Abstract
Decisions taken throughout a bioprocess lifecycle are often guided by heuristic knowledge that is difficult to summarize and sort, scattered across heterogeneous tools and documents, and partly retained as tacit expert mental models alongside fragmented computational models. This fragmentation remains a central barrier to reproducibility, transparent provenance, and systematic reuse of prior learning across comparable development projects. In this paper, it is argued that a key missing link toward Bioprocessing 5.0 is the digitalization of FAIR knowledge through a Cognitive Digital Thread that couples semantic knowledge graphs with AI methods to connect experimental data, protocols, workflows, and decision rationale with mathematical models and digital twins in a machine-actionable and auditable manner. A digitalization roadmap is outlined as a sequence of capability stages-from local device and data integration, to reproducible workflow execution and metadata capture, to semantic knowledge digitalization and explainable reasoning, to secure cross-institutional exchange of FAIR data and knowledge (optionally anchored via permissioned blockchain and smart contracts for authorship, versioning, and IP provenance), and finally to human-in-the-loop collaborative environments for model-informed bioprocess design and control. The result is a layered conceptual architecture that clarifies required functions and dependencies and provides a practical framework for implementing cognitive decision support across the bioprocess lifecycle.