Press 1 to repeat yourself. Press 2 to speak to someone who’ll transfer you. Press 3 to wait... and wait.
Customer service has turned to a test of patience. Might as well press 4 to question your life choices.
At least that’s what Marc Strigberger, a retired lawyer, concluded after spending three hours on the phone trying to cancel a transaction that should have taken minutes [*].
But before you swear off automated systems forever, let’s talk about how AI is transforming this nightmare into something surprisingly... helpful.
Benefits of Using AI in Customer Service
Faster Response Times and 24/7 Availability
One of the most immediate and impactful advantages of using AI in customer service is the ability to provide support instantly—at any time of day, across any time zone.
Traditional customer service teams are bound by work hours, time-off policies, holidays, and staffing limits.
Customers, however, expect answers on-demand. In fact, research consistently shows that delayed responses are one of the top reasons for customer frustration and churn [*].
AI changes this completely.
AI-powered chatbots, virtual assistants, and automated help systems work 24/7. They can answer common queries, initiate troubleshooting, and even trigger personalized workflows without a human needing to step in.
This reduces wait times and ensures that even when your team is offline, your brand remains accessible and responsive.
On day one, Kustomer Assist handled 10% of our chat conversations without any agent interaction and that number has been steadily increasing.
TJ Stein, Head of Customer Experience, Everlane.
Beyond just handling after-hours traffic, AI ensures real-time resolution during peak hours when human agents are overwhelmed.
It serves as a scalable frontline responder that filters routine tasks and requests, freeing up human agents to focus on more complex or emotionally sensitive issues.
In the long run, this increases customer satisfaction, contributing to a more efficient and less overwhelmed support team.
| 💡Example: Let’s say you run an ecommerce brand. And you start experiencing a spike in support volume every Friday evening as customers ask about weekend deliveries, return policies, and order status. |
You can integrate a chatbot that pulls live order data, surfaces delivery timelines, and initiates return processes automatically.
Multilingual and Global Support
As businesses expand into new markets, customer support becomes significantly more complex.
To truly meet customer needs, your support must adapt to different languages, communication norms, and expectations. For most companies, hiring dedicated multilingual support agents in every region is expensive, resource-heavy, and slow to scale.
To put it in context, the ‘Can’t Read, Won’t Buy’ Series by CSA provided some interesting stats [*]:
- 65% of customers prefer content in their language, even if it’s poor quality.
- 40% of shoppers will not buy in other languages.
- 73% of customers want product reviews in their own language (if nothing else).
On the flip side, AI technologies offer an advantage using advanced natural language processing (NLP) and LLM to handle conversations in multiple languages.
For example, you can use AI to translate incoming messages from users and also respond with localized replies. This helps you support customers across the world without needing a massive multilingual team.
But it doesn’t stop at translation. New conversational AI systems can now:
- Detect the user’s language automatically without them needing to select it,
- Adapt tone and phrasing based on cultural norms,
- Maintain consistent terminology for complex or regulated products across languages,
- And allow seamless handoff to a human agent, still within the same language context.
🌍 From Hola! to Bonjour, Kustomer detects your customer’s language and responds instantly.
Increased Agent Efficiency
In a traditional support environment, agents spend a large portion of their time doing time-consuming tasks: assigning tickets, writing routine responses, searching for help articles, or asking customers to clarify their issues.
These tasks consume time and drain morale, leaving less bandwidth for solving complex, high-impact problems.
However, all that changes with tools like AI-powered ticket triage, automated summarization, recommended replies, and real-time content suggestions.
AI can automatically tag tickets, classify intent, prioritize customer requests based on urgency, and even surface the most likely solution. Some systems even draft responses for review, pulling in product documentation and prior conversations.
This translates to shorter handle times, fewer errors, and a less mentally draining workflow.
Plus, when agents are freed from repetitive tasks, they can focus on what they do best: listening, empathizing, and solving complex issues.
🎯Use Case: Say your SaaS company is struggling with long resolution times.
Agents were handling over 50 tickets a day, but many of those involved the same 3-4 repetitive issues: password resets, billing clarifications, or plan upgrade requests.
A suitable fix would be implementing AI-powered helpdesk solution to:
- Categorize and route tickets to the appropriate agents,
- Suggest answers directly within the agent console based on ticket content and past resolved cases,
- And for FAQs, the system auto-completes replies that agents can quickly review and send.
[Kustomer] is simple enough for all our agents to use yet functional enough for customers and clients to navigate.”
Kristen Contreras, Customer Service Manager, Makesy
Lower Operational Costs
As your company grows, customer service costs can rise quickly. Whether it’s hiring more agents, training them, managing schedules, or adding layers of management, scaling support through headcount alone becomes expensive and inefficient.
