Even if large language models grasp deep concepts like symmetry or linearity, their internal representations remain messy and entangled — opaque to human understanding. This lack of structure may limit their ability to generalise far beyond training data. Pairing them with well-organised, ontological frameworks offers a path toward clarity, verifiability, and deeper reasoning.

Organisations behave like dissipative systems — optimising locally while exporting entropy to the whole. To become AI-native, we must redraw our boundaries, dissolving silos and integrating intelligence at the organisational level. Knowledge Graphs provide the connective tissue for this transformation, turning chaos into coherence.