AI adoption powers corporate sustainability in the digital economy

Authentic adoption of artificial intelligence (AI) can significantly enhance corporate sustainability performance, says a new study published in Frontiers in Artificial Intelligence, which offers the first large-scale empirical analysis linking AI integration to environmental, social, and governance (ESG) outcomes across Chinese listed firms.

Titled "Artificial Intelligence Adoption and Corporate ESG Performance: Evidence from a Refined Large Language Model", the study investigates how the implementation, not mere mention, of AI technologies influences companies' long-term sustainability goals. Using an advanced large language model (Qwen2.5-72B) fine-tuned to detect real-world AI applications, the authors differentiate genuine adoption from rhetorical or aspirational claims, providing one of the most precise measures of digital transformation to date.

How AI adoption elevates corporate ESG performance

The study is based on 22,931 firm-year observations from Chinese A-share listed companies between 2009 and 2022, analyzing how AI adoption affects ESG performance through measurable corporate behavior. By refining AI classification through large-scale text analysis, the authors identify firms actively using AI in operations, decision-making, and management processes.

The results show that AI adoption leads to significant improvements in ESG scores, with an estimated positive effect of 3.3 percent relative to firms without AI integration. These findings remain consistent across multiple robustness tests, including instrumental variable regression, lagged models, and alternative ESG measures.

According to the research, AI contributes to sustainability not only by enhancing environmental efficiency but also by strengthening corporate governance and social accountability. Firms that integrate AI into their operations show measurable improvements in data transparency, risk management, and compliance monitoring. AI's analytical power allows companies to detect irregularities, automate sustainability reporting, and manage supply-chain risks more effectively.

Moreover, the authors find that AI improves ESG outcomes through two key mechanisms: green innovation and internal control quality. AI-driven automation and analytics enable companies to identify opportunities for energy savings and emission reduction while optimizing production efficiency. Simultaneously, AI enhances the reliability of internal auditing systems, helping organizations identify compliance breaches, mitigate fraud, and improve oversight.

Bridging technology and sustainability through innovation and governance

The study's theoretical foundation combines the Resource-Based View (RBV) and the Technology–Organization–Environment (TOE) framework to explain why AI acts as a strategic enabler for sustainable performance. The RBV perspective suggests that AI functions as a valuable and rare resource that strengthens a firm's competitive advantage by fostering innovation and operational excellence. Under the TOE framework, AI adoption reflects the firm's technological readiness, organizational culture, and environmental adaptability, factors that jointly determine the success of digital transformation in sustainability contexts.

Empirical analysis shows that green innovation serves as a crucial transmission channel linking AI adoption and improved ESG scores. Firms leveraging AI technologies in research and development demonstrate higher rates of green patent applications and invest more in sustainable technologies such as renewable energy optimization and waste minimization. AI tools enhance the precision of R&D decision-making by modeling environmental impact, predicting market feasibility, and supporting real-time data-driven innovation.

At the same time, AI significantly enhances internal control quality, reinforcing governance standards. Automated audit systems powered by machine learning detect inconsistencies in financial statements and compliance reports more effectively than manual reviews. Predictive algorithms also help corporate boards anticipate operational risks, strengthening transparency and accountability across the organization.

The combined effect of these mechanisms positions AI not only as a digital innovation but also as an institutional catalyst, a technology that simultaneously supports environmental protection, social responsibility, and corporate ethics.

Uneven gains and policy directions for responsible AI integration

The study warns that its positive effects are unevenly distributed across firms. Large enterprises and technology-intensive sectors capture most of the benefits due to superior infrastructure, technical expertise, and financial capacity. Smaller firms, constrained by limited digital readiness and capital investment, lag behind, a phenomenon the authors describe as a widening "digital ESG divide."

This disparity raises important policy concerns. Without targeted support, small and medium-sized enterprises risk exclusion from the AI-driven sustainability transition. The study recommends government-led initiatives to promote equitable access to AI technologies, including subsidies, technical training, and standardized digital platforms that lower entry barriers for smaller firms.

The authors also advocate for trustworthy AI governance frameworks that ensure responsible use of intelligent systems in sustainability management. They call for stricter data protection rules, algorithmic transparency, and regular audits of AI models used in ESG reporting to prevent misuse or greenwashing.

Besides the firm level, the findings have strategic implications for regulators, investors, and international sustainability organizations. As global markets increasingly tie capital flows to ESG ratings, AI's role in improving data accuracy and real-time monitoring could redefine corporate accountability standards. The study suggests that policy alignment between digital transformation strategies and ESG regulations will be essential to sustain long-term progress.

Looking forward, the researchers call for interdisciplinary collaboration among data scientists, sustainability experts, and corporate managers to design explainable and ethical AI systems. Only by embedding human oversight into algorithmic governance can firms maintain trust while reaping the environmental and social benefits of intelligent automation.