Generative AI in Customer Support: Use Cases and Benefits
Prashanth Kancherla
Mar 13, 2025 | 15 mins read
Customers expect instant, accurate, and personalized responses—whether through chat, email, or phone. They want quick resolutions without repeating themselves and seamless experiences across channels.
For businesses, meeting these expectations is costly and complex, especially at scale. Generative AI (GenAI) offers a way to bridge this gap by automating repetitive tasks, providing real-time insights, and assisting agents with faster and smarter responses. Let’s explore how it’s reshaping customer service.
What Is Customer Obsession?
Customer obsession is about placing your customers at the core of every decision you make. It goes beyond simply satisfying their needs—it’s about actively working to improve their experience at every touchpoint, anticipating what they want, and addressing their concerns before they even raise them.
Benefits of Implementing Generative AI in Customer Support
While AI cannot fully replace human agents, it can take over repetitive tasks, help process large amounts of data, and assist with real-time responses. Here are some key benefits of integrating generative AI into your customer support operations.
Faster Response Times and Higher Productivity
Support teams often struggle with long wait times and high volumes of repetitive queries. Generative AI can significantly reduce response times by instantly generating replies, categorizing tickets, and suggesting solutions based on past interactions. AI-powered assistants can also help human agents by drafting responses, generating scripts, and summarizing conversations—allowing them to focus on more complex issues.
Personalized Customer Interactions
AI-powered support systems can analyze customer data, such as past purchases, browsing behavior, and previous interactions, to personalize responses. This helps create a better customer experience by providing relevant recommendations, tailored solutions, and proactive assistance.
For instance, if a customer frequently buys a particular type of product, AI can suggest complementary items or notify them about special discounts. This level of personalization can improve customer satisfaction and drive repeat business.
24/7 Real-Time Support
Hiring a global support team to provide round-the-clock assistance is expensive and difficult to manage. Generative AI chatbots and virtual assistants can handle customer inquiries outside business hours, offering instant support across multiple channels, including email, chat, and social media.
Even if AI cannot fully resolve an issue, it can collect details and escalate the ticket to a human agent, reducing the time needed for resolution when an agent becomes available. This allows businesses to provide real-time support without significantly increasing costs.
Lower Support Costs
Hiring, training, and retaining customer support agents can be expensive, especially with high attrition rates in the industry. AI reduces the need for additional hiring by automating repetitive tasks and assisting agents in handling queries more efficiently.
According to industry estimates, training a new support agent can cost thousands of dollars. In contrast, AI-powered solutions require minimal setup costs and can be scaled easily. AI also reduces the reliance on outsourcing, which can be costly and difficult to manage.
Consistent and Accurate Responses
Support quality often varies between agents due to differences in experience and training. Generative AI ensures that every customer receives accurate and consistent information, reducing the risk of miscommunication. AI models are trained on company policies, product details, and past interactions, allowing them to generate standardized responses that align with brand guidelines.
Faster Ticket Resolution Through Automation
Support teams deal with a high volume of repetitive queries, such as password resets, refund requests, and order tracking. AI can handle these straightforward requests instantly, reducing the workload on human agents. For more complex issues, AI can categorize tickets, extract key details, and suggest relevant solutions—helping agents resolve cases faster.
Better Agent Support and Reduced Burnout
Customer support roles often come with high stress due to repetitive tasks, long hours, and dealing with frustrated customers. AI-powered tools can reduce agent burnout by handling routine queries, allowing agents to focus on meaningful, high-value interactions that require human judgment.
AI also assists agents by summarizing conversations, providing suggested replies, and retrieving relevant customer information instantly. This reduces cognitive load and makes it easier for agents to provide efficient and empathetic support.
11 Ways to Use Generative AI in Customer Service
Beyond simple automation, AI can help personalize experiences, improve response times, and support agents in real time. This means that implementing Gen AI strategically can have a significant impact on your organization’s customer satisfaction and operational efficiency.
