What if your customer service could go from being a cost center to a revenue engine? Imagine faster resolutions, more loyal customers, and significant savings.
Today, 72% of consumers remain loyal to companies that offer quicker service. At the same time, AI has proven its worth by reducing service costs by up to 30%. It’s clear why businesses are embracing this technology.
The potential of AI in customer service is massive. It has evolved from simple automations to advanced tools that understand customer intent and deliver proactive solutions. By 2025, 80% of customer service organizations are expected to use AI to enhance experiences and productivity.
In this article, we’ll explore how AI in customer service is transforming the way businesses operate. From its benefits to real-world applications, we’ll explain how it can be applied to meet modern customer expectations. Let’s get started.
What is AI in Customer Service?
AI in customer service refers to the use of technologies like machine learning, natural language processing (NLP), and generative AI to help businesses automate ticketing triage, simplify workflows, and assist agents.
AI tools for customer service can include:
- AI chatbots and virtual agents to manage conversations
- AI-enhanced knowledge bases to boost self-help experience
- Predictive analytics to help spot customers at risk of leaving
- Sentiment analysis to gauge customer feelings during interactions
- Speech recognition for voice support
- Augmented and Virtual Reality for interactive experiences
- Knowledge base tools to quickly find answers
- Conversation summaries to keep things efficient and organize
One company seeing success with these tools is Huuuge, a global gaming platform. Using Helpshift’s customer service software , Huuuge’s agents are serving players in over 36 languages without manual translation, improving response times and boosting customer satisfaction. Huuuge also maintains a deflection rate of 79%, allowing agents to detract from mundane tickets and focus on high-value interactions.
According to Sebastian Brant, Director of Player Services at Huuuge,
“Implementing AI and automation has liberated our agents…resulting in improved metrics such as reduced TTFR, enhancing CSAT, retention, and revenue growth.”
Benefits of AI in Customer Service
According to our own studies, we are observing an adoption rate of 82%-94% for AI and automation in customer services among companies in entertainment, fintech and gaming.
And given that companies like Huuuge witness a CSAT score of 4.8 (out of 5) for VIP issues, it is unsurprising CX leaders are increasingly interested in using AI in their customer service workflows.
Let us explain to you in detail what benefits you can expect using AI for customer service.
Empowering Agents for Faster Error-free Support
Manual processes can be time-consuming for service agents, from switching between systems to retrieve customer history, to looking up information, routing field workers, and typing responses.
These repetitive tasks are also prone to human error. AI in customer service helps alleviate these challenges by offering smart recommendations across knowledge bases, conversational insights, and customer data. Access to AI assistance has been shown to increase agent productivity by 14%.
Helpshift simplifies this even further. For example, AI-driven intent detection automatically identifies the main issue in customer messages and assigns it to the right category, reducing the need for manual input.
The platform also makes it simple to set up self-service options. This allows customers to find answers through AI agents or QuickSearch bots when needed.
Additionally, Helpshift automatically creates summaries of customer conversations. This gives agents a quick overview of the issue, including what has been tried and key details, so they don’t need to review every message.
With everything in one easy-to-use dashboard, agents can respond quickly and manage conversations across multiple channels more effectively.
Personalized customer service
71% of consumers expect companies to offer personalized interactions. On top of that, 76% feel frustrated when they don’t. That’s why to be truly effective, AI understands the customer by collecting relevant company data.
When a customer starts a conversation with a chatbot, AI can quickly extract important details—like the customer’s name, location, account type, and preferred language in real time.
Let’s understand this with an example. Ovuline, a company helping women through conception and pregnancy, uses AI to offer highly personalized support.
With over 100,000 couples assisted, they needed a way to provide intimate, responsive service during emotional moments.
Before Helpshift, Ovuline struggled with disorganization and a lack of personalized support. The switch to Helpshift allowed them to offer real-time, in-app messaging, creating a more personal, immediate connection with users.
Features like image attachments, such as ovulation calendars, helped agents give more relevant and accurate advice. The FAQ voting system also lets Ovuline prioritize the most helpful answers.
Faster response and resolution times
AI can streamline workflows that lead to faster response times and better customer service metrics. In fact, 70% of service leaders say AI helps reduce the time spent resolving issues.
Take customer service chatbots, for example. They can instantly reply to customer inquiries in live chat. This drastically lowers the first response times.
Due to this your team’s average handle time decreases as AI helps resolve requests more quickly.
