12 Real-World Applications of AI in Customer Support

Artificial Intelligence in customer service isn't a far-off promise—it's a practical tool that teams are using right now to solve real problems. From slashing response times to personalizing support at scale, AI helps businesses free up their agents for the high-impact work that matters most.

But how does it work beyond the hype? In this guide, we break down 12 real-world applications of AI in customer support, complete with case studies showing you exactly how brands use it to boost efficiency and create better experiences.

1. AI-Powered Chatbots and Virtual Assistants

AI-powered chatbots and virtual assistants are intelligent software programs designed to simulate human conversation with customers in real time. 

These systems use Natural Language Processing (NLP) and Machine Learning (ML) algorithms to understand the intent behind a customer’s message, respond accurately, and improve over time. 

Some advanced versions even use Conversational AI, enabling them to remember past interactions, personalize responses, and assist in multi-turn conversations. 

Virtual assistants can:

• Answer FAQs across multiple languages.
• Handle transactional actions like checking order status or updating billing info.
• Integrate with CRMs and ticketing systems.
• Learn from historical tickets to improve future responses.

How it works: 

• Let’s say a customer lands on your website and initiates a chat. 
• The AI assistant can instantly greet them, recognize what they’re asking (e.g., “I need help with a refund” or “Do you integrate with Salesforce?”), and respond with accurate, context-aware answers. 
• If the query is too complex or sensitive, it can escalate the conversation to a live agent with the full context carried over. 

Here’s an example of my conversation with Air France’s virtual assistant for a flight change request via Apple’s iMessage: 

Case Study: How Amtrak’s “Julie” Reduced Average Handling Times

Amtrak, the U.S. national passenger railroad service, launched Julie, an AI-powered virtual assistant, to handle customer inquiries ranging from booking tickets to checking train schedules. 

Julie operates across Amtrak’s website and phone system, using NLP to interpret voice and text inputs in real time. 

Impact: 

Julie handled over 5 million customer requests in a single year, significantly reducing the load on Amtrak’s human agents. She achieved a 25% increase in self-service bookings and reduced average handling time across the board [*]. 

More importantly, Julie helped Amtrak lower operational costs while maintaining a high level of customer satisfaction—even during peak travel seasons when call volume typically spikes.

2. Voice Assistants and IVR Enhancements

AI-powered voice assistants are intelligent systems that enable customers to interact with a company’s services or support team using natural spoken language. 

These assistants use Automatic Speech Recognition (ASR) to convert spoken words into text, Natural Language Understanding (NLU) to interpret meaning, and Text-to-Speech (TTS) to respond verbally. 

Voice assistants can: 

• Resolve common issues like password resets, account updates, or order tracking entirely over voice. 
• Transcribe and summarize calls in real time for agent coaching or escalation.
• Understand and speak multiple languages or regional accents. 

How it works: 

• When a customer calls your support line, a voice assistant greets them and listens for their query. 
• It transcribes the audio, identifies intent using NLP, pulls relevant information from your systems (like a CRM or knowledge base), and responds with a clear, human-like voice. 

These systems are often integrated with backend tools for real-time data lookups, routing, and escalation, allowing for more intelligent and responsive customer support.

Case Study: How Bank of America’s “Erica” Handles Over 2 Million Requests Daily

Bank of America, one of the largest financial institutions in the U.S., launched Erica, a virtual financial assistant, to help customers manage everyday banking needs, from transaction insights to bill payments—and even the occasional joke. 

Erica is available 24/7 through the bank’s mobile app and has been trained using advanced AI and natural language processing to interpret voice, text, and tap commands. 

For example, customers can speak naturally to Erica, asking questions like “How much did I spend on groceries last week?” or “Pay my credit card bill tomorrow.” 

Impact: 

Erica has helped Bank of America streamline customer service operations and scale financial guidance while keeping support personal and fast. On average, 78% of clients get their questions resolved within 41 seconds, boosting both operational efficiency and client satisfaction [*]. 