Here’s how you can apply Generative AI in customer service:
#1: Advanced Sentiment Analysis
AI-powered sentiment analysis helps businesses gauge customer emotions in real-time, allowing them to respond appropriately. By analyzing tone, speech patterns, and word choices, AI can classify interactions as positive, neutral, or negative. This enables support teams to prioritize critical issues, improve agent training, and enhance overall customer satisfaction.
How to Implement:
- Use AI-driven speech analytics to analyze customer sentiment across voice and text channels.
- Monitor sentiment trends over time to identify recurring issues and improve service strategies.
- Integrate with CRM systems to personalize interactions based on historical sentiment data.
- Automate escalation processes for negative interactions, ensuring prompt resolution.
Example:
The National Health Authority (NHA) of India, serving 550 million citizens, used Ozonetel’s AI-driven sentiment analysis to improve call center efficiency. The AI analyzed 122,000+ hours of conversations across 11 vernacular languages to monitor call quality, identify agent training needs, and boost first contact resolution. It also saved supervisors 30 hours per month on call analysis, allowing them to focus on improving service quality.
Read the entire case study here!
#2: Digital Self-Service
Customers expect quick and accurate responses. AI-powered chatbots help by resolving common issues instantly, reducing agent workload while improving service availability. A chatbot can handle multiple requests at once, reducing wait times and keeping customers engaged.
How to Implement:
- Set up AI chatbots on WhatsApp, websites, and apps to assist customers 24/7.
- Advanced chatbots understand queries in detail and provide relevant solutions instead of generic replies.
- For complex queries, the chatbot should transfer the conversation to a human agent without losing context.
Example:
An e-commerce brand can introduce a chatbot to handle common customer queries, such as order tracking, refunds, and product availability. Initially, customers had to call support for every issue, leading to long wait times. After deploying the chatbot, most routine queries were resolved instantly, while only complex cases were passed to agents. This reduced customer frustration and allowed support teams to focus on high-priority issues.
#3: Voice of Customer
Customer conversations contain valuable feedback that can help you identify service gaps, common complaints, and potential improvements. Instead of manually analyzing reviews and surveys, AI-powered Voice of Customer (VoC) tools extract insights from real-time interactions.
How to Implement:
- Monitor sentiment across interactions using AI tools that analyze tone, keywords, and common phrases to detect dissatisfaction.
- Track trends like product complaints, refund requests, or late deliveries.
- Use insights to improve policies, update FAQs, or train agents accordingly.
Example:
Big Basket used AI-powered VoC analysis to track real-time conversations. They identified frequent complaints about leaking milk packets and late deliveries. Addressing these issues improved supply chain efficiency, increased CSAT by 15%, and led to 5% additional revenue growth.
#4: Vernacular Support
Expanding customer support to multiple languages is essential for businesses serving diverse regions. Generative AI can bridge language gaps by offering real-time translations, localized responses, and multilingual chatbot support. This makes it easier for customers to communicate in their preferred language, improving engagement and trust.
How to Implement:
- Deploy AI models trained on regional dialects to ensure accurate translations.
- Use voice AI to support speech-based interactions in multiple languages.
- Integrate AI-powered chatbots that can detect a customer’s language preference automatically.
Example:
A telecom provider in India can use an AI chatbot that understands and responds in over 10 regional languages. When a customer types in Hindi, the bot recognizes the language and continues the conversation in Hindi without needing manual selection.
#5: Social Media Engagement
Customers expect quick responses on platforms like Twitter, Facebook, and Instagram. AI-powered bots can monitor social media mentions, respond to common queries, and escalate critical issues to human agents. This helps businesses maintain brand reputation and engage with customers in real time.
How to Implement:
- Use AI to detect sentiment in customer comments and prioritize urgent concerns.
- Automate responses for frequently asked questions while ensuring a smooth handover to agents when needed.
- Track customer feedback trends using AI-based analytics.
Example:
An e-commerce brand can use AI to scan customer complaints on Twitter. If a customer tweets about a delayed order, the AI bot automatically responds with the latest tracking details and offers assistance, ensuring the issue is addressed quickly.
