Artificial intelligence is commonly evaluated through accuracy, speed, economic value, safety and computational efficiency. These measures are incomplete when an AI-enabled intervention affects interconnected human and ecological systems. This paper proposes the Regenerative Intelligence Index (RII), a transparent framework for measuring whether an AI-enabled intervention produces verified net restoration or contributes to ecological and human depletion. The framework evaluates eight domains: climate and energy, water, soil and land, biodiversity, circular material use, human capability, distributive benefit and governance integrity. RII distinguishes the environmental footprint of developing and operating an AI system from its immediate application effects and wider system-level consequences. Indicators are converted into bounded change scores, adjusted for evidence quality and uncertainty and aggregated only after non-compensable safeguards have been assessed. These safeguards prevent serious human-rights violations, ecological damage or governance failures from being concealed by positive results elsewhere. A hypothetical agricultural decision-support example demonstrates the proposed calculation, sensitivity analysis and reporting structure. The paper also establishes a measurement protocol, validation program, classification system and anti-greenwashing requirements. This publication presents a conceptual framework and prospective research methodology. It does not report completed experimental results or claim that the proposed index has already been empirically validated. The next stage is independent, multi-site testing comparing AI-enabled interventions with credible non-AI alternatives and existing practices.