Examples of AI in Customer Service Today, Sure to Grow CX

Modern customer service is instantaneous and personalized to suit customers’ expectations.

This is a gift of artificial intelligence (AI) to businesses striving to meet customers’ demands at scale. According to Nextiva’s CX trends report, 92% of companies have adopted AI to some degree in 2025, at least with simple, standalone solutions or pilots.

The use of AI is constantly on the rise. If you’re not using it to ramp up your customer service operations, you might leave opportunities on the table.

This article will cover different examples of customer service AI that you can take inspiration from. Here’s how businesses are putting AI into action:

9 Examples of AI-Powered Customer Service

Below are some valuable ways to leverage AI to make customer service remarkable.

1. AI-powered chatbots and virtual assistants

This is the most common example of AI usage. The AI chatbots and virtual assistants allow businesses to automate responses to simple questions and perform easy tasks.

In a business setting, AI helps:

• Respond to FAQs: Questions like “Where’s my order?” or “How can I cancel my subscription?” may not require the expertise of a rep. An AI chatbot easily automates them. The remaining critical inquiries are smoothly handed over to an agent for prioritized assistance.
• Pull data: AI-powered customer service chatbots integrate with CRMs and other customer data platforms. They aggregate previous interactions to study context and personalize replies accordingly.
• Implement handoffs: AI intelligently decides which questions might be too complex to answer and escalates them to a human agent with the whole conversation history.

Nextiva’s CX trends report for leaders shows that 98% of CX leaders recognize the need for seamless AI-to-human transitions, but only 10% have implemented these handoffs without a struggle.

Chatbot and virtual assistant use case example:

Tata Play, India’s leading content distribution platform, wanted to serve its massive customer base and offer 24/7 support in 14 languages. Using a legacy support platform created significant challenges for the Tata team. They leveraged Nextiva’s platform to combine AI and WhatsApp, creating a delightful customer experience at a reduced cost.

Here’s the impact of Nextiva’s AI bot and its Social & Reputation Management:

They added WhatsApp and an AI bot to their system, which helped them handle over 5 million customer requests automatically.

On the retail side, a fashion retailer, Motel Rocks, uses Zendesk’s AI chatbot to automate responses to 43% of income tickets. It has helped the company triple the self-service rate, around a 206% increase, and cut ticket volume in half:

This increased their customer satisfaction (CSAT) score by 9.4%.

2. Conversational AI for natural interactions

Conversational AI adds sentiment and contextual understanding to an interaction while using natural language processing and machine learning. It can easily engage in two-way dialogues, not just if-then logic.

It handles complex questions with layered follow-ups. The AI can pick up intent from a conversation and dynamically adapt responses with the help of its machine learning algorithms.

Conversational AI use case examples

Bank of America’s virtual assistant Erica (built with advanced NLP) manages over 2 million customer interactions per day. Erica has fielded over 2 billion chats since its launch, answering 98% of routine questions in under 44 seconds.

Similarly, Camping World, a retail RV and boating business, deployed IBM’s Watson Assistant as Arvee, a digital representative interacting via phone and web. Within months, Camping World saw a 40% increase in customer engagement and a 33% increase in agent efficiency. Here’s an overview of Arvee’s impact:

As a result, the average wait time has plunged to just 33 seconds since Arvee was deployed. Beyond the benefits reported, conversational AI also strongly influences UX, making conversations more fluid. Since the bot can absorb the context, fewer customer queries get categorized as complex, lowering the need for frequent live agent interaction.

3. Agent assist tools

Agent assist tools recommend a response to a customer inquiry based on similar past tickets. It auto-populates case notes and next steps while picking up relevant knowledge-base articles.

AI helps analyze customer history and prompts agents to remind them about upsell possibilities. Thus, it streamlines their approach to customer interactions.

Agent assist tools use case example

Telstra, Australia’s largest telecom, built “Ask Telstra” on Microsoft’s Azure OpenAI service to help support agents look up information in seconds. This tool scans massive internal knowledge bases and calls histories to give agents one-sentence summaries and recommended solutions.

Telstra reported this impact:

Beyond the impact observed by Telstra, there are different proven benefits of Agent Assist Tools, including:

• Faster onboarding for new agents: Agents leverage the AI assist feature to ramp up on existing knowledge in past resolutions or knowledge bases.
• Lower average handle time: Agents don’t have to search for relevant knowledge manually; they have it on their screen. It speeds up resolutions.
• More first-call resolutions: Agents have all the information available through AI assist, which helps them give helpful resolutions.

4. Customer sentiment analysis

Sentiment analysis allows an agent to work in real time and tweak their interaction based on the customer’s emotional tone. If the interaction shows that a customer is angry, agents try to show them the positive side of being patient for a permanent resolution.

If the sentiment analysis functionality flags a ticket for faster handling, it’s prioritized, facilitating fast resolution.

Sentiment analysis use case example

Let’s use the same Motel Rocks example. Zendesk AI assigns an emoticon (happy/sad) to each chat request. If a frustrated customer comes in, it’s automatically reflected in real time. This allows the team to recognize customers at risk and engage them as a priority.

The result was a 9.44% lift in CSAT.

Sentiment analysis helps reduce churn while improving customer trust and brand sentiments. Your CX team benefits largely as they get to operate with emotional intelligence.

5. Predictive analytics for proactive support

Predictive analytics in customer service refers to using historical data to forecast future events and optimize actions. Common examples include call volume forecasting and churn prediction. AI helps identify common issues in the lifecycle stages.

It uses the past to predict the customers at risk and the most suitable action to offer a resolution. This allows your call center or contact center team to reach out proactively.