#6: SWOT Analysis
A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis helps you understand why customers choose your brand, where you need improvements, and what risks to address. AI-driven SWOT analysis eliminates guesswork by analyzing actual customer conversations to provide unbiased insights.
How to Implement:
- Identify strengths and weaknesses using AI that will highlight what customers appreciate and what frustrates them.
- Find gaps competitors are missing and detect emerging issues before they escalate.
- Improve product offerings, update marketing strategies, and refine customer support based on real insights.
Example:
A subscription-based fitness app might want to understand why users were canceling memberships. By running an AI-powered SWOT analysis, they found that users struggled with inconsistent workout plans and a lack of personalized recommendations. They introduced customized fitness journeys and real-time coaching, leading to higher retention and more returning customers.
#7: Dynamic AI Chatbots
Traditional chatbots follow predefined scripts, but generative AI chatbots can handle more complex conversations, adapting responses based on customer input. They provide a more human-like experience and can assist with troubleshooting, order tracking, and product inquiries.
How to Implement:
- Train chatbots using past customer interactions to improve response accuracy.
- Enable bots to collect context from previous chats to provide personalized solutions.
- Use AI chatbots as the first point of contact, escalating complex cases to human agents when needed.
Example:
A travel agency can deploy an AI chatbot that helps customers modify bookings. If a customer wants to change their flight, the bot retrieves booking details, offers rescheduling options, and confirms changes—all without agent intervention.
#8: Agent Training
Your agents play a critical role in shaping customer experiences. Without proper training, they struggle to handle complex queries, leading to delayed resolutions and frustrated customers. Instead of relying on outdated training programs, you can leverage AI-powered coaching that adapts to real-time customer interactions.
How to Implement:
- AI tools analyze conversations and provide immediate suggestions, helping agents refine their responses.
- AI tracks agent performance and highlights areas where additional training is needed.
- Deploy AI-powered simulations that help agents practice handling different customer queries.
Example:
A travel agency might see that its support agents were struggling to handle last-minute booking changes. By using an AI-driven coaching system, agents received real-time response suggestions based on customer sentiment. Over time, they became more confident in handling urgent modifications, leading to faster resolutions and improved customer satisfaction.
#9: Knowledge Base Generation
AI can analyze past support interactions and generate comprehensive help articles, FAQs, and troubleshooting guides. This reduces dependency on agents by making self-service options more effective.
How to Implement:
- Use AI to scan customer queries and identify common issues that require detailed guides.
- Automate the creation of FAQ pages based on trending support tickets.
- Continuously update the knowledge base with AI-generated content based on customer needs.
Example:
A software company can use AI to track recurring customer issues and generate troubleshooting steps. When users report a login error, the system automatically adds a step-by-step guide to the help center, reducing incoming support requests.
#10: Product Recommendations
AI can analyze customer behavior, purchase history, and preferences to suggest relevant products or services. This not only improves customer experience but also boosts sales and retention.
How to Implement:
- Use AI to analyze past purchases and browsing history to make real-time recommendations.
- Personalize product suggestions in chat interactions, emails, and support conversations.
- Implement AI-driven cross-selling and upselling strategies in support chats.
Example:
A streaming service can use AI to recommend movies based on a user’s watch history. If a customer contacts support about their subscription, the AI assistant also suggests new releases they might enjoy, increasing engagement.
#11: Post-Call Processing
Call center agents spend significant time summarizing calls, logging details, and updating CRM (Customer Relationship Management) records. AI can automate this process, allowing agents to focus on customer interactions rather than administrative work.
How to Implement:
- Deploy AI to transcribe and summarize calls in real time.
- Automate data entry into CRM systems to reduce manual work.
- Use AI-generated summaries to improve follow-ups and resolution tracking.
Example:
A financial services company uses AI to generate call summaries after every customer support interaction. Instead of agents manually writing notes, the AI extracts key details—such as issue type, resolution, and next steps—saving time and ensuring accurate record-keeping.
