AI in customer service: benefits, examples and use cases
Modern user support is undergoing a profound transformation thanks to AI in customer service. The use of modern technologies makes it possible to solve common problems in the work of technical support. Neural networks make it possible to respond quickly and around the clock, constantly analyze user requests and look for patterns in them. Using AI in customer service is becoming a necessity rather than following a popular trend. We will discuss the real benefits, specific examples of implementation, and strategies for the effective use of AI in customer service.
What is AI in customer service?
Artificial intelligence in the client service allows using modern technologies to communicate with users. User support services use machine learning, natural language processing, automation, and generative AI to study a query and select an answer to it. The main task is to speed up the speed of responses and improve their accuracy.
The most common forms of AI application are:
- Chatbots and virtual assistants. They respond to typical customer requests, reduce the burden on specialized specialists, which increases the efficiency of the support service.
- Predictive analytics. Analyzes customer behavior and looks for patterns in it.This allows you to predict possible problems and take measures to solve them in advance.
- Automation of work processes. Speeding up routine tasks. This can be updating CRM records, sending notifications, maintaining customer communication reports, and other tasks.
- Sentiment and behavioural pattern analysis. Helps to better understand customer emotions and preferences, forming the basis for effective communication.
Modern AI goes beyond simple programming and learns from every interaction, adapting to unique scenarios and individual customer characteristics.
Advantages of AI in customer service
The popularity of AI in working with clients is based on the advantages that businesses receive.
Speed and continuity of support
24/7 availability. AI services operate around the clock, eliminating delays and ensuring immediate responses to requests.
Minimisation of waiting time. Automating the first steps of request resolution reduces the average processing time by 30-50%.
Quick response to spikes in activity. AI is easily scalable, supporting high volumes of requests without increasing staff numbers.
Cost efficiency
Reduced operating costs. Automating simple tasks frees you from hiring additional staff. This can result in up to 30% savings on labor costs.
Budget optimisation. Reducing operating costs frees up funds for business development, closing current debts and increasing profitability.
Improved customer experience
Personalization. AI analytics makes it possible to analyze previous customer requests and offer personalized suggestions. This increases the conversion rate, as users get what they were previously interested in.
Increased satisfaction. Personalised support increases CSAT by 15–20%.
Empathetic approach. Modern AI models can determine the customer’s emotional state and adapt their communication style accordingly.
Operator optimisation
Freedom from routine. Automating standard procedures allows specialists to focus on complex and creative tasks.
Support in decision-making. AI suggestions and prompts speed up request processing and reduce the likelihood of errors.
Professional development. Employees have more time for self-development and learning new skills.
Analytics and forecasting
Identifying hidden trends. AI analytics detects patterns invisible to the human eye, allowing you to prepare for changes in demand in advance.
Evaluating campaign effectiveness. Analyses the success of marketing initiatives and adjusts strategy in real time.
Risk management. Warns of potential problems and failures, allowing proactive measures to be taken.
Examples of artificial intelligence in customer service
Let’s look at some examples of AI in customer service:
- Chatbots and virtual assistants. Answer frequently asked questions such as ‘Where is my order?’ and ‘How do I return an item?’ They route complex requests to the appropriate specialists. They integrate with CRM for access to customer data.
- Predictive analytics. Predicts customer churn based on behaviour patterns. Recommends products: ‘Customers who bought this item also bought...’. Assesses the likelihood of cross-selling success.
- Natural language processing. Analyses the tone of reviews: highlights negative comments for urgent response. Automatically tags requests: ‘complaint,’ ‘delivery question.’ Translates texts in real time for multilingual support.
- Workflow automation. Fills out refund forms. Generates responses based on a knowledge base. Updates order statuses in systems.
- Voice AI assistants. Interactive voice menus: ‘Say “balance” to find out your balance.’ Speech recognition in call centres: transcribing conversations, highlighting key phrases. Voice assistants for booking services: table reservations, doctor's appointments.