Additionally, Erica’s AI has undergone 60,000 updates to stay relevant, conversational, and more effective. The assistant has even exchanged 400,000 jokes with clients and sent 7,000+ birthday wishes, strengthening emotional connection with users.

3. Predictive Analytics

Predictive analytics in customer service uses machine learning, data mining, and historical behavioral patterns to forecast what a customer is likely to do next. 

With these insights, customer service teams can:

• Predict when a customer might churn
• Flag accounts likely to escalate based on past interactions
• Forecast what kind of help a user will need based on their customer journey
• Identify which support channels they’re most likely to use

How it works: 

• AI systems ingest data from multiple sources. This includes customer support tickets, CRM interactions, product usage logs, past complaints, purchase history, and even sentiment from past conversations. 
• The AI models analyze these patterns and assign a “likelihood score” to key events (e.g., the likelihood a user will cancel their account, submit a ticket, or escalate a complaint).

Case Study: How HopSkipDrive Unified Its CX Team With Kustomer

HopSkipDrive, a tech-enabled rideshare service for children, launched Kustomer to help its community experience team streamline support across three key groups: CareDrivers, parents, and schools. 

The company needed a better way to unify customer data and ensure safe, timely communication across its complex service network. 

Kustomer provided a single, omnichannel timeline view, enabling the team to resolve complex issues faster, surface insights more easily, and deliver better support experiences with less agent effort.

Impact: 

Kustomer empowered HopSkipDrive to coordinate over 850,000 safe rides for children while improving agent productivity and communication transparency across the board. 

Key outcomes include:

• Faster resolution times thanks to a unified timeline that linked all conversation history, making follow-ups easier and eliminating duplicate work.
• Improved analytics enabled the team to access key metrics like CSAT and First Contact Resolution, previously unavailable to them.
• Community happiness increased, as the platform helped resolve issues in one thread, reduced delays, and allowed smoother coordination across users. 

4. Agent Assist Tools

Agent Assist Tools, sometimes referred to as AI-powered co-pilots or real-time guidance systems, are AI tools that support customer service agents during live interactions. 

Unlike bots that handle customer interactions autonomously, Agent Assist tools augment human agents by providing them with intelligent suggestions, contextual knowledge, and next-best actions during a conversation. 

How it works: 

• As an agent chats with a customer or handles a live call, the AI listens in (text or voice). 
• Next, it identifies the issue being discussed, and pulls up suggested knowledge base articles, policy documents, previous ticket history, or pre-drafted replies. 
• Some platforms even go further by flagging emotional cues (e.g., customer is frustrated), compliance risks, or upsell opportunities. 

Case Study: How a Healthcare Giant Automated 40% of Inbound Calls With Google CCAI and CDW

A leading U.S. healthcare organization with 18,000 employees and $3.5 billion in revenue turned to Google Cloud CCAI and CDW to address poor service quality, long queue times, high call abandonment, and limited staff during the COVID-19 pandemic. 

Their goal was to streamline inbound call handling and enhance patient experience.

Together, Google CCAI and CDW launched an AI-powered virtual agent integrated into their contact center system to triage, route, and respond to patient queries across departments. 

Impact: 

Within just three weeks, the healthcare organization launched an MVP virtual agent that automated over 40% of inbound calls, routing patients to the appropriate medical facility and department. It also reduced wait times and abandonment rates, improving accessibility during periods of high demand [*]. 

5. Self-Service Portals

Self-service portals are online support environments where customers can find answers and solve problems without speaking to a human agent. 

With AI integrated, these portals become dynamic, intelligent, and context-aware. They can now personalize content, recommend answers based on user behavior, and guide users through resolutions.

