Organizations are getting better at learning. They are not getting better at making meaning from what they've learned. As the volume and velocity of information accelerate — driven by forces reshaping how organizations detect, generate, and act on signals — the gap between what organizations know and what they can collectively make sense of is widening. This paper argues that the binding constraint in organizational performance has shifted — from the ability to acquire knowledge to the ability to metabolize it: to build shared meaning, integrate competing interpretations, and translate insight into decisions that actually hold. In a world of accelerating information, making meaning has become the central and increasingly critical role of leadership. The leaders who navigate this era well will be distinguished not by how fast they decide, but by how well they build meaning before they do. Drawing on organizational sensemaking theory and the Acceleration Without Metabolization framework, it introduces organizational metabolization capacity as the construct that names what is missing. When that capacity is degraded, decisions don't resolve uncertainty. They redistribute it — migrating through the system, reconstituting around successive leaders, and surfacing as the dysfunction that no leadership change seems to fix. The paper identifies three misdiagnoses leaders consistently make, and three practices that address the actual condition. Keywords: Organizational learning; sensemaking; metabolization capacity; absorptive capacity; making meaning; post-heroic leadership; premature closure; closure reflex; uncertainty redistribution; meaning-making leadership; acceleration without metabolization; bounded rationality; high reliability organizations; AI-enabled decision-making
Building similarity graph...
Analyzing shared references across papers
Loading...
David S Morgan (Thu,) studied this question.
www.synapsesocial.com/papers/69ddda22e195c95cdefd7a0e — DOI: https://doi.org/10.5281/zenodo.19543979
David S Morgan
Building similarity graph...
Analyzing shared references across papers
Loading...