When integrating artificial intelligence into customer service, it is important to select the economically sensible adjustments. This applies in particular to the initial contact with customers, the analysis of customer data and the processing of highly repetitive inquiries. However, only by combining human expertise and the targeted use of suitable technologies can efficiency be increased without losing customer loyalty.
Examples of AI in customer service
The value of AI in customer service is demonstrated by practical application scenarios. Here are five of them:
AI-supported upselling recommendations in the support chat
Process:
Based on a support conversation, a machine learning model analyzes user behaviour (e.g. previous purchases, usage behaviour, customer value) and suggests suitable additional offers to the customer service employee. These can be specifically included in the chat - e.g. "We recommend..." to suit your device.
Effect:
Increased conversion rates in customer service, personalization without additional research effort, data-based upselling strategies.
Automated 24/7 first contact via chatbot with escalation logic
Process:
Incoming emails and chat histories are not only categorized by topic using NLP and sentiment analysis, but also checked for emotional tone. Messages with a particularly negative tone (e.g. angry complaints) are given a higher priority in the ticketing system. At the same time, they are automatically flagged in order to prepare service employees for the emotional context.
Effect:
Early de-escalation of potential conflicts, increased customer satisfaction through proactive handling of critical concerns.
Automated document processing for complaints
Process:
Customers send complaints including supporting documents (e.g. photos or PDFs of damaged products) by email. An AI system with image processing and NLP analyzes the attachments, extracts relevant data (product code, damage, date of purchase) and automatically initiates a return process in the ERP system. A manual check is only carried out in the event of ambiguities.
Effect:
Massive time savings in processing, consistent quality, lower error rate, accelerated reimbursement.
Technologies used and basics
AI is not just AI and a wide range of technologies from this broad field are also used in customer service - whether in the call center or field service. At their core, they are characterized by the analysis of data and the decisions derived from it. In other words, it is mostly machine learning, the most important area of artificial intelligence. The specific technologies include
- Natural Language Processing - Natural language processing forms the basis for automatically analyzing and generating content. Chatbots on the basis of large language models or generative AI are a particularly common application of this technology and in many places already take over part of the communication with customers.
- Text-to-Speech / Speech-to-Text - Large voice models also expand the possibilities for voice input or the generation of artificial voices. This enables customer service companies to set up voicebots, interactive telephone systems or barrier-free support.
- Sentiment analysis - Here, too, we are in the field of NLP. It is a technique for decoding emotions or tonalities in content. This helps with categorizing customer inquiries and ensuring customer satisfaction along the customer journey.
- Predictive data analysis - Long-term customer care generates vast amounts of data that can be used to train ML models. These can then predict future trends and enable customer service workloads to be adjusted.
- AI agents - These complex systems combine various specialized AI models to perform comprehensive tasks autonomously or to support people in doing so. AI agents are therefore more than just tools and can interact with various CRM or ticketing systems in customer service.
Overview of suitable AI applications
Customer service usually uses AI tools that combine different technologies and are therefore suitable for various application scenarios. The following overview shows some examples:
| Application / Platform | Technology(ies) used | Benefits | Disadvantages |
| ChatGPT (OpenAI)(e.g. for chatbots) | Generative AI, NLP | Automated, context-related communication, scalable, personalizable | Can hallucinate / generate false content, data protection & compliance risks |
| Zendesk Answer Bot | NLP, ML, FAQ automation | Fast response to standard requests, integration into CRM | Limited complexity, requires a good database |
| Salesforce Einstein | Predictive data analysis, NLP, ML | Automation of workflows, sentiment analysis, upselling forecasts | Setup and training are complex, expensive to scale |
| Tidio, Intercom | Chatbot builder with generative AI | Quick start, good usability, multichannel support | Limited customization, often limited for enterprise requirements |
| IBM watsonx Assistant | NLP, AI agents | Multi-use case-capable (voice & text), robust platform for large companies | Technically complex, longer implementation time |
| DeepL API | Neural Machine Translation | High-quality translations, helpful for international support | No context recognition outside of individual sentences |
| Observe.AI | Voice AI, sentiment analysis | Coaching of agents, analysis of customer emotions, call quality | Focus on call center - no fully automated processing |
| UiPath + AI modules | RPA + ML | Process automation of back office tasks (e.g. invoice reconciliation, returns) | No direct customer contact, only useful for standardized processes |
| Kore.ai, Cognigy | AI agents, multimodal AI | Complex dialog systems with backend integration | Technically demanding, high initial outlay |
| Konfuzio Chat | NLP, ML, Low-Code | Simple integration and customization, high data security | No direct ordering of articles possible |
Why use AI in customer service?
Many business areas are fundamentally suitable for using AI technologies to automate and accelerate processes, increase accuracy or make data-based decisions. Generative AI in particular offers extensive opportunities for these improvements, especially in text-based environments. Customer service, which is very much characterized by language, stands out in this respect. The concrete benefits include
- High availability: Automated processing, analysis and handling of customer inquiries is possible around the clock. Corresponding processes in customer service are therefore no longer tied to employees' working hours.
- Error reduction: Thanks to their extensive training based on huge amounts of text, generative AI models are resistant to linguistic errors. Typos and other inaccuracies in customer service are avoided.
- Personalization: AI-based analysis of customer data enables tailored recommendations to be made and individual offers to be submitted. This can be based, for example, on the complete customer journey to date or the order history.
- Improved decision making: AI-based analyses of data from customer feedback and purchasing behavior provide a sound basis for strategy adjustments. In addition, potential for upselling, for example, can be identified in a targeted manner.
- Efficiency improvement: The killer argument for automation par excellence. In customer service, service employees are relieved of repetitive queries (e.g. "Where is my parcel?", "How can I reset my password?") and can concentrate more on strategic or personal concerns.
Efficient customer service with high customer loyalty has a direct impact on sales. The return on investment (ROI) is therefore particularly high in this area and the payback period is short.
Conclusion
Customer service is an extremely interesting business area for the use of AI, but should be evaluated precisely and individually. In particular, a large volume of similar inquiries in natural language offers considerable savings potential. Chatbots, sentiment analysis and data analysis can significantly reduce the workload for employees and also reveal future trends. By closely examining the status quo and comparing it with business objectives, companies can draw up a precise plan for selecting and implementing suitable tools and technologies. Last but not least, it also depends on the AI competence of service employees, which is now mandatory in principle, but remains highly variable.
Would you like to automate parts of your customer service with the help of artificial intelligence? You are welcome to Contact to us for expert advice.
EN 
DE 
ES 
FR