AI helps reduce these costs in two ways: it automates repetitive interactions and optimizes workflows.
Similar to what we discussed earlier, customer service queries like password resets, order tracking, account updates, and simple FAQs—are all repetitive.
AI chatbots and voice bots can automate responses to these customer queries entirely, eliminating the need for a human touch.
Beyond automation, AI also reduces indirect costs: shorter training cycles for new agents, fewer escalations, less need for overtime staffing during peak periods, and reduced customer churn due to poor service.
And while AI has an upfront cost (especially for enterprise tools), its long-term ROI becomes clear as customer service operations become leaner, faster, and more self-sustaining.
Recommended → How leading DTC brands use AI to stay lean and competitive
Real-Time Personalization
71% of your customers expect to be treated like your only customer. 76% of them get frustrated when they don’t find it [*].
But true personalization at scale is hard largely due to budget constraints. Plus, human agents can only juggle so much context in a short amount of time, especially when they’re switching between tickets, tools, and channels [*].
Meanwhile, AI solves this by looking at real-time context—like past purchases, support history, browsing habits, account type, subscription plan, or even location. Then it uses that data to tailor every response automatically, enabling more personalized interactions with each customer.
AI can also adjust its responses based on:
- Who the customer is (first-time buyer vs. loyal subscriber),
- What they’ve asked about in the past,
- What stage of the lifecycle they’re in (onboarding, renewal, etc.),
- Or what actions they’ve recently taken on your website or product.
This level of contextual intelligence allows AI to resolve customer issues more efficiently, and also deliver personalized experiences.
According to McKinsey’s Next in Personalization Report, customers want business to demonstrate they know them on a personal level [*].
And when AI and agents work together, it gets even better. Agents see who the customer is, what they’ve done, and what they need.
This improves satisfaction, boosts retention, and opens the door to timely cross-sells or upsells—all without coming off as pushy or irrelevant.
“We love the Kustomer timeline. It’s one simple place where we can view data across email, live chat, telephone, SMS, Facebook and sales data. By having a 360 degree view of the customer, it enables us to respond to them more quickly and with more relevant information.
Our products are highly technical, so by tracking our customer experience with our product, we can better serve them and better learn from their experiences.”
Dave Weiner, Founder & CEO, Priority Bicycles.
You can use Kustomer to tailor responses to customers that interact with your business.
This helps maintain a consistent voice across your communications, build trust by providing more accurate answers, and elevate the overall customer service experience.
Explore Kustomer AI Agents for Personalized Support →
Seamless Channel Integration
Customers don’t stick to one channel. They might start with a website chat, follow up via email, send a DM on social media, check status in a mobile app, and eventually call your support line—all for the same issue.
And they expect that each interaction picks up where the last one left off.
Unfortunately, most support systems are siloed, meaning customer conversations get fragmented, agents lack context, and customers have to repeat themselves.
In a recent report, 79% of customers expect consistent interaction across departments, while 55% say it feels like they’re communicating with separate departments, not one company [*].
AI solves this by offering an omnichannel support experience. Instead of treating each channel as a separate thread, AI-powered systems aggregate, track, and transfer conversation history, customer data, and ticket context across every touchpoint.
Here’s how it works:
- A customer starts a live chat with a bot on your site. Next, AI logs their issue and generates a case ID.
- Later, when they email support, the AI recognizes the email address, attaches the message to the same case, and updates the ticket.
- If they later call the hotline, the AI pulls the case history in real time and feeds it to the human agent.
All this happens in the background.
This unified experience reduces friction for customers, boosts agent productivity, and increases resolution speed. It also enables better analytics, since you now have a complete, multi-channel view of every customer’s journey.
One thing that my team and I really love about Kustomer is just that holistic view of the customer. It’s just so nice because that means we don’t duplicate effort.
Plus, we have that history of everything that went back and forth with the customer.
Heather Kunert, Head of Customer Experience, Comrad.
Related → What is Omnichannel CRM? A Complete Guide for CX Teams
Better Insights Through Data Analysis
Every customer interaction; whether it’s a live chat, a ticket, a call, or a bot conversation—generates data. But without the ability to analyze and understand that data at scale, it’s just another clutter.
This is where AI steps in, turning raw support data into actionable insights.
For example, AI customer service solutions can scan thousands (even millions) of support conversations in real-time, detecting patterns that would take agents months to uncover.
These systems can:
- Implement ai-driven predictive analytics to identify trending issues before they become major problems (e.g., customers about to churn),
- Surface common pain points tied to specific product features,
- Perform sentiment analysis in real-time to detect shifts in customer mood,
- And even measure agent performance across speed, accuracy, and tone.