GenAI: A New Era of Customer Service
From automating routine interactions to assisting agents with real-time insights, AI is helping businesses provide faster, more personalized support at scale. But AI alone isn’t the solution; the real impact comes from integrating it seamlessly with human expertise.
With platforms like Ozonetel, businesses can implement AI-powered solutions that enhance agent productivity, improve customer satisfaction, and streamline support operations—all without compromising on the human touch.
Chatbot QA Checklist
Ensure your AI assistant delivers exceptional customer experiences with this comprehensive quality assurance framework.
1. Fundamental Testing Areas
Intent Recognition Accuracy
- Test coverage for primary customer intents
- Edge case handling for similar or overlapping intents
- Ability to understand variations in phrasing
Conversation Flow Logic
- Coherent multi-turn conversations
- Appropriate handling of context switching
- Recovery mechanisms for conversation breakdowns
Response Quality
- Accuracy of information provided
- Consistency across similar questions
- Appropriate tone and brand voice adherence
Error Handling
- Graceful responses to user errors
- Appropriate fallbacks for out-of-scope queries
- Escalation paths when needed
2. Technical Performance
Response Time
- Latency measurement across different query types
- Performance under high volume
- Consistency of response times
Integration Testing
- Data passing to/from backend systems
- Authentication processes
- API connection reliability
Cross-Platform Compatibility
- Consistent performance across web, mobile, and messaging platforms
- Adaptation to different screen sizes and interfaces
3. User Experience
Accessibility
- Screen reader compatibility
- Keyboard navigation support
- Compliance with WCAG guidelines
Personalization
- Appropriate use of user data
- Contextually relevant suggestions
- Memory of previous interactions
Customer Satisfaction
- Task completion rates
- User feedback collection mechanisms
- Sentiment analysis of interactions
4. Pre-Launch Final Verification
Security & Privacy
- Data handling practices
- PII protection protocols
- Compliance with relevant regulations (GDPR, CCPA, etc.)
Load Testing
- Performance under peak traffic conditions
- Scalability verification
- Recovery from system interruptions
Analytics Implementation
- Proper event tracking setup
- Conversation analysis capabilities
- Performance dashboard functionality
Conclusion: Ensuring High-Quality Chatbot Performance with Ozonetel
A well-tested chatbot reduces customer frustration, improves engagement, and ensures that automation actually enhances user experience rather than hinders it. However, maintaining chatbot quality requires continuous testing, optimization, and monitoring.
With Ozonetel, you get a robust chatbot QA framework that helps detect errors, optimize conversations, and deliver accurate, fast, and human-like interactions—ensuring your chatbot is always at its best.
How Industries Use In-App Calling
In-app calling is widely used across various industries to improve communication, provide better services, and create a more engaging user experience. Here’s how different industries are making the most of this feature:
- Social and Dating Apps: In-app calling in social and dating apps allows users to make voice or video calls without leaving the app. This helps build stronger connections and keeps users engaged. It adds a personal touch to online conversations, making users feel more comfortable and encouraging longer interaction times, which leads to higher retention rates.
- Gaming: In multiplayer games, in-app calling enables real-time communication among players, which is essential for coordinating strategies and building team dynamics. Whether it’s voice or video calling, players can stay connected and interact with each other directly within the game, creating a more engaging and social gaming experience.
- On-Demand Services: Rideshare and food delivery services use in-app calling to allow users to contact drivers or delivery personnel easily. This can help resolve issues like location problems or specific delivery requests without needing external communication tools.
- Marketplaces: In-app calling in online marketplaces allows sellers to offer live product demonstrations or answer customer queries through voice or video calls. This creates a more interactive shopping experience, allowing customers to make more informed decisions.
- Telehealth and Telemedicine: Patients can use voice or video calls to speak with doctors, get diagnoses, and even receive follow-up care. This allows people to access healthcare services from home, reducing the need for in-person visits and improving convenience.
