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Expertise Erosion, Skill Dependency, and Tacit Knowledge Loss Inside Automated Workflows
Citation. Silva, M., Pereira, J., Costa, B., & Alves, R. (2026). Expertise Erosion, Skill Dependency, and Tacit Knowledge Loss Inside Automated Workflows. Journal of Applied Organizational Systems and Behavior, 6(1), 47–56. https://doi.org/10.51847/t06fub63bi
Summary. This critical review distinguishes automation-supported performance from retained human expertise in automated workflows. It synthesizes pathways through which reduced task participation, limited error exposure, and less visible situated reasoning may weaken unaided capability or tacit knowledge while emphasizing that automation can also redistribute expertise and support resilience. Its proposed Expertise-Resilience Framework is an integrative conceptual account rather than a validated causal model.
Key claims
- Skill dependency is a work arrangement in which competent performance increasingly presupposes automated assistance; it should not be inferred from technology use or adoption alone.
- Deskilling requires evidence of weakening unaided capability or reduced maintenance opportunities, such as diminished rehearsal, error diagnosis, and exposure to meaningful task variation.
- Assisted productivity gains and retained capability can diverge, so automation should be evaluated through its learning architecture and failure-recovery demands rather than task removal alone.
- Automated workflows may attenuate, redistribute, or preserve tacit knowledge depending on whether situated interpretation, knowledge brokerage, feedback, and consequential human judgment remain accessible.
- The review identifies task exposure, epistemic visibility, work design, career stage, practice and feedback, human–AI calibration, and knowledge exchange as conditions shaping expertise trajectories.
Evidence & limitations
- Critical review drawing on 36 included sources after structured identification and screening; it synthesizes mechanisms and boundary conditions rather than estimating effects or testing a causal model.
- The framework explicitly treats expertise resilience as conditional and unvalidated; claims about deskilling require longitudinal or otherwise defensible evidence of retained human capability.
- The paper is a published journal review; its central contribution is conceptual integration, so individual empirical findings it discusses should be consulted directly for design-specific inferences.