What is a perfect customer service agent? Someone who never sleeps, can handle thousands of conversations at once, and remembers every single one perfectly. You would be right to say a person like that doesn’t exist. But what if it wasn’t a person?!
AI agents are fundamentally changing customer service. It’s not by replacing humans but by creating a new kind of partnership between technology and people. What used to take days now happens in seconds, and what seemed impossible just a few years ago is becoming a standard practice.
AI customer service is transforming how businesses think about their interactions with clients, and the numbers we’re seeing are turning heads across industries. At Neontri, we’re helping companies navigate this shift, and in this article, we’ll explain how we do it.
Key takeaways:
- While a human agent handles 50-60 inquiries daily, AI processes this volume in under an hour. This translates to roughly 30% reduction in support costs.
- By 2027, about 25% of businesses will use AI as their primary customer service channel – but the key is combining AI efficiency with human insight, not choosing between them.
- AI analyzes customer history and behavior to deliver tailored experiences automatically. This leads to a 5-15% revenue increase and significantly higher customer retention rates.
Understanding AI customer service
AI in customer service combines machine learning (ML), natural language processing (NLP), and generative AI (GenAI) to make customer service interactions more effective. Think of it as having a smart digital teammate that helps handle customer conversations and tasks.
Recent data and real-world implementations paint a compelling picture of where the industry is headed. Let’s look at what leading companies and customers are saying about customer service and AI:
- We are heading to the future where 100% of customer interactions involve some form of AI. 70% of industry leaders see AI agents becoming skilled architects of highly personalized customer journeys. This isn’t just hype – customers also share this belief. 59% of consumers believe this technology will transform how they interact with companies over the next two years, and this number jumps to 75% among people who’ve already experienced generative AI customer service. (Zendesk)
- The launch of Microsoft Copilot is a real workplace example of how AI assistants can improve the productivity of support agents (70%) and the quality of their work (68%). About 67% of users say it allows them to dedicate more time to customers, 64% can better personalize customer engagements, and 85% can create a good first draft faster. They also see major time savings. 64% of Copilot users say it helps them spend less time processing emails, while another 75% claim it saves time by finding relevant customer data in their files. (Microsoft)
- We’re entering the agentic AI era. NVIDIA and partners are creating blueprints for this next wave of technology that can tackle more sophisticated customer service challenges. Think of it as AI that can handle complex, multi-step problems through better reasoning and planning. (NVIDIA)
Key benefits of AI in customer service
Companies are leveraging AI to streamline their support operations and make customer service experience better, smarter, and more personal than ever before. The technology helps businesses better understand customer needs, allowing for more context-aware interactions. It also promotes self-service solutions, making information easily accessible and reducing the need for human intervention.
Let’s take a closer look at the advantages of this technology for businesses.
Higher productivity
Every day, companies handle thousands of support tickets. Their agents are swamped with questions like “How do I reset my password?” or “Can you provide more details about this product?” For routine inquiries like this, AI becomes a support team’s smart assistant. It handles repetitive tasks, letting the support specialists tackle complex issues that actually need human expertise.
Companies using AI assistants were able to boost agent efficiency by an average of 14%. The numbers were even higher in entry-level or low-skill employees, who experienced productivity jumps of 34%.
24/7 customer support
AI never sleeps. So customers get answers in seconds rather than hours. When someone asks “Where’s my order?” at 3 am, the customer service chatbot can track the required info instantly. It can pull up the client’s order history and check for standard solutions. With 24/7 support, customers get help even when the support team is off for the night.
Reduced agent burnout
The real power of AI in customer service isn’t about replacing humans – it’s about empowering them. By automating routine tasks, artificial intelligence frees agents to focus on meaningful customer interactions. So, instead of answering the same question 50 times a day, support specialists get to solve interesting challenges and build real connections with people.
As a result, 79% of industry leaders believe that AI adoption will help decrease staff workload and reduce burnout. 35% of companies currently using GenAI have already noted that service professionals are less overwhelmed by the information in front of them during calls. They also report 56% higher agent productivity and a 2.5 times increase in employee satisfaction.
Cost efficiency
Customer service AI is making a huge impact on cost reduction and efficiency. Here are some some examples:
- Labor costs. Training and hiring more support specialists is expensive. However, by using AI for customer service, instead of adding ten new agents to handle growth, companies might add just three and let artificial intelligence take care of routine tasks.
