In this paper, we introduce a new framework for the intent-based configuration of Industry 4.0 (I4.0) components that integrates high-level business objectives with the operational deployment of industrial technologies. Recognizing the limitations of current top-down approaches, our model combines these with a bottom-up analysis that considers the real-time technical constraints and execution conditions of I4.0 components. We propose a taxonomy of intents that includes business, operational, and infrastructure considerations, represented using Extended Backus–Naur Form (EBNF) grammar and mapped through knowledge graphs to enhance the precision of component configuration. The framework employs machine learning algorithms to assess component capabilities and an intent translator to convert these assessments into actionable configuration policies. This dual approach ensures dynamic and precise adaptation of industrial infrastructures to changing strategic goals and operational demands. By leveraging the Reference Architectural Model Industrie 4.0 (RAMI 4.0) standards, our model aims to enhance reliability and foster continuous optimization of I4.0 components. This work represents a significant step towards realizing the potential of intent-based systems in Industry 4.0 environments, offering a robust method for aligning technology with organizational objectives effectively.