- Education: In educational apps, in-app calling enables real-time communication between students and instructors. This can be used to answer questions, conduct virtual office hours, or offer personalized tutoring.
- Professional Services: For businesses offering professional services, in-app calling helps establish a direct line of communication with clients. From consultations and project updates to addressing concerns, this feature provides a convenient and efficient way to stay in touch without relying on emails or phone calls outside the app.
How Can Ozonetel Help in Implementing the In-App Calling
You can enhance the in-app calling experience with Ozonetel CXI Switch, which enables businesses to power instant voice and digital conversations with customers over the internet. It features a plug-and-play widget that seamlessly integrates with apps, websites, online ads, newsletters, in-store displays, digital kiosks, and other touchpoints, ensuring hassle-free communication.
It is basically routing engine at the core of the oneCXi platform that routes conversations to the appropriate technology platform (CCaaS, UCaaS, or CPaaS) based on their context.
5X Higher Brand Engagement
Connect with customers instantly through any channel. When it’s easier to reach you, customers engage more often, building stronger connections with your brand.
4X Higher Conversions
Reach customers at the perfect moment. When your team can respond instantly, with the right context, sales conversations become more successful.
3X Increase in Customer Lifetime Value
Solve problems faster and keep customers happier. Quick responses and better support mean customers stay longer and spend more on your brand.
Real-World Examples of Exceptional EX
Now, let’s look at some real-world examples that show how organizations are fostering environments where employees can thrive, with a focus on personal growth, open communication, and adaptability.
Cisco
Cisco fosters exceptional employee experiences (EX) by prioritizing self-directed learning, career development, and performance management. The company invests in employee growth through training, with an average of 8.6 hours spent on learning per full-time employee in fiscal 2023.
Moreover, Cisco empowers employees to explore different roles and develop new skills, while also ensuring teams are aligned and supported through regular feedback and agile performance management.
Microsoft
The company uses Microsoft Viva Insights to provide personalized recommendations that help employees improve their work habits, manage stress, and achieve better work-life balance. This platform also supports managers by offering data-driven insights to improve team dynamics and leadership practices.
As “Customer Zero” for its own products, Microsoft collaborates closely with the Viva Insights team, using internal feedback to refine features and ensure the tool meets employee needs. With a focus on privacy, inclusion, and adaptability, Microsoft is creating a thriving, engaged workforce through data-backed solutions and continuous improvement.
Salesforce
With 94% of employees willing to go the extra mile and 92% feeling productive in a flexible environment, Salesforce’s approach is clearly effective. The company also established an EX team dedicated to enhancing employee engagement by using data to address challenges and support employees in balancing work and personal life. This focus on connection, flexibility, and data-driven improvements has helped Salesforce create a thriving, inclusive workforce.
Alphabet Inc., the parent company of Google, is a prime example of exceptional employee experience (EX). Known for its innovative workplace policies, the company prioritizes employee well-being by offering flexible schedules, mental health support, and generous parental leave for its US-based employees. These benefits help foster a strong work-life balance, supporting both personal and professional growth.
NVIDIA
NVIDIA supports professional growth with robust training programs, workshops, and mentoring opportunities, ensuring employees have clear paths for career advancement. The company also promotes a culture of innovation through initiatives like the NVIDIA Inception Program and maintains an inclusive environment with Employee Resource Groups (ERGs).
Additionally, NVIDIA prioritizes employee well-being with comprehensive health benefits, mental health support, and flexible work arrangements, creating a supportive and balanced workplace for its employees.
How Ozonetel Helps Improve EX
With Ozonetel, you can empower your employees to manage high call volumes, address customer needs effectively, and maintain productivity, whether they work on-site or remotely. Here’s how Ozonetel helps enhance employee experience across industries:
Monitoring Call Quality Boosting Agent Productivity & Prioritizing High-Intent Leads
Ozonetel’s advanced tools for call quality monitoring and intelligent lead prioritization ensure that your agents stay productive while maintaining high customer satisfaction. For example, a stockbroking company partnered with Ozonetel to help with the fluctuating call volumes. The result?