- Automation. Modern contact centers are finding smarter ways to balance automation with human connection. By using AI for simple tasks and connecting agents to customers only when truly needed, companies can cut cost per contact by 9%.
- Improved efficiency. One AI system can deal with thousands of customer queries at a fraction of the cost. While a human agent handles about 50-60 inquiries daily, AI processes this volume in less than an hour. This allows companies to reduce support costs by approximately 30%.
- Personalization. Using AI for customer service personalization can increase revenues by 5%-15% and marketing ROI by 10%-30%.
Top applications of AI in customer service
As businesses strive to meet growing customer expectations, AI has emerged as a powerful tool for delivering faster and more efficient support. In this section, we’ll explore the top applications of AI in customer service, highlighting how businesses can leverage technology to enhance customer satisfaction and stay ahead of the competition.
Chatbots
Chatbots are the front line of customer support. Gartner predicts that by 2027, they will become the main customer service channel for about 25% of companies. Modern AI solutions can understand natural language and respond instantly to questions about order status, account information, or product details.
This benefits both support agents and customers. For service professionals, it’s like having a personal assistant that does all the tedious work. For customers, AI provides instant support. It can understand service inquiries and provide relevant responses.
But chatbots are more than just glorified FAQ machines. Modern AI can tell when a customer is angry and flag it for immediate human attention. It’s smart enough to know when it’s out of its depth and automatically escalate complex issues to support specialists. This approach combines the automation with the empathy and problem-solving skills that only people can provide.
Intelligent ticket management
AI can help the team identify precisely who should handle which customer problem. Instead of manually sorting through support tickets, the customer service platform instantly analyzes the content of each request. It then automatically directs it to the most qualified agent based on expertise, language skills, and current workload.
AI looks at customer history, issue type, urgency, and complexity. For basic support operations, like password reset, it might trigger an automated solution. But if, for example, someone writes an email about a failed payment, the system marks it as urgent and sends it to a senior financial specialist.
Sentiment analysis
Thanks to AI’s ability to process user feedback across multiple channels, companies can analyze customer sentiment in real time. This helps them understand how people feel about their products and respond to those emotions.
By scanning messages, calls, and chat logs, AI detects subtle language patterns that reveal customer attitudes. When a client shows signs of irritation, the system can flag the interaction and pass it on to a human agent to prevent negative experiences from escalating. The technology can also spot positive customer sentiment that can be used for brand awareness campaigns.
Personalized customer experience
AI learns what clients like, how they shop, and what they might want next. It analyzes data from customer behavior, interactions, and engagement history to deliver exceptional service experiences.
According to McKinsey, 71% of consumers now expect personalized experiences. Failure to provide it can lead to frustration and customer churn in 76 % of shoppers. Meanwhile, effective use of AI personalization can cut acquisition costs by half while increasing customer engagement and loyalty.
AI is transforming how businesses connect with their clients by creating tailored experiences. Here are some examples:
- Product recommendations
Based on customer preferences, browsing history, and purchase patterns, AI can capture people’s intent and suggest products that align with their tastes. This feature is quite popular with clients: 42% of users are keen on receiving personalized product recommendations from AI. Among the Gen Z population, this number rises to 66%.
- Intelligent content
By leveraging AI for customer service, companies can create engaging messages that resonate with their target audiences. They can deliver personalized emails, articles, product descriptions, or text messages. According to Hubspot, 63% of marketers that use generative AI for content generation say it performs better than media assets created in a traditional way.
- Ad targeting
AI helps companies target their ads to people most likely to be interested in them. Instead of showing everyone the same staff, the system looks at what people like, what they buy, and how they browse online to make better choices about the messaging. For example, if a person was researching camping gear, AI might show them ads for tents and outdoor equipment. If someone often shops late at night, it shows them ads during those hours.
- Dynamic pricing
AI-powered pricing mechanisms automatically adjust prices up or down based on what’s happening. The system watches several things, such as local demand, time of day or season, and stock availability. It can also take into account market conditions: what competitors are charging, currency fluctuations, and industry-specific supply chain disruptions.
Knowledge base management
A knowledge base is a repository that includes essential information such as product guides, FAQs, troubleshooting, tutorials, and company documentation. Many organizations incorporate broader industry-related content, which serves existing clients and can also attract potential ones seeking valuable tips and insights.
Here, artificial intelligence acts as a librarian who never forgets and constantly organizes the company’s assets. It helps manage both customer-facing guides and internal documents (like HR policies and procedures).
