Abstract: Continuous processing, inaccessible internal states, pronounced lag between actions and quality outcomes, and rare-yet-costly failure samples distinguish the manufacturing of metallic materials from discrete manufacturing and dictate that industrial AI for steel rolling cannot remain at the level of prediction and generation, but must advance toward a goal-driven closed loop of perception, inference, decision-making, execution, and feedback. Taking goal-drivenness as the central thread, this paper analyzes the capability leap of artificial intelligence from generative AI to agentic AI and Physical AI, and proposes explicitly introducing “goal-driven” into the “data-intensive-intelligence-emergent-human-machine collaborative” paradigm to form a new paradigm for industrial control. A Vision-Memory-Controller (VMC) functional loop is used to characterize the core capabilities of the world model, clarifying its relationship to and distinction from digital twins, embodied intelligence, and Physical AI; three technical routes—full-stack-supported, spatial-simulation-based, and abstract-prediction-based—are summarized together with their bottlenecks. On this basis, a rolling industrial AI system architecture is constructed, in which the cloud-based world model provides global “Knowing,” domain-specific agents together with deterministic control provide field-level “Acting,” and full-stack vertical integration provides the engineering foundation. Given the marked differences among microstructure/properties, geometric dimensions, and shape quality in measurability, mechanism maturity, and execution complexity, three differentiated modeling paths are proposed: “data first—knowledge empowered,” “mechanism led—data compensated,” and “problem driven—virtual-physical integrated.” Representative engineering cases show that the relevant technologies have formed verifiable local closed loops within individual domains and are accelerating toward cross-process, multi-objective coordinated full-process autonomous decision-making, providing a theoretical foundation and engineering pathway for the intelligent transformation of metal industries such as iron and steel, as well as other large-scale complex process industries.