R&D activities in the cosmetics industry are characterized by large volumes of data, strict regulatory requirements, and complex processes conducted across several systems and platforms. In the current landscape, information is fragmented across SAP, SharePoint, local documents, and informal sources. This process hinders access to historical data, increases the volume of manual labor, and causes decisions to be made based on individual expertise rather than consolidated data.

This practice-oriented case study project was conducted based on an analysis of R&D processes, internal documentation, and a literature review. The methodology included identifying critical points in current workflows and evaluating AI opportunities based on impact and feasibility, as well as developing an AI conceptual framework. The result is a strategic model, not a technical implementation. In addition, this work serves as a strategic starting paper for the client to design and align an AI‑supported R&D structure.

The project offers a structured basis for artificial intelligence adoption in R&D, tailored specifically for the cosmetic industry and conformity requirements. Based on the analysis of the existent R&D processes, relevant AI use cases were identified, prioritized, and systemically evaluated in accordance with the potential impact on operational efficiency and implementation feasibility. The results highlight three main fields with high importance: intelligent knowledge management, automation of documentation flows, and the use of predictive models for testing. Additionally, the project includes recommendations for the management regarding AI adoption and proposes a phased implementation roadmap, which allows for a gradual introduction of the solutions depending on organizational maturity and associated risks. As a result, the client benefits from reduced administrative activity time, faster and easier access to technical knowledge, increased traceability and decision consistency, and shorter development cycles.