When users seek help, an AI-powered system understands the intent behind their questions and directs them to the most relevant resources, even if they don’t use exact keywords. It also constantly scans support tickets, chat logs, and customer conversations to check what information is missing. When AI spots something that’s not covered, it can automatically draft new knowledge base articles.
The use of technology extends beyond AI-powered knowledge bases for customer self-service. Companies leverage digital assistants to maintain their internal knowledge articles so that employees always have access to current information about company policies, benefits, and procedures.
Quality assurance
Quality assurance has moved beyond random sampling of client interactions thanks to AI automation. Support teams can now analyze customer conversations in real time, ensuring consistent service quality and compliance with company standards.
Here’s how it works: AI listens for specific things that make great customer service – like proper greetings, empathy phrases, and accurate solutions. When an agent forgets to verify a caller’s identity or misses an opportunity to offer additional help, the system flags it instantly. For example, if a client mentions being frustrated three times but the support specialist doesn’t acknowledge it, AI can prompt them to show more empathy.
The system can also identify successful conversations that can be used for training purposes. This helps managers explore what top performers do differently and see opportunities for improvement that would be impossible to discover otherwise. By reviewing all customer interactions, AI can highlight areas where agents might need additional coaching or support.
Best practices for AI-powered customer service: A step-by-step implementation process
Implementing AI in customer support is not just about choosing the right tech. It’s about data, team readiness, and integration with existing systems. Each step presents its own challenges that organizations must address. To avoid common mistakes when using AI for customer service, businesses need to understand implementation is a continuous journey of learning, adaptation, and refinement rather than a one-time setup.
Pre-implementation planning
The journey begins with thorough preparation. First up is optimizing the knowledge base. You need to ensure your documentation is in shape. Well-structured, concise documents equal a clear training manual for your AI model.
Instead of long, rambling files, you need bite-sized, focused guides that explain one topic at a time. For example, rather than one massive “Returns policy” document, break it down into specific scenarios like “How to return damaged items” or “International return process.” This helps AI quickly find the right information and give immediate answers to customer questions.
But there’s a human side to this preparation, too. When companies mention bringing in AI, customer service teams often worry about the impact of new technology on their daily work. That’s why it’s crucial to be clear about its real role: handling the repetitive stuff so agents can focus on more rewarding work. Show your employees how AI will make their job better, not replace them.
Data structuring and labeling
The next step is to add your proprietary data to the system. You should either prepare a dedicated file with all the necessary information or pool the data from your customer relationships applications, such as CRM.
Raw data should then be cleaned and standardized for better accuracy and consistency. This involves organizing various data types – from customer feedback and service preferences to past support interactions – into specific groups. Each piece of information needs proper labeling according to relevant categories, such as product information, billing, customer requests, purchase history, etc., to give the AI model examples to learn from.
Training the AI model
The training process ensures your AI helper gets smart enough to actually serve customers. Here is how it goes:
- Select a training model. Once you’ve set up your data structure, you’ll need to select an AI model architecture that aligns with your specific needs, be it chatbots, email management, call centers, or sentiment analysis. Depending on the use cases for AI in customer service, your training model will require different project parameters, amounts of resources, and compute power.
- Divide the dataset. You’ll need to split your data into two parts: a larger portion for training and a smaller one for validation. The training set helps the model to learn patterns and relationships within the data, while the validation set is needed to verify the model’s accuracy and prevent overfitting.
- Perform initial training. Feed your data into the system and let AI agents learn to understand customer queries and deliver relevant responses. The model’s accuracy and performance are going to improve over time, so you will get optimal results within the expected parameters.
- Maintain human oversight. Implement regular human review to validate the model’s predictions and identify any false positives or negatives that need to be addressed.
- Test the model. Before full deployment, it’s important to conduct pilot testing to evaluate the AI system’s performance. If the model doesn’t meet the required standards, the training process repeats until AI can handle complex customer requests on its own.
Integration with existing systems
Integrating AI tools with customer service infrastructure requires a strong technical setup. The best way to achieve that is through Application Programming Interfaces (APIs), which link different software components. These API connections allow real-time data exchange between AI and existing applications.
To increase workflow efficiency and quality of customer interactions, AI customer support tools should have access to the CRM platform. By using data stored there AI can provide relevant responses and more personalized service, drawing on customer history.
The integration should also include mechanisms for handoffs between AI and the support team. When a customer asks something AI can’t answer, the system should be able to transfer the conversation to human agents along with all relevant contexts.