These examples of artificial intelligence in customer service show how widely the technology has been adopted and how it simplifies customer interactions.
Cases of AI use in customer service
International experience shows that artificial intelligence has become an integral part of customer service in various sectors of the economy. Below are some striking examples of the successful implementation of AI technologies in foreign companies.
E-commerce
One of the world’s leading e-commerce companies, Amazon, has implemented an AI-based personalisation mechanism called Amazon Personalise. This technology has revolutionised product recommendations by using generative AI to create a unique user experience. As a result, the company increased its sales by up to 35% compared to organisations that do not use such personalisation.
The American company US Foods Inc., which is engaged in wholesale food distribution, has successfully applied AI to optimise its e-commerce. As a result, sales grew by 6.4% compared to the previous year, reaching $37.877 billion in 2024.
Telecommunications
Telecommunications giant Verizon has implemented an AI assistant, developed based on Google models, to support its customer service employees.
This has resulted in a significant reduction in the length of phone calls and freed up staff time for active sales. In just one year, sales through the customer service department have grown by almost 40%. Spanish telecommunications company Telefónica has implemented its own project called Kernel, using Next Best Action AI Brain technology, which analyses customer behaviour and offers personalised recommendations.
This innovation resulted in an increase in sales of almost 20% and a growth in the conversion rate of approximately 30%.
Financial sector
The well-known American financial conglomerate JPMorgan Chase has successfully used artificial intelligence tools to maintain sales volumes to its wealthy clients even during periods of serious economic turmoil. Mary Erdoes, CEO of the division, emphasised that advanced AI tools helped financial advisors respond quickly to market changes and maintain a high level of customer service.
AI chatbots examples
JetBlue chatbot helps travelers book tickets, check flight status, and answer frequently asked questions. Thanks to the intuitive design, users can easily navigate complex travel requests, which improves the quality of the airline’s customer service.
Other areas
French technology consulting company ACI Corporation implemented advanced AI-powered sales techniques. The model provided managers with real-time prompts on various aspects of the sales process, including negotiation and product knowledge. The result was a noticeable improvement: an increase in sales conversion from less than 5% to 6.5%, an increase in the number of qualified leads from 45.5% to 64.1%, and an improvement in product knowledge from 24% to 34.6%.
Canadian telecommunications company Rogers Communications integrated advanced AI-powered sales techniques into its core sales cycle. This enabled it to achieve 80% accuracy in sales forecasting and 90% accuracy in forecasting losses at the beginning of the sales cycle.
International experience confirms that artificial intelligence can transform customer service, making it more efficient, personalised and focused on meeting the needs of today’s consumers.
How to implement AI in customer service
To implement an AI client service, you need to choose reliable software and follow simple recommendations.
Step 1: Audit the current state
It is necessary to study the effectiveness of support and identify the main disadvantages and what can be improved. This will require collecting data on the number of requests, types of requests, and the load on operators. Next, you need to identify tasks that can be automated.
Stage 2: Select priority scenarios
Select the most repetitive and low-cost tasks for initial implementation. Test the selected scenarios on a limited sample of customers. Monitor the results and adjust the settings.
Stage 3: Integration with existing systems
Connection to CRM, billing systems, and knowledge bases. Configuration of APIs and data transfer protocols. Verification of compatibility and stability.
Stage 4: Testing and optimisation
Conducting A/B tests to compare the effectiveness of AI and traditional methods. Continuous monitoring of key metrics: FRT, CSAT, share of automated queries. Adjusting models and algorithms based on the data obtained.
Key success metrics:
- First response time (FRT) – the minimum value indicates high service quality.
- Customer satisfaction (CSAT) – reflects the customer's perception of the quality of services provided.
- Percentage of automated requests – a high figure indicates successful implementation of the AI strategy.
Possible implementation errors
Insufficient model training – ignoring the calibration stage leads to incorrect results. Overestimating the capabilities of AI – attempting to replace humans in areas that require empathy and a creative approach. Ignoring human control – the lack of a backup mechanism worsens the customer experience.