AI-powered insights for understanding customer behavior
Without AI, brands rely on traditional methods like surveys and feedback forms, which often fail to provide a complete view of customer behavior. These approaches can miss underlying trends, such as how customer preferences shift over time or what factors drive their decisions.
73% of consumers say customer experience directly influences their purchase choices. This means brands that don’t fully understand these behaviors risk losing valuable customers. In fact, one in three customers would leave a brand they love after just one bad experience.
AI tools can change this by analyzing data such as customer interactions, support requests, and player behavior. This allows businesses to resolve issues more quickly, predict potential problems, and deliver a more personalized support experience.
A great example of this is Playrix, an international game development company that faced challenges as its player base grew.
The company’s outdated email-based customer service system couldn’t keep up with increasing support requests, leading to dissatisfaction and potential lost players.
To address this, Playrix turned to Helpshift, which provided a fully-native in-app messaging experience.
This allowed Playrix to offer support without players leaving the game.
This integration helped Playrix better understand its players, using data like device type, game level, and current progress.
Helpshift’s solution also included a three-pronged support strategy:
- Using FAQs for common issues
- Micro-bots for more complex ones
- Human agents for the toughest problems
This approach made support more personalized and efficient, greatly improving player satisfaction.
Reduced employee fatigue
Even with the advent of Gen AI (and whatever comes next), if you’re making agents spend time on simple ticket requests, you’re setting up both their morale and your bottom line in the throes of failure. Even customers themselves don’t want to talk to agents to resolve issues. 61% of customers would prefer to use self-service to resolve simple issues, reports Salesforce.
AI-enabled knowledge bases and self-service solutions can save several hundreds of hours for your agents. This lets your team focus on more important work without sacrificing quality or efficiency. Salesforce reports that 79% of IT leaders believe generative AI will help ease team workloads and reduce burnout.
Closer to home, we too found our AI-powered Quick Search Bot increased deflection rates and reduced agent workload. Quick Search Bot (QSB) uses the user’s message to
surfaces the top self-serve solutions in the player’s native language using multilingual machine learning. The image below describes how the average deflection rates have increased y.o.y.
ALT text: Quick search bot average deflection rate YoY chart
24/7 Customer Support
AI in customer service, such as chatbots, can operate around the clock, providing 24/7 support. This is especially useful for customers in different time zones or those needing assistance outside regular business hours.
By using AI-powered systems, businesses can manage a high volume of customer inquiries—like responding to questions, processing requests, or offering troubleshooting advice—without sacrificing service quality or response times.
Examples of AI in Customer Service
Although AI in customer service is not new, many companies are still figuring out how to implement it. Here are some examples to include AI in your customer service operations.
AI-Powered Chatbots
According to Gartner, by 2027, chatbots will be the main customer service channel for about a quarter of organizations. Unlike traditional, scripted chatbots, these AI chatbots offer a more natural, conversational experience. This shift reflects the increasing importance of AI in customer service.
These chatbots use advanced technologies like Natural Language Processing (NLP) and machine learning. This helps them understand and respond to customer queries instantly.
They can assist with a wide range of tasks like:
- Answering common questions
- Helping users navigate websites
- Diagnosing and resolving typical issues
Helpshift, for example, goes further with its AI-powered customer service platform. Helpshift’s intelligent chatbots use AI to understand customer needs and route them to the right solutions, offering personalized answers.
They can handle issues and resolve problems on their own, passing more complex cases to human agents when needed.
Helpshift’s Smart Intent AI is constantly trained with customer data and knowledge base content. This ensures chatbots provide the most useful and accurate responses.
In fact, Helpshift’s case studies show that the platform resolves 70% of customer queries by itself.
Helpshift uses Generative AI to pull accurate answers from your knowledge base. This AI can adapt to different customer situations and handle complex questions. However, like any AI, it can sometimes create misleading responses.
To reduce these issues, Helpshift uses a Retrieval-Augmented Generation (RAG) approach with its Quick Search Bot. RAG adds extra context to the Large Language Model (LLM) prompt, which helps the AI give more accurate and reliable answers.
This system learns over time from customer interactions, improving its responses. For complex questions, it can also pass them to support agents when needed.
AI automation in customer service ticketing
AI-powered automated customer service ticketing systems sort and prioritize tickets automatically, routing them to the right team based on the urgency and content.
By analyzing ticket text with natural language processing, AI understands the context and directs it accordingly. This leads to quicker, more accurate responses.
Take Stitcher, a podcast platform with over 12 million downloads, for example. They struggled with slow response times and repetitive queries, as many users weren’t using the FAQ page effectively. This led to a backlog of support requests.
