Decision-makers in transformation processes face complexities introduced by evolving regulatory landscapes and market dynamics. This study delves into the use of Agent-Based Models (ABMs), Multi-Agent Systems (MAS), and Digital Twins (DTs) to support strategic planning through the simulation of intricate interactions and dynamics. Our research specifically focuses on the application of MAS and DTs to the steel industry’s shift from coal-based to hydrogen-based processes, with a strong emphasis on sustainability. By analyzing literature, case studies, and theoretical frameworks, we provide insights for modelers on leveraging these technologies for industrial transformation. We present a conceptual framework designed to address complex decision-making challenges and propose a corresponding architecture. We identify potential benefits of ABMs, MAS, DT in steel industry transformation and illustrate how these tools can become particularly valuable for understanding market dynamics, enhancing stakeholder engagement, and incorporating non-monetary factors into decision-making.