How it works: 

• When a user logs into the portal or starts typing a question, the AI instantly analyzes their profile, previous interactions, and current query. 
• It then uses NLP and machine learning to surface the most relevant solutions.
  • Often, this is based on results that helped other users in similar situations. 
• Some even feature guided troubleshooting flows, chat-like experiences, or integrations with community forums and knowledge bases. 

Case Study: How Everlane Increased Deflections 4x and Boosted Agent Efficiency

Everlane, a modern clothing retailer known for its transparent pricing and ethical supply chain, partnered with Kustomer to overcome rising ticket volumes, siloed communication, and inconsistent reporting. 

The company needed a platform that could empower agents, unify conversations, and automate responses at scale. 

Kustomer delivered a single timeline view, integrated tools, and advanced AI capabilities that helped the Everlane team proactively address customer needs before they even reached out.

Impact: 

With Kustomer in place, Everlane had a 400% increase in deflection rate using automated and intelligent self-service. They also recorded a 25% increase in agent productivity, thanks to the unified timeline and reduced context-switching. 

According to Ashley Monganaro, Director of CX at Everlane— 

“Now we are proactive in answering customer questions before they even need to reach out to our team.”

6. Sentiment Analysis

Sentiment analysis is the use of NLP and machine learning algorithms to determine the emotional tone behind a customer’s message (whether positive, negative, or neutral). 

It works by scanning the text (and sometimes voice tone) of emails, chat messages, social media posts, and call transcripts to understand how a customer feels. 

How it works: 

• You start with a piece of text that you want to analyze.
  • Example: “I love the new update, but the app still crashes sometimes.”
• The system cleans the text to remove noise. E.g., break it into individual words (tokenization). 
• It then converts the words into numbers so a computer can understand them.
  • Uses methods like Bag of Words, TF-IDF, or word embeddings (like BERT). 
• Next, it runs the numbers through a model trained to recognize customer sentiment and checks for patterns in the words. This is to identify whether the tone is positive, negative, or neutral. For example:
  • “love the new update” → Positive
  • “app still crashes” → Negative
• Finally, it combines all the information and gives you a final sentiment.
  • Result: Mixed/Neutral
  • Score: 60% Positive, 40% Negative

Case Study: How a Leading Bank Used Sentiment Analysis to Improve NPS and Customer Satisfaction

A major retail bank partnered with Repustate to analyze and improve customer experience across thousands of online reviews and survey responses. 

Faced with low Net Promoter Scores (NPS), rising customer complaints, and poor in-branch service perception, the bank needed a scalable way to understand sentiment across all customer feedback channels. 

Repustate’s sentiment analysis platform enabled the bank to extract insights from unstructured data at scale, detect pain points, and measure satisfaction trends over time.

Impact: 

The bank uncovered top frustration areas like long wait times during lunch hours and limited teller availability. Also boosted NPS by identifying negative sentiment trends and resolving issues in real-time [*]. 

7. Automated Ticketing & Routing

Automated ticketing and routing is an AI-driven system that uses machine learning, NLP, and business logic models to automatically classify incoming inquiries, generate support tickets, and direct them to the right teams or agents. 

How it works: 

• AI reads and analyzes incoming messages. 
• It identifies the intent (e.g., billing issue, technical problem, order inquiry), urgency, and sometimes even sentiment. 
• It then applies a set of smart rules or learning-based models to:
  • Create a ticket in your helpdesk
  • Auto-tag and classify the ticket
  • Assign it to the right team, agent, or queue
  • Set the appropriate SLA priority level based on context

Case Study: How Bulletproof Cut Handle Times by 50%

Bulletproof, a premium wellness brand, needed a support platform that could scale with its rapid eCommerce growth while offering seamless routing and omnichannel service. 

The company chose Kustomer to help reduce inbound request volumes, automate workflows, and enhance First Contact Resolution (FCR).

Impact: 

Kustomer helped Bulletproof’s customer care team hit new performance highs:

• 50% reduction in handle times through automated routing and unified views. 
• 15% increase in FCR thanks to better workflows and smarter issue classification. 