Helpshift, through its AI-powered solution, helped Stitcher improve its support process.
By introducing automated ticket routing and pre-formulated answers for common questions, Stitcher was able to reduce ticket volume by 70%. What used to take 25 hours a month to manage now took just 8.5 hours, freeing up time for other important tasks.
Sentiment analysis using AI for customer feedback
AI-driven sentiment analysis uses natural language processing (NLP) and machine learning to assess the emotional tone in tickets, customer feedback, reviews, and interactions.
For example, Helpshift’s Sentiment Analysis feature detects the emotional tone of customer conversations. Not only does this help support teams prioritize and adjust responses , they can also identify changes in sentiment over time. As a result, sentiment-charged tickets are met with highly customized responses in a short time.
Alt text: Helpshift’s unified workspace dashboard
Multilingual customer support
Over 70% of customers expect service in their preferred language. Not meeting this need can lead to frustration and affect loyalty. Multilingual support helps companies offer consistent, quality service to diverse audiences, regardless of language or location.
This often means localizing content and offering multilingual options like website chat, in-app chat, and email support.
For example, Halfbrick used Helpshift’s multilingual chatbot to handle support in over 12 languages. This solution helped them manage high ticket volumes and reduce response times by over 40%, improving the player experience.
Helpshift also helped identify 80% of the key issues impacting game quality, benefiting both customers and product teams.
Helpshift’s Language AI takes this further by using advanced, in-platform machine translation, which has a lower error rate than Google Translate.
Language AI adapts to brand-specific terms in multiple languages and can avoid certain words when needed, allowing agents to reply accurately in their own language.
Omnichannel Support
Omnichannel customer service connects all communication channels—phone, email, chat, and social media—into a single experience. This way, customers can switch between platforms without losing track of the conversation.
With omnichannel support, companies get a full view of each customer by pulling data from all channels into one profile. This helps support teams respond faster and more personally, while customers can reach out however they prefer without losing context.
Though setting up these systems can be complex, the benefits are clear: improved customer satisfaction, easier operations, and a consistent brand experience.
Helpshift takes this even further. Its AI-powered platform connects channels, supports multiple languages, and recognizes customer intent, ensuring that each interaction feels personal. With Helpshift, companies can manage all customer touchpoints in one place, making every conversation smooth and meaningful.
Provide proactive guidance to agents
AI-powered tools help agents work more efficiently, speeding up issue resolution with response suggestions personalized to each customer. This support is especially useful for new agents during onboarding.
For example, Helpshift’s agent co-pilot guides agents through every step of an interaction. It fits right into their existing workflows to enable faster and more accurate responses.
With Issue Summary, Helpshift condenses customer conversations into clear summaries that capture key details, past resolutions, and additional context. This gives agents a quick overview, so they can respond faster without needing to read through entire conversations.
Plus, Helpshift’s queue management system improves both flexibility and speed, keeping agents and customers happy.
It offers features such as:
- Auto Assignment: Automatically routes issues to the right teams, helping manage workload.
- Queue Monitoring: Live updates on queue and team activity let you adjust resources instantly.
The Advanced Analytics dashboard gives support teams insights into key metrics like self-help success and agent productivity. A live dashboard offers real-time visibility into queues and team activities, helping managers keep service consistent and efficient.
What to Consider While Implementing AI in Customer Service?
Even though AI in customer service has its benefits, many organizations are still slow to adopt it. Here’s what might be stopping them:
Workforce Impact
Since AI, particularly generative AI, is still new, many service leaders are facing a skills gap. In fact, 66% of leaders feel their teams aren’t fully equipped to work with AI.
At the same time, 30% of service professionals worry that technology could replace their jobs, making them hesitant to adopt it.
Eric Vermillion, the CEO of Helpshift, addresses this concern in his blog “How To Make Generative AI The Robin To Your Batman …” by highlighting that while the rise of generative AI may seem difficult, it’s important to understand that AI isn’t here to replace humans—it’s here to make us more efficient.
At Helpshift, we understand the concerns around AI and its potential impact on jobs. It’s natural to worry that AI might replace human workers, especially with the rise of generative AI.
However, Generative AI is a tool, just like any other technology we’ve seen throughout history. For example, when the steam engine was invented, people feared it would eliminate jobs. But what actually happened is that it created new opportunities.
The same is true for AI. Yes, AI can perform tasks that were once time-consuming. However, it still lacks the creativity, empathy, and judgment that humans bring to the table. Those are qualities no machine can replicate.