Best practices:
- Hybrid model: combining AI and live operators provides the optimal balance.
- Regular feedback: continuous analysis of customer dialogues and reactions allows for service improvement.
- Open communication: informing customers about the use of AI strengthens trust.
The future of AI in customer service
The prospects for further development of AI in customer service look very ambitious.
Multimodal interfaces
Systems with support for different communication formats will become more popular. Depending on personal preferences, clients can communicate by text, voice, or video format.
Enhanced personalisation
The main disadvantage of neural networks is the lack of personalization that a person can provide. A new generation of neural networks will be able to read the mood, tone and meaning of messages, which will make the answers more accurate and suitable for each user, rather than boilerplate ones.
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Ethics and transparency
Customers’ distrust of neural networks will gradually decrease. This is influenced by the large personalization of responses, as well as transparent AI mechanisms, including data encryption, which ensures the protection of users’ personal information.
Legislative challenges
Companies will need to pay more attention to security. There are standards, such as GDPR, that are aimed at protecting users. Businesses need to comply with them when implementing AI in customer service.
Seamless service
In the coming years, the intensive implementation of neural networks in customer service will continue. By 2030, it is expected to use automated services that ensure a smooth transition between channels and devices.
Challenges of AI in Customer Service
Artificial intelligence makes customer service easier, but it’s important for companies to be aware of the complexities and challenges they may face. The main disadvantages of artificial intelligence are customer support:
- Understanding complex queries. The system often has problems handling complex, atypical, or very detailed requests. In this case, the AI may give an incorrect or incomplete response.
- Customer trust. The first difficulty leads to the second one. Many customers do not trust artificial intelligence systems because they are sure that they will get the wrong answer.
- Proper AI training. In order for AI to respond effectively to customer requests, it needs to be trained on large, accurate, and diverse data. The AI learning process requires constant updating to adapt to new problems and models.
- Limitations of personalization. Artificial intelligence-based systems work faster and efficiently solve typical queries, but they are inferior to humans in personalizing responses. Humans can adjust their tone, empathy, and approach depending on each client's needs, while AI usually relies on pre-programmed patterns and scenarios.
- The cost of implementation. Using AI increases efficiency and reduces costs, but the implementation phase can be expensive.
Understanding the underlying issues and how they can be solved increases the overall level of customer engagement.
Conclusion
Artificial intelligence cannot yet completely replace humans in customer service. User support services help simplify work, minimize costs, and respond quickly to typical requests. The introduction of AI allows us to achieve an optimal balance between automation and personalization of responses, while maintaining a high level of competence and efficiency.
Various customer service formats have been created based on AI. This allows businesses to choose the model that meets their needs, expectations, and financial capabilities. Using modern trends in the development of customer service makes it possible to build a competitive and customer-oriented company.
Frequently asked questions
What is AI in customer service?
This is a set of technical solutions aimed at improving communication with customers. AI in the client service is used in the form of chatbots, voice assistants, data analysis programs and in other forms. All this increases the automation of support work and the quality of service.
What tasks does AI solve in customer service?
The main tasks are to provide round–the-clock responses, predict problems, reduce the burden on operators, and improve the overall efficiency of the support service.
How secure is data when using AI?
There will be no security issues when choosing a reliable service provider. Modern programs are licensed and comply with security standards, such as GDPR.
Can AI be integrated with our CRM?
Most platforms support integration with CRM via API, which allows you to synchronise data and manage it centrally.
How long does it take to implement a chatbot?
Simple versions of chatbots can be launched in a couple of weeks, while complex custom solutions require up to three months of preparation and training.
How can we measure the effectiveness of AI in our service?
The main indicators are FRT, CSAT, the proportion of automated requests, and savings on staff costs. A comprehensive analysis of these metrics allows us to assess the real return on investment in AI.
