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Harnessing AI Capacities for Sustainable Business Models: Empowering Firms across Economic, Social, and Environmental Dimensions

Giulia Nevi et al.

IEEE Transactions on Engineering Management2026https://doi.org/10.1109/tem.2026.3677052article
AJG 3ABDC A
Weight
0.50

Abstract

The rapid advance of artificial intelligence (AI) is transforming industries by enabling cost reduction, personalised solutions, and improved customer service. AI also supports sustainability by facilitating data-driven decisions, operational efficiency, and better management of environmental and social externalities. However, a gap persists between technological potential and the effective adoption of sustainable and responsible business practices. This study explores how AI capacities contribute to sustainable business model (SBM) innovation, focusing on the Italian cosmetics sector. Based on a qualitative analysis of interviews with chief executive officers (CEOs) and senior managers from 11 companies, the research highlights how dynamic capabilities (DCs) powered by AI capacities enable organisations to sustainably enhance value creation, delivery, and capture. The findings identify best practices that leverage AI for sustainability, including product personalisation, process automation, and predictive analytics. The study provides actionable insights into how AI-based systems can drive both innovation and sustainability. Building on these insights, the paper proposes a theoretical framework that conceptualises the link between lower-order AI capacities, high order DCs, and SBM innovation. Within this framework, best practices emerge as the operational outcome of AI-enabled DCs: they not only innovate processes and practices but also act as catalysts that transform and reconfigure the business model itself towards sustainability. By contextualising these insights within a sector-specific framework, the paper contributes to the growing body of knowledge on AI's role in supporting sustainable business transformation.

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https://doi.org/https://doi.org/10.1109/tem.2026.3677052

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@article{giulia2026,
  title        = {{Harnessing AI Capacities for Sustainable Business Models: Empowering Firms across Economic, Social, and Environmental Dimensions}},
  author       = {Giulia Nevi et al.},
  journal      = {IEEE Transactions on Engineering Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tem.2026.3677052},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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