Artificial intelligence (AI) aims to automate rational behavior, which is rooted in decision-making. A central problem in AI development is the lack of a methodology guaranteeing that an AI system will achieve its operational goals, leading to outcomes that fail to meet the Decision Maker’s (DM) expectations. Addressing this issue requires solving two interrelated tasks. Task 1 (System Development): A synthetic approach, based on the law governing a system’s construction and functioning, is necessary. The fundamental principle proposed here is the Law of Conservation of Object Integrity (LCOI). This law ensures the guaranteed achievement of goals by establishing an invariant correlation between a system’s intrinsic properties and its purposeful behavior under constraints. LCOI serves as the mechanism for synthesizing the system’s architecture. Task 2 (Model Formulation): Since decision-making is model-dependent, advanced capabilities for model synthesis are required. Optimal AI performance hinges on both a properly architected system and mathematically rigorous models. Our methodology is grounded in the LCOI. The AI system’s functioning is modeled as the integration of four processes: goal-setting, problem (threat) manifestation, its identification, and its neutralization. The probability of detecting and neutralizing each threat serves as the key performance metric. Simulation results confirmed the principal trends of this approach, demonstrating its viability for creating effective AI systems.