Michael Callahan, VP of Customer Experience, shared:

“Previously, customers would call when they had a bad delivery experience. Then we would look up the account. Then we would look at their order… so we’re altering customer behavior by driving more to these automated mobile experiences.”

8. Personalized Recommendations

Personalized recommendations in customer service refer to AI systems that analyze user behavior, preferences, past interactions, and contextual data to proactively suggest products, solutions, or next best actions tailored to each customer. 

How it works: 

• AI algorithms analyze data from CRM records, previous support tickets, product usage, purchase history, or even in-app behavior. 
• Based on this, they surface personalized suggestions directly in the chatbot interface, customer portal, or agent dashboard. 
• The more data the system ingests, the smarter and more tailored the recommendations become. 

Related → What Is Personalized Customer Service? 

Case Study: How Licorice.com Boosted AOV by 17% Overnight Using Smart Cart

Licorice.com, a family-owned business selling handcrafted licorice gifts, needed a smarter way to increase revenue without overhauling its store design. 

The team turned to Rebuy’s Smart Cart to elevate the shopping experience through personalized product recommendations and dynamic upsells [*]. 

Impact: 

Licorice improved conversion rates by making every cart a personalized shopping experience. 17.65% increase in Average Order Value (AOV), with 1 in 3 orders included a product recommended by the ReBuy Smart Cart integration. 

9. Predictive Support

Predictive support is the use of AI and data analytics to foresee customer issues and resolve them before a support ticket is ever filed. 

How it works: 

• AI models analyze usage patterns, event logs, error messages, account activity, and customer behavior trends.
• These models detect early warning signs of common problems. E.g., declining usage, failed API calls, dropped sessions, or unusual user flows.
• Once a risk is flagged, the system can alert the customer before they complain, trigger automated fixes or step-by-step guides or notify success teams to reach out personally

Case Study: How Makesy Scaled Personalized Support and Response Times With Kustomer

Makesy, a brand built by creators for creators, struggled with a legacy CRM that limited customer engagement, delayed response times, and made customization difficult. 

Their mission to build community and deliver high-touch service wasn’t reflected in their tech stack. By switching to Kustomer, Makesy empowered agents with automation, bulk messaging, and smart self-service features. 

Impact: 

Kustomer helped Makesy create a more efficient and customer-friendly support system:

• Decreased response times, thanks to automation and intuitive agent workflows
• Improved self-service rates, with the conversational assistant resolving 48 tickets in its first week
• Bulk messaging capabilities, enabling outreach to multiple customers with just one click

Kristen Contreras, Customer Service Manager at Makesy, said it best: 

10. AI-Powered Email Response Systems

AI-powered email response systems use NLP and machine learning algorithms to automatically read, understand, and reply to customer emails. 

How it works: 

• The AI tool scans incoming emails to detect intent, urgency, tone, and topic (e.g., refund request, technical issue, billing question).
• It then either:
  • Automatically generates a full response using pre-approved content or dynamic templates, OR 
  • Suggests a draft or reply snippet for an agent to review and approve
• It can also auto-tag, assign, or prioritize tickets based on content

Some advanced systems integrate with the CRM to personalize responses using customer history, location, or purchase data

Case Study: How Brex Tripled Responsiveness With Superhuman’s Email Productivity Tools

Brex is a fintech company for startups and growing teams. However, they needed a way to stay on top of high-volume email communication, especially for customer support and external operations. 

Traditional tools slowed them down, creating inbox clutter and delays in follow-ups. To solve this, Brex integrated Superhuman, an AI-powered email platform, to reclaim control of their inboxes and communication flow [*]. 

Impact: 

Brex experienced 3x faster response time across all customer and partner emails. The company also saved 3.5 hours per user per week managing emails. 

11. Real-Time Language Translation

Real-time language translation uses AI and NLP to instantly translate customer messages from one language to another. This allows support agents to communicate fluently with global customers without actually speaking their language. 

