Imagine this: Your company has just launched a new product, and customers have questions. Lots of questions. Your support team is ready to help, but there’s just one problem: The volume of inquiries is overwhelming, and it’s impossible for your team to keep up. Customers are frustrated by slow response times and are leaving negative reviews online. AI for customer service could have saved the day. This article will outline how conversational AI tools from Conversational AI Companies can help scale customer service to meet rising demands, improve response times, and deliver a better experience for customers, without sacrificing personalization or quality.
Among the various AI for customer service solutions, Voice AI’s text-to-speech tool stands out as an effective way to quickly enhance your customer service offerings.
What is AI for Customer Service?
AI for customer service is defined as the technologies and the process of leveraging AI to automate customer support and knowledge management tasks as well as augment/assist humans, i.e., contact center agents, branch workers, and field service agents, in their customer conversations for effective, efficient, and consistent problem resolution and personalized advice.
Among commonly used AI technologies are Natural Language Processing, ML, Case-Based Reasoning, and Conversational Generative AI, among others. Businesses need a trusted AI knowledge hub for AI and knowledge orchestration so that the right AI is used and the proper knowledge delivered for the right situation through the right channel in the right tone.
What AI Technologies Are Used in Customer Service?
Here are some commonly used AI technologies for customer service, and what they are:
Machine Learning (ML) and Natural Language Understanding (NLU)
Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without being explicitly programmed. NLU is the ability to understand natural language input, infer user intent, and respond accordingly.
Generative AI or gen AI
Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to change the way we approach content creation drastically.
Conversational AI
Conversational AI leverages technologies such as Case-Based Reasoning to guide frontline staff in their customer interactions, including the following best questions to ask and the next best thing to do in their effort to resolve problems or provide advice.
Conversational generative AI can also be used to drive conversations where regulatory compliance is not critical and to drive customer self-service conversations.
Case-based Reasoning
Looks at past cases to resolve new instances, just like a doctor would go about diagnosing a disease or a sales expert would go about recommending a new product to a customer.
How Does AI for Customer Service Work?
There are many different ways you can use AI in customer service. For example, you can embed AI-powered chatbots across channels to instantly streamline the customer service experience. Beyond answering common questions, these chatbots can greet your customers, serve up knowledge base articles, guide them through standard business processes, send out a field technician for field requests, and route more complex questions to the right person.
Imagine this from the customer perspective: you want to return a pair of shoes and you need some help. You start an online chat with an agent, but then wait 30 minutes for a response.
Instant, Personalized Help with AI in Customer Service
With customer service AI, you get a personalized response in seconds. Think of it like a virtual buddy who’s not only knowledgeable but also understands your exact needs and preferences. All you have to do is tell it what I need help with, and it will take care of the rest.
No need to find my tracking number, provide my email, or explain the details of my purchase; it already has all that information and knows exactly what to do. So many organizations are already using AI for customer service. 83% of decision makers expect this investment to increase over the next year, while only 6% say they have no plans for the technology.
What Are the Benefits of AI in Customer Service?
Let’s look at six ways AI in customer service can help your team, especially if you’re interested in getting started with generative AI:
Higher productivity
An AI tool like Einstein Copilot can empower service teams to get work done faster. For example, AI can act as an assistant built directly into an agent’s workflow. Recent research shows that 84% of IT leaders believe AI will help their organization better serve customers. Case in point: AI-based conversational assistants can increase productivity by 14% for support agents.
Better efficiency
Manual processes can be a heavy lift for service agents. This includes tasks like swiveling back and forth between systems and screens to view customer history, searching for knowledge articles, routing field workers to service locations, and manually typing responses, all of which tend to be error-prone when done by a human.
AI in customer service can give customer service workers intelligent recommendations across knowledge bases, conversational insights, and customer data. Our research found that 63% of service professionals say AI will help them serve their customers faster.
A more personalized service interaction
For AI to be useful, it needs to understand your customer, which means it needs access to your company’s data. When a customer initiates a conversation with a chatbot, AI can populate important information, such as the customer’s name, location, account type, and preferred language in real time.
If the request requires a field service technician, AI can send all of the critical information to the field worker so they can provide personalized service the moment they walk in the door.
Optimized operations
AI in customer service makes customer service operations smoother and more efficient. You can use AI to analyze customer calls, emails, and chatbot conversations to determine the signs that a customer is likely to escalate an issue, the time it will take to resolve a problem, and more.