Key challenges in AI and customer service integration
AI in customer service brings powerful capabilities, but success hinges on addressing several crucial issues. Here’s an overview of the main challenges businesses face when implementing artificial intelligence in their support operations.
Data quality
Businesses collect tons of information, from basic customer details to Instagram comments and recorded service calls. Some pieces are neatly organized (like customer profiles), while others are unstructured and hard to use (like social media conversations). Data silos complicate matters further, as valuable insights are often scattered across different departments.
Imagine a company training agentic AI for customer service on incomplete customer information or biased feedback. That might cause mistakes, misguided business decisions, and unfair customer treatment.
Since poor data can lead customer service AI down the wrong path, data scientists must vet and categorize any information before feeding it to the system. They ought to check that the data reflects real customer situations the company aims to address. For example, when training AI for banking customer service, the training set should include examples of typical transactions, fraud cases, and customer service scenarios they deal with daily.
Team training and skill development
A recent study by Salesforce indicates that 62% of desk workers say they lack the skills to effectively and safely handle AI. Organizations must invest in well-rounded training programs that teach tech skills and help teams understand how to use AI tools efficiently. This includes developing new competencies in data analysis, AI system management, and enhanced problem-solving capabilities.
Resource investment
The more advanced the AI model, the more computational resources and infrastructure support are required. Meeting the demand for tech capacity needed for training and running AI models, like powerful hardware and scalable cloud infrastructure, can be resource-intensive and expensive.
You need to consider the practicality of ongoing maintenance when selecting the model type. If a model requires more resources than what can be delivered, the whole project will collapse.
Performance monitoring
Use clear metrics to measure AI effectiveness. These are response accuracy, customer satisfaction rates, and resolution times. Regular performance reviews and system tuning are necessary to ensure AI delivers consistent service quality and exceptional customer experiences.
Ethical considerations
You should be transparent about using AI for customer service. This means being clear when customers chat with AI versus service professionals and providing options for those who prefer human interactions. You should also regularly audit your AI systems for bias and make adjustments as needed to maintain ethical service delivery.
Provide exceptional support with AI solutions from Neontri
As businesses enter the world of AI-driven customer support, partnering with a technology provider like Neontri can be a good move. Our team brings 10+ years of experience building and implementing GenAI solutions for the fintech, retail, and banking industries.
As a custom software development provider, we offer guidance and support at every step of your AI journey. By working with Neontri, you can:
- access cutting-edge AI tools and technical expertise;
- ensure compliance with regulatory requirements;
- seamlessly integrate AI solutions with their existing systems;
- benefit from continuous improvement of AI capabilities.
Wrap-up
As AI technology continues to advance, its applications are becoming more sophisticated. Companies can now provide personalized support at scale while maintaining high-quality standards and reducing response times. And the potential benefits – from improved customer satisfaction to more engaged support teams – make it worth the investment.
As AI technology evolves, staying flexible and maintaining a learning mindset will be crucial. You should keep monitoring performance, gathering feedback from both customers and staff, and adjusting your approach accordingly. With proper preparation, clear goals, and continuous refinement, AI can become a powerful tool in your customer service arsenal, helping create better experiences for everyone involved.
If you would like to receive more tips on maximizing AI’s potential as your customer service solution, send us a message using the contact form below.
Resources
- https://www.microsoft.com/en-us/dynamics-365/blog/business-leader/2024/08/29/elevating-experiences-with-ai-from-productivity-to-personalization/
- https://www2.deloitte.com/us/en/pages/about-deloitte/articles/press-releases/deloitte-digital-research-identifies-behaviors-of-contact-center-service-innovators.html
- https://www.salesforce.com/news/stories/generative-ai-research/
- https://www.deloittedigital.com/content/dam/digital/us/documents/insights/insights-20240501-gcs-survey-charticle.pdf
- https://www.qualimero.com/en/blog/ai-customer-support-savings
- https://www.gartner.com/en/newsroom/press-releases/2022-07-27-gartner-predicts-chatbots-will-become-a-primary-customer-service-channel-within-five-years
- https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-personalization
- https://www.surveymonkey.com/curiosity/3-key-trends-in-ai-and-cx-what-to-do-and-what-to-avoid/
- https://blog.hubspot.com/marketing/how-ai-can-improve-your-customer-experience
- https://www.salesforce.com/news/stories/generative-ai-skills-research/