How it works: 

• When a customer sends a message (via live chat, email, or even voice), AI translation models like those powered by neural machine translation (NMT) step in. 
• They translate the incoming message into the agent’s preferred language, and when the agent replies, the system re-translates the message back into the customer’s original language. 

Some AI technologies also preserve context, sentiment, idioms, and domain-specific terminology, making the translations feel natural and accurate instead of robotic. 

Case Study: How Navan Achieved 100% Product Localization and Cut Translation Turnaround Times by 93%

Navan (formerly TripActions), a leading travel and expense platform, needed to scale global operations without slowing down release cycles. 

With thousands of words being translated weekly and teams working across product, support, and marketing, they lacked a centralized translation system. This resulted in inconsistencies, slow turnaround, and unclear ownership. 

To solve this, Navan implemented an end-to-end localization pipeline via Lokalise that automated translation workflows, reduced manual tasks, and made multilingual collaboration seamless across teams and vendors [*]. 

Impact: 

Navan transformed its localization operations across nine languages, resulting in up to 93% reduction in translation turnaround times. They also achieved 100% product localization, enabling simultaneous feature rollouts across global markets. 

12. Customer Feedback Analysis

Customer feedback analysis powered by AI refers to the process of automatically interpreting, organizing, and drawing insights from vast amounts of unstructured customer input. 

How it works: 

• AI collects data from multiple sources: CSAT and NPS surveys, post-interaction ratings, app reviews, customer emails, even social posts.
• NLP models parse the text to:
  • Detect sentiment (positive, negative, neutral)
  • Identify recurring themes or topics (e.g., “checkout issues,” “pricing confusion”)
  • Highlight emotional tones or language (e.g., frustration, delight, urgency)
• Machine learning models then group and rank this feedback based on frequency, severity, or business impact.

Related → From surveys to stories: Redefining customer feedback 

Case Study: How AgoraPulse Reduced Churn With Pendo Feedback

As AgoraPulse scaled its social media management platform, it found itself struggling to manage the influx of customer feedback across various touchpoints. 

With no unified system in place, product teams couldn’t easily see what customers were requesting most, and customer-facing teams had no visibility into whether those requests were being addressed. 

To solve this, AgoraPulse implemented Pendo Feedback, a dedicated platform for collecting, managing, and acting on product feedback at scale. It offered a transparent, centralized hub where customers could submit ideas, upvote existing ones, and track the status of requests.

Impact: 

With users now being able to see their requests move from “under review” to “planned” to “shipped” in real time, AgoraPulse saw a measurable reduction in customer churn. 

Internally, collaboration also improved. Product, customer success, and support teams began working from a shared source of truth. 

Every new idea was backed by data; how many customers asked for it, what segments they belonged to, and what revenue impact was at stake. This made roadmap discussions more objective, streamlined, and aligned with business goals. 

Best Practices for Implementing AI in Customer Service

• Target High-impact, Low-complexity Issues First
• Ensure Your AI is Trained on Brand-Specific Data
• Always Provide a Human Escalation Path
• Use AI to Assist Agents
• Continuously Monitor AI Accuracy and Performance
• Be Transparent With Customers About AI Use
• Align Your AI Strategy With Business Goals

Target High-impact, Low-complexity Issues First

Zoom in on one high-impact, repeatable problem AI can solve in your support workflow. This could be automating password reset inquiries, triaging support tickets, or giving agents real-time reply suggestions. 

By narrowing your scope, you gain tighter control over training data, better evaluation metrics, and faster wins that justify further investment. It also allows your internal team to learn and iterate without being overwhelmed by complexity.

💡For example, you can start with an AI chatbot that handles “Where is my order?” (WISMO) customer queries. Once that proves successful, expand it to handle returns, cancellations, or billing FAQs. 

