Artificial Intelligence and Environmental Decision-Making: Does AI Adoption Improve Corporate Resource Efficiency?

Companies increasingly use machine learning and other artificial intelligence (AI) tools to forecast demand, tune processes, choose suppliers, route goods, and monitor environmental performance, and these tools are often described as a route to lower use of energy, water, and materials. AI can improve the decisions that determine resource
use, but it also consumes resources itself, it can increase the output that a firm produces, and its recommendations improve resource efficiency only if people and systems act on them. This study builds an evidence based framework for asking whether AI adoption improves corporate resource efficiency. It synthesizes peer reviewed studies published before 1 January 2024 on the environmental footprint of AI, the sustainability potential of AI, algorithm use in decision-making, firm level productivity effects of data driven decisions and AI, and rebound effects. The evidence does not support a universal efficiency gain: direct firm level evidence linking AI adoption to resource use
is very thin, the footprint of large models is substantial and rarely netted against the savings they enable, and the productivity literature points to higher output as well as higher efficiency. The paper therefore proposes a resource ledger in which the net resource saving equals the efficiency gain from better decisions minus an output scale effect and minus the operating footprint of the AI system itself. It derives a break even condition for output growth, a footprint budget, and an offset ratio, models the efficiency gain as the product of decision error reduction and the rate at which recommendations are acted on, and develops research propositions, an empirical design, and an AI
Environmental Decision Maturity Index (AEDMI). The central implication is that AI improves corporate resource efficiency only when the savings from better decisions exceed the resources used to produce those decisions and the additional production they enable.