This paper explores how artificial intelligence can extend the methodological boundaries of materials science by enabling coherent, information-driven discovery processes. Rather than treating AI as a tool for optimization alone, the work introduces a framework in which human expertise and machine intelligence operate as a coupled cognitive system. The focus lies on hybrid material systems and functional coatings, where complex interactions between chemistry, structure, and performance challenge classical experimental approaches. AI-assisted pattern recognition, hypothesis generation, and test-matrix design are discussed as mechanisms to reduce experimental entropy while preserving scientific control. A central contribution of this paper is the concept of coherent AI usage: AI is not positioned as an autonomous decision maker, but as a reflective system that amplifies human reasoning, domain intuition, and experimental intent. The paper outlines methodological principles, boundary conditions, and practical implications for applying AI in materials research without compromising reproducibility, interpretability, or intellectual property integrity. By bridging material science, information theory, and human-AI co-creativity, this work proposes a scalable research paradigm for future material development under increasing complexity constraints.