AI-Driven Eco-Conscious Production Management in Agri-Business: Accounting and Competitive Dynamics - Eshbayev Oybek; Ibragimova Iroda; Eshpulatov Dostonbek; Jamalova Gulnora; Mamadjanova Nozimaxon; Rizaeva Kamola (SHS Web of Conferences, 2025)
The ongoing digitally enabled transformation processes have an impact on the accountability and efficiency of eco-conscious production systems. There is a significant research gap in understanding those operational complexities and finding data-driven responses to them. The aim of this work is to analyze the influence of AI-powered managerial factors on sustainable production outcomes in agri-business enterprises. Based on a review of the existing literature and sentiment analysis and cosine similarity method, we identify the drivers of green production performance as well as the competitive impacts of eco-conscious management strategies on these drivers. The results of the integrated computational analysis confirmed the hypothesis that to strengthen sustainable practices in agri-business, measures should be taken aiming at streamlining resource use, reducing environmental externalities, supporting intelligent forecasting and adaptive supply chains, developing real-time monitoring systems to provide decision transparency and strategic agility. The paper finishes with practical recommendations and policy directions for industry stakeholders. Thus, AI-driven eco-conscious production management boosts the competitive sustainability by helping to anticipate market shifts, optimize operational flows, and enhance value creation with increased resilience. Overall, we find that sentiment-driven indicators, easy-to-use AI-enabled dashboards can monitor perception fluctuations and changes related to environmental condition and managerial activity in three dominant agri-business clusters.