Klarna recently made headlines by announcing that an AI-powered customer service assistant is already handling two-thirds of their inquiries. Such impressive figures raise the question of how much potential generative AI truly holds for improving customer service. From accelerating processes to easing the workload on human employees, AI opens up countless opportunities to meet the demands of modern customers. In this article, we explore how companies are leveraging this technology to provide efficient, personalized, and around-the-clock service—and what’s behind these success stories.
Generative AI can generate human-like text in real time, analyze complex customer inquiries, and provide context-specific answers. Its decisive strength lies in its ability to identify relevant patterns from vast amounts of data and draw accurate conclusions. An example of this is the capability of tools like Genesys Copilot, which automatically creates conversation notes and simplifies post-call processing, enabling significant time savings.
A report by BCG shows that generative AI not only optimizes existing processes but can also create new business opportunities. Routine tasks can be automated, allowing employees to focus on strategically important areas. A prime example is the real-time analysis of customer interactions using Amazon Connect, which provides managers with real-time insights into customer satisfaction.
Generative AI offers a range of benefits that help companies increase efficiency and productivity:
Time savings: Automating repetitive tasks reduces employee workload, enabling them to focus on strategically important activities.
Consistency: Generative AI ensures uniform responses and minimizes human error, enhancing the overall quality of customer interactions.
Scalability: AI-driven systems can easily manage high inquiry volumes, which is especially beneficial during peak seasons (e.g., the holiday period).
Flexibility: Generative AI can respond to changes in real time and provide dynamic solutions tailored to specific customer requirements.
Cost reduction: Automation lowers personnel costs and makes handling inquiries more efficient.
These advantages illustrate how generative AI supports companies not only operationally but also in securing long-term competitive advantages.
There are many ways to apply generative AI in customer service. Below are several key use cases:
Automation of customer interactions: Chatbots and virtual assistants respond to frequently asked questions, enabling rapid problem resolution.
Real-time support for employees: Systems like NICE Enlighten Actions provide context-specific information during customer conversations to facilitate decision-making.
Translations and overcoming language barriers: Platforms such as Google Contact Center AI offer real-time translations for seamless international customer support.
Analysis of customer sentiments: Tools like Talkdesk identify emotions and sentiments, allowing for communication adjustments accordingly.
Creation of training materials: Generative AI generates personalized training content for employees, tailored to specific customer service challenges.
Prediction of customer needs: Predictive analytics enable companies to anticipate future customer requirements and proactively provide solutions.
These practical examples underscore how generative AI can optimize both operational and strategic tasks in customer service.
Implementing generative AI in customer service is a process that requires clear steps:
Conduct a needs analysis: According to Deloitte, companies should first identify areas where generative AI will provide the greatest benefit. At triebwerk.ai, we use an assessment questionnaire to capture the current status—with great success. This allows us to perfectly tailor our training programs to our target audience and offer our customers maximum value.
Determine use case: A clearly defined application is key to the successful introduction of generative AI. The goal is to identify processes that can be optimized with AI. You can read about how to identify the right use case here.
Ensure data quality: A robust data foundation is essential, as the accuracy of the AI depends directly on the underlying data. Tools like Google Cloud’s Generative FAQ help optimize data quality.
Start pilot projects: Smaller pilot projects can help gather initial experiences and validate use cases. One example is the introduction of agent assist tools that demonstrate early productivity gains.
Empower employees through training: Comprehensive training adapted to individual needs—such as triebwerk.ai’s AI Promoter Enablement Program—gives teams the skills to use AI solutions effectively and develop their own projects. The program focuses on practical learning, tailored to the specific requirements of each company.
Continuous optimization: Ongoing feedback loops and updates are critical to keeping AI solutions current.
triebwerk.ai actively supports companies in introducing generative AI in customer service. Through targeted training and practical solutions, this training program ensures that companies can use this technology effectively and benefit from it in the long term. This approach enables businesses to empower their teams to independently drive innovative AI initiatives, thus strengthening their competitive edge.
