopting ChatGPT by combining AI-powered chatbots with human support capabilities:
- Tidio features pre-built website widgets that are quick to set up.
- The platform uses controlled AI responses that are specially trained for customer service scenarios, which helps prevent hallucinations.
- It offers a hybrid approach that blends AI responses with handoffs to human agents.
- Companies lacking in-house technical expertise can still deploy advanced chatbot features using no-code flows.
FAQ: ChatGPT for Customer Service
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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