These insights help find new ways to improve the customer experience. For example, if customers often ask for an agent when they want to return a product, a chatbot can proactively share a knowledge base article to minimize escalation.
AI-Powered Support Through Case Analysis and Knowledge Creation
AI can also analyze your company’s case history and identify the top reasons your customers contact customer service. If a knowledge article doesn’t exist to address one of these reasons, you can use generative AI to draft a knowledge article or update an existing one.
Once your team approves the article, it will then help agents to provide quick and exceptional support and can be used to deflect cases in a self-service portal or with a chatbot.
Less burnout and improved morale
AI allows agents to eliminate repetitive, time-consuming work and focus on situations that require creative problem solving, social intelligence, and complex critical thinking, activities that will move the needle on overall customer experience. It’s not a surprise that 79% of IT leaders say generative AI will help reduce team workload and thereby reduce burnout.
A proactive service experience
AI can draw info from your customers’ contracts, warranties, purchase history, and marketing data to surface the following best actions for agents to take with your customers, even after the service engagement is over.
For instance, AI can let customers know that it’s almost time to renew their subscription, remind them when it’s time to book a maintenance appointment, or inform them about available product upgrades or discounts. And taking that to the next level, generative AI can even summarize customer conversations and produce knowledge base articles for future reference.
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19 Ways to Use AI for Customer Service
1. Voice AI, Transcription, and Call Analysis
Voice AI technologies automate phone-based customer service interactions, provide real-time support during calls, and extract valuable insights from conversations. During a call, AI can answer common questions, gather information, and resolve issues before transferring complex cases to human agents. AI transcription converts spoken dialogue into text for record-keeping and allows for live sentiment analysis to gauge the emotional state of the customer. Additionally, AI can assist agents during calls by providing prompts, suggesting relevant information from knowledge bases, and offering guidance in real time to help them handle inquiries more effectively.
Example Use Case
A customer calls about their order status. A virtual agent answers, uses voice recognition to verify who they are, and immediately tells them when their package will arrive. If they have a more complicated question, the virtual agent transfers them to a human, along with a quick summary of what’s already been covered.
Create Realistic Voiceovers Fast with Voice.ai
Stop spending hours on voiceovers or settling for robotic-sounding narration. Voice.ai’s text-to-speech tool delivers natural, human-like voices that capture emotion and personality, perfect for content creators, developers, and educators who need professional audio fast.
Choose from our library of AI voices, generate speech in multiple languages, and transform your projects with voiceovers that sound real. Try our text–to-speech tool for free today and hear the difference quality makes.
2. Conversational AI: The Next Generation of Customer Service
Conversational AI is a subset of artificial intelligence that focuses on automating communication with customers. Simply put, it enables machines to communicate with humans conversationally.
AI is the automated part of a support process, while conversational AI is the “conversation” part of the interaction. We’re all hearing about conversational AI these days, and for good reason. It’s transforming customer service by making interactions more human-like and efficient.
3. Chatbots: The Most Common Use of AI in Customer Service
One of the most common uses of AI in customer service is customer service chatbots. Businesses use chatbots for a variety of reasons, with automating customer support interactions being number one.
Support teams use chatbots to automate the most repetitive and redundant customer support inquiries. This includes information for routine questions about personal accounts, order status, and product or service usage.
4. Assist Agents With AI
In today’s customer support world, AI can be used on both the customer-facing side and the agent-facing side. AI can help automate repetitive customer inquiries that send customers canned responses containing the information they seek.
When a support query and question can’t be automated, those tickets then get sent to agents who must sift through mountains of past ticket history and their internal company wiki to find the correct information for that question, which often takes time.
Boost Agent Efficiency with AI-Powered Knowledge Search
This knowledge search can be automated for agents. With AI, agents can get assistance surfacing the knowledge they need to answer tickets and resolve them much faster.
With the right AI tool integrated into a support agent’s helpdesk, reps can have an AI assistant at the ready all day long. By providing an agent assist tool, support agents can reduce Time to Resolution, Average Handle Time, CSAT, and more.
5. Improve Self-Service Rates With AI
AI can help improve self-service rates; the customer self-service rate refers to the rate at which customers can identify and find the support they need without relying on a customer service agent. With the help of AI, customers can consult chatbots that automatically produce the information they are seeking.
The right AI tool for customer support embeds into a support agent’s helpdesk and learns from a company’s historical data, including past tickets, internal company wikis, external-facing knowledge bases, agents’ notes, and more. This information helps customers self-serve and get the information they need without agent interference.