Instead of replacing people, AI helps us work smarter. It can automate repetitive tasks like research or basic customer support, and craft more personalized responses at scale. By handling the routine tasks, AI lets us apply our human skills in areas that require creativity and strategic thinking.
Trust and Reliability Issues
While AI is improving at a fast pace, it’s still not without flaws. For example, a lot of language models are based on data that’s nearly two years old, which can impact their ability to handle current customer needs.
Additionally, there are concerns about AI’s accuracy when it comes to complex queries or sensitive information. Privacy and trust issues are also major factors, and businesses need to address them carefully to protect both customer and business data.
The good news? By using trusted CRM data and a solid knowledge base, these challenges can be tackled more effectively.
Investment and Implementation
Deciding whether to build your own AI or use customer service software with AI can affect your budget. Developing AI from scratch can be expensive. It also requires a lot of training and the right tech setup.
Small businesses or those with limited resources might find it hard to afford these costs. They may also lack the skills to set up and manage the AI systems properly.
Future of AI in Customer Service
AI in customer service is growing rapidly, with more use cases emerging. For example, generative AI is moving from the contact center to the field. It gives frontline teams the right customer and service data they need.
AI also handles tasks like work summaries and knowledge articles. This enables teams to focus on higher-priority tasks, such as solving complex customer issues, improving customer relationships, and developing new service strategies.
These changes are just the beginning. Let’s explore three more trends shaping the future of customer service.
Metrics will focus on value, not just efficiency
AI can make your business run more efficiently, saving money. But focusing just on efficiency won’t always improve customer service. While efficiency helps, offering real value to customers has a bigger impact. Meeting their needs leads to more sales, loyalty, and repeat business, which helps grow your bottom line.
AI can help you deliver both value and efficiency. For example, Helpshift uses AI to analyze customer sentiment and identify language barriers. This helps agents understand emotions and communicate effectively in multiple languages, improving the overall customer experience.
American Express uses AI to detect fraud instantly. The AI can make decisions in milliseconds, something humans can’t do. It doesn’t save time or reduce staff, but it provides real value by protecting customers from fraud.
Social listening and engagement
AI tools are essential for customer self-service and gaining actionable insights. Social listening, powered by AI, is one such tool that helps brands better connect with their audience. In fact, 61% of businesses use social listening tools today, and over 82% of marketers see it as a critical part of their social media strategy.
AI social listening algorithms track data from blogs, social media, and forums to help brands understand customer concerns and preferences. This enables businesses to stay ahead of trends, anticipate customer needs, and build loyalty. By using these insights, brands can improve engagement and provide better customer experiences.
Emotional intelligence (EQ) in customer service
The role of Emotional Intelligence (EQ) is growing in customer service, alongside traditional IQ. This change isn’t just about human agents; AI systems are getting smarter too.
Emotional AI, which can understand and react to human emotions, is reshaping customer interactions. From empathetic chatbots to AI-powered CRM systems, these technologies are built to provide more personalized support and improve customer loyalty.
Power your AI in Customer service with Helpshift
Helpshift is revolutionizing customer service with AI. With AI-powered chatbots, intent detection and classification, real-time translation, and self-service options, Helpshift makes customer support faster and easier for both agents and customers. Their platform allows businesses to offer 24/7 assistance, streamline support, and provide personalized responses. By analyzing customer history and trends, Helpshift’s AI even predicts what customers might need next, making each interaction feel thoughtful and proactive.
If you’re ready to explore how AI can transform your customer support, Helpshift is here to help. Book a demo today to see how Helpshift’s AI can elevate your support experience.
Check Delivering of Better Player Support with Helpshift and Generative AI
Frequently asked questions
What industries benefit the most from AI in customer service?
Industries like retail, banking, telecommunications, and healthcare benefit significantly from AI in customer service, as it enables faster responses, personalized recommendations, and 24/7 support.
How is AI used in customer service?
AI is used in customer service through chatbots, virtual assistants, and automated responses, helping businesses answer common questions, assist customers in real-time, and personalize interactions.
Will AI replace support agents?
AI is unlikely to replace support agents entirely; instead, it will handle simpler queries and repetitive tasks, allowing human agents to focus on complex, emotionally sensitive issues.
Do customers prefer chatbots?
While many customers appreciate chatbots for quick, straightforward queries, they still prefer human support for more complex or sensitive issues, suggesting a need for a balanced approach.