AI can also summarize conversation themes, recommend updates to your knowledge base, and suggest where to invest in product improvements based on customer feedback trends.
How a Child Rideshare Service Got its CX Team Where it Needed to Go
With better insights and more trust in its CX data, such as CSAT and FCR, HopSkipDrive was better able to understand its performance, improve team efficiency and make better business decisions.
Potential Limitations of Using AI in Customer Service (And How to Handle It)
Risk of Over-Automation
There’s a fine line between automation and alienation. Some companies, in a quest to reduce costs, rely too heavily on AI. As such, making it difficult (or impossible) for customers to speak to a human. When users are forced into loops of canned responses or can’t escalate when needed, it becomes a major point of friction.
Furthermore, this “automation wall” ruins service quality, creating an experience where customers feel stuck, unheard, or undervalued. In fact, when Morgan Stanley interned its interns, about 93% said they’d prefer to talk to human agents, while another 75% said chatbots failed at least half to solve their problem [*].
| 💡How to handle it: Use AI as the first-line of defence, and not the only line. Make escalation paths clear. Give customers a visible, easy way to reach a human when they need one. |
Limited Understanding in Complex or Contextual Scenarios
AI excels in structured, repetitive, and predictable environments. However, it struggles when support scenarios require deep context, multi-step reasoning, or industry-specific knowledge that’s constantly changing.
For example, let’s say a customer is asking about a very niche use case that the bot hasn’t been trained on. The AI might give incorrect, vague or misleading answers. In some cases, it might even hallucinate something that sounds plausible but is factually wrong.
A recent incident was when Cursor, an AI-coding assistant, logged out multiple users from their accounts when they switched devices. When users contacted support, the AI support agent ‘hallucinated’ a company policy prohibiting users from using the software on multiple devices—which of course wasn’t true [*].
| 💡How to handle it: Opt for tools that summarize past interactions or highlight the most relevant data points. This helps human agents step in with full context and respond intelligently. |
Data Privacy and Compliance Challenges
AI systems process vast amounts of data, including messages sent, account history, and even customer behavior.
If not handled responsibly, that opens the door to privacy violations, regulatory penalties, and decline in customer trust. For industries with strict compliance requirements (like fintech, healthcare, or enterprise SaaS), this is even worse [*].
| 💡How to handle it: Work with vendors who are GDPR, CCPA, and SOC2 compliant. Set clear boundaries on what data AI can access, retain, and use. And also communicate your AI policies transparently with customers. Read Our AI Compliance FAQ→ |
Lack of Emotional Intelligence and Empathy
At the end of the day, AI sticks to its name ‘artificial intelligence’, meaning it’s simply a machine following a set of instructions. This limits its ability to truly understand human emotions.
While NLP can detect customer sentiment or tone (“angry,” “frustrated,” “neutral”), it doesn’t actually feel anything. It doesn’t recognize sarcasm, subtle emotions, or cultural nuance the way a well-trained human does.
In moments where a CFO is angry about a billing error or a product lead is frustrated with a bug threatening their launch, a robotic “We understand your concern” is tone-deaf and brand-damaging.
| 💡How to handle it: Use AI to streamline predictable tasks. But give your human agents the space, tools, and time to handle the emotional ones. You can flag messages with high-frustration language or sentiment shifts and route them to trained agents who know how to de-escalate. |
Checklist for Responsible AI Implementation in Customer Support
Define the Problem AI Should Solve
Start with clarity: Are you trying to reduce first-response time? Scale support during off-hours? Improve routing accuracy?
AI is most effective when it’s solving a clearly defined operational challenge.
Checklist:
✅ Identify 3-5 frequent customer inquiries where AI could help
✅ Prioritize use cases: deflection, summarization, tone consistency, etc.
✅ Set clear goals (e.g., 30% faster first-response time in 90 days)
Map Your Customer Journey and Pinpoint AI Opportunities
Next, look at your entire customer support journey, from pre-sale inquiries to post-sale troubleshooting.
Where are the gaps? Where are you repeating yourself? Where does your team need backup?
Your AI might start in one part of the journey (e.g. live chat or email replies), but eventually, it should touch everything: routing, triaging, summarizing, follow-up.
Checklist:
✅ Audit where support interactions happen: chat, email, phone, portals
✅ Identify repetitive tasks: FAQs, onboarding, password resets
✅ Identify high-friction moments AI could smooth (e.g. complex handoffs, conversation summaries)
Clean, Label, and Prepare Your Data for AI Training
AI learns from what you give it. If your historical support data is incomplete, inconsistent, or filled with bias (e.g. agents tagging things differently or vague internal notes), you’ll train a flawed assistant.