6. Sentiment Analysis and Opinion Mining
Through natural language processing, AI can sift through mountains of customer feedback and market research surveys, support tickets, online reviews, and social media. It pulls out the emotional tone and key themes, giving you a bird’s-eye view of what people are saying about your business.
This intel helps you spot recurring pain points, understand what customers love, and make data-driven decisions to improve your products and support. Plus, eavesdropping on what people say about your competitors can reveal market gaps and show you where they’re crushing it or falling flat.
Example Use Case
You unleash AI on your post-purchase survey comments and discover that tons of customers are confused by your product setup instructions. You then create and send out a user-friendly tutorial video.
7. Natural Language Processing: The AI Behind the AI
As mentioned, artificial intelligence works in conjunction with other technologies to make chatbots and automated customer interactions possible. One of these technologies that goes hand-in-hand with AI is Natural Language Processing (NLP). Natural language processing is a branch of artificial intelligence that uses machine learning algorithms to help computers understand natural human language.
NLP is an umbrella term that encompasses anything and everything related to making machines able to process natural language, whether it’s receiving the input, understanding the input, or generating a response. This means that most support interactions require NLP to process information and respond accordingly.
8. Machine Learning: The Smarter AI
Just as crucial as NLP is Machine Learning (ML). Machine learning makes it possible for an AI application to learn and improve from experience without explicit programming. Machine learning enables continued improvement, which is highly important in customer support.
With this technology, AI helps chatbots become better. As the AI learns, responses for customer needs improve, and the automated responses become even more consistent and concise.
9. Automate Ticket Creation
Sending in customer support tickets can be overwhelming and sometimes confusing to customers. Customers who are seeking support are often looking for a chat widget, a “Contact Us” form, or a company email they can reach out to with their questions and concerns.
AI can help automate ticket creation by allowing customers to submit questions via a chatbot widget that is designed to deflect repetitive customer support tickets and create tickets for those that can’t be automatically answered. AI can help streamline this process and help both agents and customers.
10. Automate Ticket Routing
One of the most mundane and redundant tasks within customer support is ticket routing, which can be automated with the help of AI. Intelligent routing and triage AI takes the headache out of support ticket management by sorting, prioritizing, and assigning incoming customer issues automatically. Using natural language processing and sentiment analysis, it figures out what each message is about and how the customer is feeling, then routes it to the appropriate agent or team.
This automated sorting means critical issues get immediate attention while agents skip the mind-numbing task of manual ticket organization. As your support strategies evolve, the AI adapts, too, continuously learning to make smarter assignments that match cases with the right experts.
Example Use Case
The support team gets a customer ticket with “Urgent: My order hasn’t arrived!” in the subject line. The AI spots the “urgent” keyword, detects the frustration in their message, tags it as “Shipping Issue” and “High Priority,” and bumps it to the top of the shipping team’s queue.
11. Automating Email Responses
If AI can automate individual chatbot inquiries, then it can automate your email responses too! With the help of NLP and ML, AI tools can help agents automate email responses by assisting them with surfacing the correct information when resolving customer support tickets via email.
With AI, agents can have access to a widget that sits on top of their helpdesk and will surface the correct information for customer questions they’re responding to based on previously answered tickets and company data.
12. AI Summaries and Action Recommendations for Agents
AI boosts efficiency by summarizing customer histories and recommending next steps, saving time and providing immediate context. Instead of digging through past interactions, agents receive concise overviews that highlight key issues, attempted solutions, and outcomes.
It also analyzes similar successful cases to suggest actions, troubleshooting steps, or tailored response templates. These suggestions support agents without replacing them, offering a balance between automation and human judgment.
Example Use Case
When a customer contacts support repeatedly about a recurring software issue, generative AI for customer support can summarize past interactions, flag tried solutions, and suggest a next-step diagnostic with a clear, conversational response. These AI-assisted replies can be saved as templates and used to train bots, continuously improving support quality.
13. Real-Time Language Translation for Multilingual Support
For businesses with customers around the world, the ability to offer multilingual support is massive (just like my beloved Christmas breakfast burrito). AI translation tools are like having an instant interpreter in every conversation. These systems detect what language someone is using and translate both incoming questions and outgoing responses on the fly.
Advanced neural machine translation (NMT) doesn’t just swap words; it captures context, grammar, and cultural nuances for natural-sounding communication. Some systems even detect where customers are located and adjust phrasing to match local expressions and cultural norms. This works for both text and voice interactions, breaking down language barriers in real time.
