Rethinking Knowledge Work: Designing LLM-based Systems for Complexity Management

Knowledge workers increasingly face complexity in managing heterogeneous documents, strict compliance requirements, and cross-functional processes, which disproportionately burden small and medium-sized enterprises (SMEs). Particularly in public procurement, tender analysis involves processing heterogeneous documents, complying with requirements, and coordinating across functions to inform bid decisions. Using design science research with an SME, we investigate how to manage complexity in document-intensive knowledge work—exemplified by public tender analysis—across document, compliance, and process dimensions, deriving four design requirements and two design principles. We instantiate these in a Large Language Model (LLM)-based artifact that supports document analysis. Evaluated on real tenders, the artifact reduces initial screening time while maintaining high retrieval accuracy. Our findings demonstrate that knowledge work can be advanced by LLMs, reducing complexity and transforming the nature of human work—shifting the focus from reading and extracting information to orchestrating and verifying LLM-generated outputs.