Start by cleaning and structuring your support data. Label it by intent, outcome, topic, and sentiment. Refactor your knowledge base so it’s clear, up to date, and actually helpful.
Checklist:
✅ Organize past support tickets by category and resolution
✅ Update knowledge base and tag articles by function
✅ Clean duplicate macros and standardize message templates
✅ Map common intents: billing, bugs, usage, onboarding, etc.
Choose the Right AI Tool Based on Your Maturity Level
- If you’re new to this, you may want an AI-powered customer service platform with built-in AI assistants, pre-trained on support language.
- If you’re mature, you might build your own custom model or fine-tune a generative AI model on your brand data.
What matters is that the tool fits your team’s needs, tech stack, and support complexity.
Checklist:
✅ Evaluate tools across pricing, integrations, and training ease
✅ Make sure it supports your preferred channels (chat, email, CRM)
✅ Confirm vendor compliance with data privacy laws (GDPR, CCPA, SOC2)
✅ Test how “editable” and customizable the AI actually is.
Fine-Tune the AI to Match Your Brand Voice
Your brand voice is your personality, and AI should reinforce it. You want your AI to sound like you, know what you know, and know what it doesn’t know.
Train it on your tone of voice, your product documentation, and your support transcripts.
Also add guardrails so if it doesn’t know the answer, it’ll route to a human agent and not hallucinate.
Checklist:
✅ Input brand tone guidelines and example replies
✅ Ground responses in your current knowledge base and ticket history
✅ Program fallback responses for low-confidence answers
✅ Set strict escalation rules for sensitive or ambiguous queries
Review, Retrain, and Iterate
AI is not a one-and-done project. It’s now a living part of your support operation. As your products evolve, customer expectations shift, and your knowledge base grows, your AI must evolve too.
Checklist:
✅ Assign an AI “owner” on your support, contact center or ops team
✅ Create a weekly review cadence for AI errors and improvements
✅ Update AI decision-making process including knowledge base links, macros, and fallback logic
✅ Train AI on new intents and seasonal topics
Measure the Right Metrics
To know if AI is really working, you need to track both leading and lagging indicators.
Is it reducing time to resolution? Are customers satisfied with AI-only replies? Are agents less burnt out?
Just as importantly, don’t mix up AI and agent performance. Measure both. Know where automation wins and where humans are still better.
Checklist:
Track:
✅ Resolution rate for AI-handled tickets
✅ Escalation rate and escalation reasons
✅ CSAT for AI interactions vs. human-led ones
✅ Average handle time before & after AI involvement
✅ Ticket deflection vs. ticket avoidance (i.e., did it help or just reroute?)
Related → How to measure customer service performance
Communicate AI Use Transparently to Customers
Don’t trick users into thinking they’re talking to a human when they’re not. Transparency builds trust. Some users may prefer human support up front, others will appreciate self-service options using AI, as long as they know what they’re getting.
Checklist:
✅ Clearly introduce the AI in your support interfaces
✅ Offer a quick path to human help for those who prefer it
✅ Use friendly, non-robotic language that matches your brand tone
✅ Include trust-building statements like “Powered by your help center” or “Backed by human review”
Kustomer’s Handoff feature give your agent the complete context of the entire conversation, helping them assist customers better.
Press 1 If “Please Repeat” Is Your Team’s Catchphrase
→ (Or switch to a platform that already remembers everything.)
If your support team had a dollar for every time they said, “Can you remind me what happened again?”—they’d have a budget for a better platform. Like Kustomer.
- It summarizes entire conversation histories so agents can jump in confidently, without asking basic questions.
- It auto-tags intent and sentiment, so issues are prioritized based on what actually matters.
- It routes conversations smartly, reducing wait times and matching each customer with the right agent, every time.
- It even suggests responses and resolutions in real time, learning from your knowledge base, macros, and past interactions.
All inside a single timeline view that eliminates the “what happened before I got here?” scramble.
As Vuori’s Head of Customer Service, Chad Warren, puts it:
“At the end of the day, no matter what tool we use, it’s still about being in a great relationship with our customers. If you look at what it means to be in a great relationship with someone, it’s remembering their favorite things, remembering what they said to you last week. And Kustomer allows you to do that because of the timeline view.”
Chad Warren, Sr. Manager of Customer Service, VUORI
Kustomer brings that same philosophy to customer service by turning every interaction into a continuation of the relationship.
AI Can’t Hug Your Customers, But This Comes Close