Example Use Case
A customer in Spain submits a support ticket in Spanish. The AI immediately translates it to English for your U.S.-based support agent. When the agent responds in English, the AI converts it back to fluent Spanish for the customer. Neither party knows the other’s language, but the conversation flows naturally anyway.
14. Personalized Customer Experiences and Recommendations
Machine learning helps businesses create personalized customer experiences by analyzing everything from demographic data to past purchases, browsing behavior, and explicitly stated preferences.
Smart Follow-Ups and Customer Retention with AI
AI builds a comprehensive profile of each customer, allowing it to tailor interactions that boost engagement and loyalty. In practice, that might mean suggesting help articles before a customer even reaches out or making sure their message goes straight to the support rep best equipped to help.
It can also trigger friendly follow-ups after a ticket is closed to check that everything is resolved. Because machine learning can spot patterns in behavior, it’s excellent at flagging customers who might need extra attention, whether they’re long-time loyal customers or those at risk of leaving.
Example Use Case
Using machine learning, you can automatically segment customers’ profiles into groups, aligning browsing history with your product categories. You then have email follow-up campaigns to offer each group 10% discount codes for products within those categories.
15. Inventory and Demand Forecasting
While not customer-facing, AI-powered inventory management directly impacts the customer experience by ensuring products are available when people want them. Using predictive analytics, AI can forecast demand and optimize stock levels, allowing businesses to minimize those dreaded “out of stock” messages and backorder notifications.
Machine learning analyzes historical sales, seasonal trends, marketing campaigns, and external factors to predict future demand with impressive accuracy. This lets businesses proactively manage inventory, keeping enough on hand to meet customer needs without drowning in excess stock and storage costs.
Example Use Case
An online retailer uses AI to analyze sales data for their popular winter coats. The AI spots a predictable surge in demand during November and December, plus regional differences in preferred styles. Armed with this forecast, the retailer increases inventory of hot sellers before the holiday rush and ensures regional warehouses are stocked with locally popular styles.
16. Wait Time Monitoring and Management
If there’s a 10th circle of hell, it probably involves waiting for a customer service representative for all eternity. Letting customers know how much time they can expect to wait for an agent can mean the difference between happy customers with solved problems and customers giving up on any possibility of a resolution after five minutes to leave a one-star review.
AI can analyze an entire archive of past interactions and tickets, calibrate them to current resolution processes, and then churn out dynamic wait times based on parameters like ticket type, agent, agent workload, and more. These measures don’t solve anything for customers, but they go a long way in setting expectations and keeping them satisfied.
Example Use Case
When queries come in that your bots can’t handle, AI assesses agent utilization according to average time to resolution by ticket type. This shows customers their place in line and how long they have to wait for an agent if they are unable to troubleshoot themselves.
17. AI-Powered Workflow and Process Automation
Beyond directly helping customers, AI enhances support operations by automating behind-the-scenes workflows. This goes beyond routing tickets to include automatically pulling up customer records, initiating return processes based on specific criteria, and syncing data across different systems.
AI streamlines operations by automating these routine steps, reducing human error, and freeing up agents to focus on what matters most: solving customer problems and handling complex issues. This backstage automation creates a more efficient and responsive support experience.
Example Use Case
When a customer requests a product return online, AI automatically pulls their purchase history and kicks off the return workflow in your CRM.
Based on why they’re returning it, the system generates a prepaid shipping label and emails it directly to the customer, requiring no human intervention. Once the return is confirmed, the AI updates your inventory system to keep stock levels accurate across all platforms.
18. Improve Service Quality
AI in customer service quality assurance (QA) can help reduce customer churn by evaluating your support conversations. AI speeds up the QA process by reviewing all conversations across agents, channels, languages, and business process outsourcers (BPOs).
From there, it provides instant insights into your support performance, which enables you to enhance agent training and solve knowledge gaps.
Example Use Case
Rentman, an all-in-one event rental solution, uses Zendesk QA to analyze all customer interactions and QA reviews to deliver actionable feedback based on customer needs to support agents.
Axel Keicher, customer integration lead at Rentman, says, “With 360-degree feedback, you can coach agents in specific areas, such as teaching them how to offer better support or educating them about the product. The feedback is peer-based and everyone is involved in the process, which helps agents become more engaged.”
19. Predictive Analytics
AI-based predictive analytics uses customer data to anticipate customer needs, behavior patterns, and potential issues. This helps companies proactively address customer concerns, optimize resource allocation, and personalize customer interactions.
