This article proposes a Zero-to-One methodological framework for the development of scalable artificial intelligence (AI) products, derived from the author’s leadership of enterprise-scale AI platforms. The framework is formulated as an engineering-oriented system model that links data pipelines, automation layers, and operational control loops across successive stages of AI product maturation. Unlike traditional AI development models that assume data completeness and architectural stability, the proposed Zero-to-One framework enables controlled evolution under conditions of partial data, streaming inputs, and operational uncertainty. The study demonstrates that AI product viability depends on the coordinated advancement of data quality, unified information layers, infrastructure readiness for real-time processing, and a culture of continuous piloting. The framework contributes an engineering-oriented methodological model that supports system-level reasoning about scalability, resilience, and operational control in AI-driven platforms.

artificial intelligence, product development, data flows, automation, technical product management, Zero-to-One methodology

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