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Materials science increasingly relies on artificial intelligence (AI) pipelines that integrate multiple models of varying fidelities, architectures, and objectives to accelerate discovery and design. These multi-model workflows—encompassing low-fidelity approximations, high-fidelity simulations, machine learning surrogates, and experimental feedback—promise efficiency but introduce a fundamental coordination problem: reconciling disparate predictions, managing conflicts, ensuring interoperability, and mitigating emergent behaviors or bottlenecks. This conceptual manuscript examines the coordination challenges in such pipelines, drawing on recent advances in multi-fidelity learning, active learning, hybrid modeling, and workflow orchestration. It analyzes how integration conflicts arise from differences in scale, accuracy, and data provenance, potentially leading to consensus failures, validation cascades, and optimization bottlenecks. The discussion highlights conceptual strategies for robust coordination, including uncertainty-aware fusion, adaptive sampling, and iterative refinement, while underscoring the need for principled frameworks to harness the full potential of multi-model systems in materials AI.