Generative AI spurs sustainable competitiveness across manufacturing sectors

Generative artificial intelligence, GenAI, is proving to be more than a technical tool in manufacturing. It is shaping how companies create and deliver value while supporting sustainable business outcomes. A new study, titled “Generative AI and Sustainable Performance in Manufacturing Firms: Roles of Innovations and AI Regulation,” published in Sustainability, reveals that the integration of generative AI can significantly enhance sustainable performance, especially when combined with strong regulatory frameworks.

The research analyzes how generative AI drives positive outcomes not just through its direct effects but also by enabling two complementary innovation paths: novelty-centered and efficiency-centered business model innovations. It also examines the moderating influence of AI regulation, highlighting that effective governance amplifies the sustainability benefits of AI adoption.

Understanding how generative AI drives sustainability

The study explores how generative AI contributes to sustainability goals in manufacturing firms. The authors find that generative AI acts as a meta-resource, helping firms leverage existing capabilities more efficiently while transforming processes to create new forms of value.

The research is based on a large-scale survey of 1,192 managers across 500 manufacturing firms in China, analyzed through structural equation modeling. It shows that firms using generative AI achieve higher sustainable performance metrics, such as reduced waste, optimized resource use, and eco-friendly production practices, compared to those that have not adopted it.

Generative AI’s dual role is central to its impact. On one side, it supports novelty-centered innovation, enabling firms to redesign their business models with new products, services, and delivery channels. On the other, it strengthens efficiency-centered innovation, improving existing processes, cutting operational waste, and enhancing resource allocation. These combined effects help align profitability with long-term sustainability objectives.

The role of innovation pathways

The second major question explored in the study is through which mechanisms AI adoption drives these positive outcomes. The researchers identify innovation as the primary channel, with two distinct pathways.

Novelty-centered business model innovation involves using AI to break traditional molds, such as developing eco-friendly products, entering green supply chains, or creating new service platforms that reduce the environmental footprint of manufacturing operations. These innovations often lead to transformative shifts in business practices that redefine competitive advantage.

Efficiency-centered business model innovation, by contrast, focuses on optimizing existing processes. This includes using AI for predictive maintenance, smarter inventory management, and energy-saving production schedules. Such incremental improvements deliver measurable gains in sustainability without requiring radical changes to the overall business strategy.

The findings reveal that both pathways are critical, but they complement each other in unique ways. Firms that integrate AI with a balanced focus on novelty and efficiency see the greatest improvements in their sustainable performance.

The regulatory environment as a key enabler

The third critical question addressed by the study concerns the impact of AI regulation. While regulation is often seen as a constraint on innovation, the research shows that it can act as a catalyst when aligned with sustainability goals.

The study finds that supportive and well-structured AI regulations strengthen the links between AI adoption and both forms of business model innovation. Firms operating in clearer regulatory environments are more likely to channel AI investments toward sustainable solutions, ensuring compliance while unlocking greater value.

This insight underscores the need for governments and industry regulators to provide transparent, predictable, and sustainability-focused frameworks. Such policies encourage firms to innovate responsibly and use AI to meet environmental and social performance targets.

Implications for business leaders and policymakers

The research provides actionable guidance for corporate managers and policymakers alike. Managers are advised to align their AI strategies with their chosen innovation pathway, using novelty-centered approaches when aiming for transformational change and efficiency-centered approaches for incremental progress. Building organizational readiness, including staff training and digital capability development, is essential to harness AI effectively.

For policymakers, the findings highlight the importance of viewing regulation not merely as a compliance requirement but as an enabling infrastructure that supports responsible innovation. When regulatory frameworks are clear and aligned with sustainability objectives, they help firms scale AI solutions that deliver both economic and environmental benefits.