Ensure Your AI is Trained on Brand-Specific Data

Generic models trained on public internet data don’t speak your customers’ language. To get meaningful, on-brand results, your AI must be trained (or fine-tuned) on your specific ticket logs, help center articles, product documentation, and conversation tone.

This ensures the bot interacts in a way that reflects your brand’s voice, policy, and values. For instance, you want it to know that “plan downgrade” means a billing issue, or that “Express Tier” customers should be escalated to premium support. 

Always Provide a Human Escalation Path

One of the biggest reasons AI-powered support fails is the lack of human fallback. Customers don’t want to feel trapped in a loop of unhelpful suggestions or robotic replies.

From the very first message, give users a visible, easy way to escalate to a real support agent. 

Set a confidence threshold; if the AI is unsure or encounters emotional language (“I’m really frustrated”), it should instantly route the issue to a human. 

This preserves customer trust and gives agents context to jump in quickly and pick up where the bot left off. 

Use AI to Assist Agents

The goal of integrating artificial intelligence into your customer service should be to make your agents better, faster, and more confident. 

To do this, focus on use cases that optimize their workload (e.g., auto-summarizing tickets, suggesting replies, or retrieving knowledge base content). 

When routine tasks are automated, agents can spend more time delivering thoughtful support to customers who actually need a human touch.

Continuously Monitor AI Accuracy and Performance

AI models require constant calibration and retraining, especially as products, policies, and customer expectations evolve. 

Track how your AI performs over time:

• What’s its response accuracy?
• Are customers accepting the bot’s resolution or requesting escalation?
• Are agents finding its suggestions useful or ignoring them?

You can also set up dashboards to track AI deflection rate, CSAT post-bot interaction, and false positive rates. Regular reviews help you spot when the model starts drifting and when it needs new training data. 

Be Transparent With Customers About AI Use

Don’t pretend your bot is human. That strategy backfires quickly. 

Here’s an example of a failed customer service chatbot integration by the parcel delivery company, DPD [*]. 

Instead of going through something similar, make it clear when someone is speaking to an AI assistant, what the bot can help with, and when a human will step in. Transparency builds trust. 

In fact, customers are more forgiving of bots when they know what to expect, and more likely to stick with self-service if it feels honest and respectful. 

Also, respect customer data. Tell them how their information is used, and never surprise them with opaque automation decisions.

Simple line you can use:

“Hi! I’m your AI assistant, here to help with quick answers. If I miss the mark, I’ll connect you to a real person.” 

Align Your AI Strategy With Business Goals

Set KPIs that connect AI usage to real business objectives. Whether it’s reducing average handle time (AHT), increasing ticket deflection, improving CSAT, or lowering cost per resolution, your AI should be driving a measurable outcome. 

For example: 

• Number of tickets deflected = time saved for agents
• Reduction in first-response time = better customer satisfaction
• Improved ticket routing = shorter resolution cycles

Related → 7 Benefits of Using AI in Customer Service 

Turn These Examples Into Your Everyday Wins: Meet Kustomer AI 

Those 12 teams made AI customer service look effortless, —and honestly? It should be. 

If you’re wondering whether your team could replicate those wins, the answer is yes. 

Because the difference isn’t headcount, budget, or some ‘secret’ playbook. It’s choosing AI built by people who know customer service in-and-out. 

People who understand the pressure of peak season, the grind of ticket queues, and the reality of supporting customers with empathy and speed every single day.

That’s why we built Kustomer AI for you. To remove the complexity and make the agent experience just as seamless as the customer’s. 

Vuori’s Sr. Manager of Customer Service, Chad Warren shared what it’s like using Kustomer: 

“[...] We cut our onboarding time down by close to 20% when we switched to Kustomer. But also agents say they love Kustomer — and how often do you hear your employees say they love your enterprise software?” 

Our Kustomers love it, we believe you will too. 

Your Team’s New Superpower (Side Effects May Include Actual Productivity)

Become a Happy Kustomer.