AI Workflow for Scarce Data: A Modular Approach to Optimise Processes

Many small and medium-sized enterprises lack large datasets and AI expertise, limiting their ability to apply traditional AI methods. However, they often possess valuable yet underutilised experimental data. This paper introduces an interpretable AI workflow tailored for such scarce data environments. It guides users through experimental design, data labelling, Decision Trees, and Active Learning to optimise processes efficiently. A bread roll baking use case illustrates the workflow’s practical value and transferability to other industrial settings.