Predictive analytics for a proactive support use case example

One analytics vendor reports a deployment where predictive churn insights improved customer retention by 70%. The vendor enabled timely and personalized retention campaigns.

Advanced predictive modeling can cut average handle times by up to 40% and reduce escalations by 5–20%. It reduces response time and automatically translates into millions of cost savings per center. Most importantly, this turns reactive support into proactive service for your support team.

6. AI-powered ticket routing

An AI platform ensures tickets are routed to the right person based on their context, previous interactions, and other parameters. The platform categorizes tickets based on topic, sentiment, and urgency and assigns them to the right department.

With every routing decision, AI technology improves and becomes more accurate.

AI-powered ticket routing example

A Nucleus Research study observed that AI-enhanced routing and case management increased admins’ productivity and led to more ticket resolution. This is critical since 27% of customers report a lack of effectiveness as the top frustration with customer service reps..

Here’s the overview of what Nucleus observed in their research:

In addition to the benefits observed, intelligent routing also influences customer experience and satisfaction. Customers get faster resolutions.

7. Email response automation

Email remains a core support channel for many businesses. AI accelerates email support in two ways: auto-generating responses and automated triage.

Generative AI tools (like GPT-based assistants) draft human-like email replies for routine customer issues, which agents can review. This dramatically cuts agent workload. These tools also offer suggestions for knowledge-base articles or step-by-step instructions that customer service representatives can use.

Email response automation use case example.

A global beauty brand used IntouchCX’s Sidd Pro, an AI-powered email assistant, to increase its email processing productivity by 49.3%.

After implementation, CSAT improved by 10%, as customers got faster, more consistent replies. Here’s an overview of the impact that the brand observed:

On a broader scale, AI in email response automation develops consistency and agents’ capacity without increasing the headcount.

8. Knowledge base optimization

AI supports knowledge base optimization by monitoring failed service attempts and recommending new additions. The AI picks up the context from these attempts to draft complete articles using generative AI.

Businesses that already use AI to communicate with customers reap the following benefits for reduced manual workload:

• Keep the knowledge base updated: Since knowledge is constantly updated with new service issues, the self-service rate increases.
• Reduce dependency on agents: AI takes care of the updates without manual upkeep, allowing agents to focus on assisting customers with critical queries.

9. AI call summarization and post-call intelligence

This is a popular use case in which AI takes notes on behalf of agents and analyzes them in real time. Agents spend roughly 10% of call time on handwritten notes. Speech-to-text and summarization engines help companies reclaim this time and improve data accuracy.

The AI tool transcribes calls and summarizes key takeaways, reducing agents’ manual administrative work. It auto-tags calls based on their tone and resolution status, keeping them on the priority list if required. This data is later fed to the CRM and other reporting tools.

AI call summarization and post-call intelligence example

Companies like ASAPP have demonstrated that removing manual disposition notes improves efficiency and customer service experience. Here’s an overview of their findings:

Why You Need AI in Your Customer Service Function

Below are a few reasons why using AI in customer service isn’t just a “nice-to-have” and why it’s become a must-have for businesses to remain competitive. These are based on the insights observed in the above section. Here’s why AI is no longer optional, but essential:

Reduced operational costs

AI excels at automating high-volume, repetitive tasks, including triaging emails, routing tickets, and responding to FAQs. For example, AI-powered companies have deflected up to 43% of incoming tickets.

It significantly reduces the cost per contact. The result is leaner operations that maintain quality while serving more customers with fewer resources.

Improved customer satisfaction

With AI-enabled systems working 24/7, customers no longer have to wait for business hours to get help. AI reduces friction and delivers timely, consistent resolutions. Brands like Motel Rocks and Bank of America have seen CSAT scores jump by 9% or more.

Self-service also empowers customers to find answers on their terms, leading to more satisfying experiences.

Increased agent productivity

AI doesn’t just serve customers; it supports customer service agents, too. It handles repetitive queries and surfaces relevant knowledge in real time so agents can focus on higher-value, complex issues.

Companies like Telstra have reported 20% reductions in repeat calls after adopting agent assist tools. This translates into faster resolution, shorter average handling times, and a more engaged, effective AI customer service team, as reported on Microsoft’s site:

Improved accuracy and personalization

AI systems continuously learn from customer data to create responses and suggest relevant content. This allows you to personalize AI responses at scale according to customer preferences and feedback.

This personalization makes every customer conversation more meaningful. For customers, it feels like talking to a brand that truly understands their needs, leading to stronger loyalty and higher lifetime value.

Proactive service at scale

Perhaps AI’s most strategic advantage is its ability to anticipate problems before they occur. It analyzes user behavior and service patterns to trigger proactive interventions. For example, updating a customer about a delay and letting them know you’re working on it when it’s taking a long time to resolve.

This shift from reactive to predictive customer support transforms the entire service model, helping brands build trust and prevent churn.

Improve Your Customer Service With AI Solutions From Nextiva

Based on the examples above, AI is necessary to transform a reactive customer service approach into a proactive one. It also has several other advantages in customer experience, CSAT, agent efficiency, and critical KPIs.

Nextiva offers an all-in-one, AI-first CX platform with chat, voice, SMS, social media, and email in one dashboard. Some companies use 6.3 separate CX tools for different purposes and needs. The Nextiva platform caters to various use cases through a single unified interface.

It offers a no-code setup to help launch AI tools without developers. Rather than a generic model, you get pre-trained models based on your conversations with customers.

Elevate your customer service experience. See how Nextiva’s all-in-one, AI-first CX platform can help you. Get a demo.