Organizations increasingly rely on algorithmic systems because prediction appears to offer consistency, speed, scalability, and protection from human error. Yet predictive performance can acquire a broader organizational meaning: uncertain outputs may be enacted as settled answers, managerial authority may become attached to model recommendations, and disagreement may be reframed as resistance to objective evidence. This critical review examines how such algorithmic certainty may narrow inquiry, weaken the consequential influence of employee dissent, displace experimentation, and obstruct learning from error. The review integrates organizational, information-systems, work-design, voice, experimentation, and human–AI collaboration research. It evaluates convergent mechanisms, conflicting findings, alternative explanations, and level-of-analysis limitations rather than treating algorithms as uniformly controlling or employees as passive. The synthesis suggests that prediction becomes hazardous to learning when confidence cues, opaque evaluation, concentrated decision rights, and metric dependence reduce the legitimacy of questions, challenges, and locally generated alternatives. However, professional expertise, psychological safety, job autonomy, transparent uncertainty, and contestable decision processes may preserve inquiry and corrective action. The article’s original contribution is a Certainty–Learning Framework that connects predictive outputs to organizational enactment, dissent, experimentation, error interpretation, and updating. The framework is a critical integrative synthesis, not a validated causal model. Longitudinal, comparative, multilevel, and intervention research is required to distinguish algorithm effects from pre-existing hierarchy, culture, task structure, and implementation choices. The implications remain context dependent.
