"In addition, our experience from customer projects shows that companies that rely on AI assistants and in-deep automation not only gain competitive advantages, but also sustainably strengthen their innovative power," says Theresa Tran, Business Manager AI & Data Analytics at Lufthansa Industry Solutions (LHIND).
Data quality determines the success of AI
The quality of the underlying data plays a critical role in the success of AI chatbots. Companies need to ensure that the data for the chatbot is of high quality and legally sound. This includes unstructured data from documents, databases and other digital sources.
With the new Retrieval Augmented Generation (RAG) technology, AI chatbots can access specific data and provide accurate, fact-based answers. This allows organizations to use their own data as the primary source for the chatbot. This method prevents the AI from providing incorrect information. With RAG, companies can tailor their chatbots to meet specific needs. They can then provide not only standardized answers, but also specific, contextual solutions in accounting, logistics, or customer service.
Compliance: AI systems and EU regulation
The LHIND white paper also looks at the legal dimension of AI. Its use is subject to strict regulations, especially since the introduction of the EU AI Act. This law divides AI systems into different risk classes and specifies the conditions under which they may be used. Companies must pay attention to these risk classes when using AI in customer transactions and, under certain circumstances, restrict certain functions.
