Corporate resilience through generic AI-based workflows in strategic product planning Özcan, Deniz Gräßler, Iris Graessler, Iris In increasingly volatile and uncertain markets, corporate resilience has become a critical capability in strategic product planning. Companies face significant challenges in systematically monitoring and interpreting heterogeneous environmental data originating from diverse sources, formats, and temporal contexts. While predefined workflows and decision trees can support strategic analysis, they often lack the flexibility required to cope with dynamic market conditions and foresightrelated data from extreme dispersed and heterogeneous sources. This paper proposes a method to enhance corporate resilience through the application of generic, reusable AI-based workflows in strategic product planning. The approach integrates Data Science and Artificial Intelligence methods into modular, visually modelled workflows that enable hybrid human-AI decision-making. Based on a systematic literature review and an analysis of industrial challenges, key success factors and resilience criteria are identified. These insights are used to develop a method that supports internal and external analyses, scenario-based strategy development, and adaptive implementation monitoring within a generic workflow structure. The method leverages techniques such as machine learning and generative AI to process structured and unstructured data, identify patterns, and support real-time strategic assessments. Validation with decision-makers from medium-sized companies demonstrates improved transparency, repeatability, and cross-functional collaboration compared to predefined workflows. Universitätsbibliothek 2026 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/66709 Özcan D, Gräßler I. Corporate resilience through generic AI-based workflows in strategic product planning. In: Graessler I, ed. <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i>. Universitätsbibliothek; 2026. doi:<a href="https://doi.org/10.17619/UNIPB/1-2636">10.17619/UNIPB/1-2636</a> eng info:eu-repo/semantics/altIdentifier/doi/10.17619/UNIPB/1-2636 info:eu-repo/semantics/closedAccess