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How to Use AI in Customer Service with the Right Software
Before integrating AI into your customer service strategy, you must examine the practical and operational implications. From data security to team transition, thoughtful planning helps ensure AI tools enhance, not hinder, your support experience.
Privacy Matters When Adopting AI for Customer Service
AI still isn’t perfect, and there have been concerns about sensitive user data being anonymized effectively and staying secure when vast amounts of data are being synthesized. Before adopting AI for customer service, identify what data the technology will collect, how it will be stored, and what measures your vendor has in place to secure it.
Long-Term System Maintenance for AI Operations
Maintenance for AI customer service tools in the long term can be complex and require implementation teams with specialized skills and knowledge. Prepare for this before adopting AI for customer service to avoid hiccups down the line.
Expect Implementation Friction with AI Tools
Enterprises can expect rollout to take time and involve plenty of trial and error before going live. AI for customer service will likely change how your internal teams operate. So, while planning for deployment, consider how you’ll manage the transition to this new way of working.
Your Team May Resist AI Adoption
Current team members may need additional training and may have some resistance early on to changing established workflows.
To mitigate this, involve customer service agents in the decision-making process when selecting an AI tool. This will help them feel more comfortable with the upcoming changes and allow them to voice any concerns.
Ethical Considerations for AI in Customer Service
Ethical AI use means ensuring automated responses are fair and transparent and maintaining quality by avoiding bias and handling tone, escalation, and prioritization responsibly. Before adopting AI for customer service, develop a plan for how your organization will uphold ethical standards when using the technology.
Software Options for AI in Customer Service
Businesses have a growing menu of options for bringing AI into their customer service operations. Here’s a quick rundown of some key software categories to consider.
AI Help Desks
These solutions are built specifically for deploying automated support workflows with AI-powered agents. Help desk software like Zendesk has AI built in and can serve as an all-in-one customer service platform.
AI CRMs
If you use a CRM to manage support workflows, many offer built-in AI tools that integrate directly into customer service processes.
AI Chatbot Builders
One of the more common solutions is AI chatbot builders, which allow you to create bespoke bots for your customers.
AI Sales Tools
If your service and sales ops intersect, AI sales assistants can offer AI reception services, automate outreach campaigns, and enhance emailing.
AI Meeting Assistants
For teams that attend a lot of virtual meetings, AI assistants can take notes, create transcripts, summarize meetings, and timestamp recordings.
AI Orchestration
You can orchestrate all your AI workflows, connecting your customer service tools and pulling in the power of AI when you need it. Build custom AI chatbots with no code, then connect those chatbots to your entire tech stack.
Try our Text-to-Speech Tool for Free Today
Voice AI, or artificial intelligence for voiceovers, uses machine learning to create humanlike narrations for videos, podcasts, games, and more. In recent years, voice AI has taken giant leaps forward. Modern voice AI can produce audio that sounds like real people, complete with emotion, personality, and varying tones.
This has massive implications for content creators, educators, developers, and anyone else who needs professional voiceovers fast. Instead of spending hours recording audio or settling for robotic narrations, users can generate quality voice AI in minutes.
Voice AI: The Key to Natural Business Communication
Good communication is critical to running a successful business. The quality of your customer interactions can make or break your company’s reputation. Voice.AI’s realistic text-to-speech can help you improve your customer communications and boost your business’s bottom line.
The software creates lifelike voice-overs that sound like real human speech. The voices even capture different tones and emotions, allowing you to choose a style that matches your business’s needs and will resonate with your customers.
Speed Up Your Customer Service Processes
The longer customers wait for a solution to their problems, the more frustrated they become. Research shows that 33% of customers will never contact a business again after experiencing poor customer service. Voice.AI’s text-to-speech software can improve your customer service experience by providing faster resolutions to customer inquiries.
Instead of having a human employee greet customers and read them scripted responses to their questions, you can use our lifelike voice AI to create a more natural and efficient dialogue for your customers. Not only will this reduce the time customers spend waiting for answers, but it will also make the process feel more personal.
Personalize Customer Interactions
Customers are more likely to be satisfied with customer service interactions that feel personalized. Voice AI can help businesses create more tailored experiences for their customers.
Using Voice AI, you can create custom voice-overs that address customers by name and incorporate information from their files to make interactions sound more natural. This can reduce the repetitive nature of customer service calls and improve outcomes for both businesses and their customers.
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