How Energy-Intensive Firms Achieve AI-Enabled Green Innovation: A Multi-Stage Framework of Orchestrating Ecosystems

In the race to net-zero, energy-intensive firms face significant challenges transitioning to AI-enabled green innovation (AIGI), which requires strategic orchestration of collaborative innovation across ecosystems. Based on an exploratory multiple case study of four leading Chinese energy-intensive firms (ecosystem orchestrators), we develop a multi-stage framework describing the scarcely understood process of how orchestrators address high -stickiness transformations to facilitate ecosystems in achieving AIGI. The findings demonstrate that ecosystem orchestrators identify and address transformation stickiness caused by AI function iteration through three iterative stages: assessing ecosystem readiness, creating institutional environments, and integrating ecosystem actors. This study contributes to ecosystem and green innovation literature by theorizing a structured orchestration process that is adapted tothe top-down strategic transformation intent of energy-intensive firms and matches differentiated ecosystem orchestration activities to diverse transformation stickiness. For engineering managers, the framework provides a diagnostic tool for continuous assessment and improvement, enhancing their capabilities in orchestrating ecosystems toward sustainable AIGI outcomes.