This master thesis explores how artificial intelligence can help businesses in the rapidly evolving fast moving consumer goods industry, which is characterized by high product turnover, low margins and dynamic consumer behavior, to integrate consumer knowledge into every step of the marketing process. Recent advances in artificial intelligence offer new ways to automate and enhance CKM processes. However, there remains a lack of in-depth academic research that systematically explores how AI tools can be strategically implemented in CKM within the specific context of FMCG marketing. The motivation for this thesis is the growing necessity among marketing practitioners to enhance their awareness of AI's practical capabilities in managing customer knowledge more efficiently, while also navigating organizational, legal, technological and cultural challenges. Previous studies discuss the potential of AI in marketing, but they offer limited guidance on tool and industry-specific guidance. To address this gap, the study formulated three research questions: 1. How can artificial intelligence facilitate the creation, organization and application of customer knowledge across different stages of the marketing process in the FMCG Industry? 2. How can firms faster and more effectively generate Customer Knowledge Management with the help of AI? 3. Which AI-based methods and tools are the most valuable and suitable for firms? The thesis is structured in six chapters. After the introduction and theoretical foundation in Chapters 1 and 2, Chapter 3 presents a structured evaluation of the selected AI tools across five key phases of the marketing process. Chapter 4 outlines the research methodology, including the interview design and the qualitative content analysis description. Chapter 5 presents the empirical findings of the qualitative research, which is structured along four thematic blocks. Finally, Chapter 6 concludes the thesis with a summary of the key findings, answering the research questions, highlighting limitations and outlining future research directions. The thesis adopts a qualitative research approach, which is demonstrated by the employment of semi-structured expert interviews with a total of five marketing professionals from FMCG companies of varying size and AI complexity. Thematic coding was applied in accordance with Mayring's (2015) qualitative content analysis, with support from the software MAXQDA. In parallel, a structured evaluation of AI tools across the marketing process was conducted, comparing real-world tools such as Altair, Synerise, SAS Viya, Peak.ai, Dynamic Yield, Kameleoon, Jasper.ai, Neuroflash, Tellius and Funnel.io based on functional, ethical and technical criteria. The findings indicate that AI has already become an essential component of CKM in several key areas, particularly in content creation, segmentation, personalization and predictive analytics. It was reported by firms that significant time savings, cost reductions, customer satisfaction improvements and efficiency gains through the AI implementation were achieved. Furthermore, behavioral and transactional data were identified as having a greater importance than demographic data, emphasizing the transition towards a data-driven personalization approach. The study also shows that AI implementation is not just a technical task but strongly depends on organizational culture, onboarding practices, GDPR compliance and user trust. However, it is important to note that several limitations were also identified. The relatively small sample size of five interviewees (N=5) limits the extent to which the results can be generalized. Participants were pre-selected based on the existing AI use, with the exclusion of firms that have not yet adopted such technologies. Furthermore, the findings are exclusively focused on the FMCG industry and qualitative methods, which limits the broader applicability of the results. Future studies could adopt quantitative methods to assess the adoption of AI across different sectors and company sizes. Additionally, more AI use cases, such as sentiment analysis, or commonly used tools like ChatGPT, Midjourney and Microsoft Copilot, should be examined in greater detail to better capture the evolving tool landscape. As AI continues to develop at such a rapid pace, it is becoming increasingly crucial for companies to adopt a flexible approach for implementation to maintain competitiveness in the digital marketing era.