- 40,000+ calls handled daily with up to 1,340 calls managed concurrently
- 80% First Contact Resolution (FCR)
- 25% reduction in average handle time (AHT)
- Advanced Tools to Facilitate Remote Working Stockbroking
Apart from managing call volumes, Ozonetel provides you with tools like real-time dashboards and remote agent features that allow supervisors to maintain productivity effortlessly.
For example, a leading stockbroking firm transitioned to a fully virtual contact center ensuring uninterrupted service with Ozonetel’s platform. The result?
- 650+ agents managed remotely, handling over 1 lakh minutes of calls daily.
- 60% increase in CSAT (Customer Satisfaction Score)
- 40% reduction in agent attrition
Improve the Performance of 900+ Healthcare Advisors
For large-scale initiatives like PM-JAY, managing the performance of healthcare advisors while ensuring beneficiary satisfaction requires advanced analytics and monitoring. Ozonetel’s AI-based tools simplify supervision and enhance advisor performance. The results?
- 900 advisors monitored daily using AI-driven speech analytics.
- 122,000+ hours of conversations analyzed across 11 vernacular languages.
- 30 hours saved per supervisor per month through automated quality audits.
Conclusion
When EX is strong, employees are engaged, motivated, and less likely to leave, which means less turnover, more productivity, and a culture people want to be part of. Leaders who invest in EX aren’t just keeping up with trends; they’re actively setting their companies up for a competitive edge in attracting and keeping top talent.
Take a close look at your current EX. Where are the gaps? What could be done better? Small improvements today can create a lasting impact tomorrow. A positive employee experience doesn’t just improve work for your team—it drives real results that lift the whole organization. So, let’s get moving on building an EX that truly shines.
Start your EX journey today by conducting employee surveys, implementing flexible work arrangements, and investing in employee development.
Elevate your customer support with Generative AI—Boost efficiency, enhance CX, and drive faster resolutions.
Frequently Asked Questions
To ensure a chatbot functions correctly, QA involves several steps:
- Define use cases and objectives: Determine what the chatbot is supposed to do (e.g., handle customer inquiries and process orders).
- Create a test plan: Outline different scenarios, edge cases, and expected responses.
- Perform functional testing: Check whether the chatbot correctly interprets and responds to queries.
- Test with real users: Deploy a minimum viable product (MVP) and monitor real interactions for errors.
- Automate QA checks: Use AI-driven tools to monitor accuracy, sentiment, and conversation flow.
- Continuously improve: Regularly analyze chatbot interactions and update responses based on user behavior.
AI-driven QA tools like Ozonetel’s CXi agents can automate this process by monitoring live interactions and optimizing chatbot responses dynamically.
A chatbot’s quality is measured using key performance indicators (KPIs), such as:
- Response accuracy: Percentage of chatbot answers that are correct and relevant.
- Resolution rate: Percentage of queries resolved without human intervention.
- User satisfaction (CSAT): Customer rating of chatbot interactions (e.g., 1–5 stars).
- FCR (First Contact Resolution): Percentage of queries resolved in a single interaction.
- Fallback rate: Frequency at which the chatbot fails to understand or respond correctly.
There are four main types of chatbots based on their capabilities:
- Rule-based chatbots: Follow predefined scripts and decision trees (e.g., answering FAQs).
- AI-powered chatbots: Use machine learning (ML) and natural language processing (NLP) to understand intent and context.
- Hybrid chatbots: Combine rule-based flows with AI capabilities for better flexibility.
- Voice-enabled chatbots: Process and respond to voice commands using speech recognition.
AI enhances QA by:
- Automating chatbot testing: AI-driven tools simulate user queries and analyze responses.
- Identifying incorrect responses: AI can detect errors, inconsistencies, or irrelevant answers.
- Monitoring real-time interactions: AI agents analyze sentiment and user behavior to refine chatbot responses.
- Self-learning improvements: AI-powered systems adapt based on conversation history and improve accuracy over time.
