Although Artificial Intelligence (AI) has been advanced as a lever for corporate decarbonization, evidence on whether and how AI-enabled carbon reduction strategies (AICRS) translate into superior firm performance remains thin and theoretically under-specified. Integrating the natural-resource-based view with dynamic capabilities theory, we theorize AICRS as a strategic orchestration process through which firms combine AI assets with environmental routines to sense carbon risks, seize low-carbon opportunities, and reconfigure operating models. Using a panel of 1,842 large multinational listed firms from 14 industries over 2014-2024, we construct AICRS via transformer-based text classification of disclosures, triangulated with AI patents and validated against cloud-expenditure and green-AI job-posting microdata. Two-way fixed-effects, system-GMM, and staggered difference-in-differences estimators consistently show that AICRS improves financial performance (ROA, Tobin’s Q) and carbon performance, with dynamic capabilities partially mediating and AI capability amplifying the effect. Supplier-facing (Scope 3) AICRS initially attenuates financial returns owing to coordination costs but converges to comparable returns over five years while delivering persistent Scope 3 abatement, and netting out the energy footprint of AI deployment yields attenuated but still significant carbon gains. We reframe decarbonization as an AI-mediated dynamic-capability problem, deliver a replicable operationalization of AICRS, and surface contingencies that reconcile conflicting evidence on the sustainability performance link.
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