Paper Number
ECIS2026-2875
Paper Type
SP
Abstract
The twin transition links digital transformation and sustainability, yet the role of artificial intelligence (AI) in supporting sustainability-oriented business model reconfiguration remains underexplored in Information Systems research. This paper examines how an AI-enabled platform supports such reconfiguration through a qualitative case study of a Canadian engineering firm, EnginiCo. Drawing on interviews, observations, and documentary data, we show how the firm’s AI platform, OptiMesh, supported a shift from transactional consulting to recurring, outcome-oriented services centered on asset durability, lifecycle performance, and sustainability value. Our findings highlight four dimensions of EnginiCo’s AI-enabled digital transformation: strategic drivers and framing, AI-enabled business model reconfiguration, governance and capability building, and trade-offs and constraints. These dimensions show how AI can support twin transition ambitions beyond pilot projects while remaining shaped by organizational, technical, and economic tensions. The paper contributes to research on twin transition, dynamic capabilities, and sustainability-oriented digital transformation.
Recommended Citation
Mosconi, Elaine and Boudreau, Marie-Claude, "From Brown To Green: How Ai-Driven Transformation Enables Business Model Reconfiguration In The Twin Transition" (2026). ECIS 2026 Proceedings. 8.
https://aisel.aisnet.org/ecis2026/twin/twin/8
From Brown To Green: How Ai-Driven Transformation Enables Business Model Reconfiguration In The Twin Transition
The twin transition links digital transformation and sustainability, yet the role of artificial intelligence (AI) in supporting sustainability-oriented business model reconfiguration remains underexplored in Information Systems research. This paper examines how an AI-enabled platform supports such reconfiguration through a qualitative case study of a Canadian engineering firm, EnginiCo. Drawing on interviews, observations, and documentary data, we show how the firm’s AI platform, OptiMesh, supported a shift from transactional consulting to recurring, outcome-oriented services centered on asset durability, lifecycle performance, and sustainability value. Our findings highlight four dimensions of EnginiCo’s AI-enabled digital transformation: strategic drivers and framing, AI-enabled business model reconfiguration, governance and capability building, and trade-offs and constraints. These dimensions show how AI can support twin transition ambitions beyond pilot projects while remaining shaped by organizational, technical, and economic tensions. The paper contributes to research on twin transition, dynamic capabilities, and sustainability-oriented digital transformation.