Decision-making in the agricultural sector has become more complex and challenging as climate change and ethical demands alter traditional approaches, which are based on intuition, historical data and straightforward methods. Proactive risk assessment and optimized supply chains are now more difficult to navigate with added variables, but technologies have the potential to transform this current capacity. In the article by Gupta (2026), the author examines how artificial intelligence can be used to optimize resource use, reduce waste and integrate sustainability measures across supply chains in the agricultural sector.The agricultural supply chain is highly complex, involving numerous diverse stakeholders and the management of more vulnerable ecological systems. Decision-making in the agricultural sector has historically relied on managerial experience, historical data and seasonal knowledge. Still, this existing model is not well-suited to emerging factors such as climate change, resource scarcity and more sustainability-minded consumers.Currently, data are fragmented across the various stages of the agricultural supply chain (such as farming, processing and distribution), and strategic decision-making is reactive rather than proactive. The factors that influence decision-making are typically considered in isolation, which limits the agricultural supply chain’s ability to forecast demand and risks, as well as to adapt rapidly. With an isolated decision-making system, sustainability metrics such as carbon emissions, water use and labor conditions are much harder to thoughtfully and holistically consider in the decision-making process.Artificial intelligence offers a solution to this issue, not just as an automated tool but as a logical collaborator that can support strategic decision-making aligned with sustainability goals. By offering features such as predictive analytics, machine learning, real-time data management and data security via blockchain, artificial intelligence can transform the current strategic decision-making system. Artificial intelligence can analyze many more factors across the entire supply chain, leading to optimized resource use, reduced waste and smaller environmental footprints.Gupta (2026) highlights several potential methods for artificial intelligence to optimize the agricultural supply chain. For example, decision-making about which crops to plant currently relies heavily on farmers’ judgment and historical trends. Artificial intelligence can analyze soil health, weather forecasts, satellite imagery and market demand and use machine learning to forecast risk, prices, suitability and potential yield. Real-time predictions provide actionable sowing recommendations, and continuous monitoring and feedback further improve the model. The author found that the benefits of artificial intelligence-driven crop selection include greater resilience to climate change, better alignment with market demand and improved long-term planning for food security.Artificial intelligence–driven crop production planning also positively affects the agricultural supply chain. Current production challenges can be addressed by predicting soil fertility and yields, optimizing processes such as irrigation and fertilization, early detection of disease and weeds, and enhanced communication with farmers. Artificial intelligence can also support quality control through predictive analytics and automated inspections, monitor storage conditions to prolong crop life and automate tasks such as harvesting, weeding, planting and packaging to reduce labor costs. Quality parameters can ensure more accurate shelf-life predictions, increased reputation and alignment with sustainability and quality certifications.Artificial intelligence can cover both ends of the supply chain, managing market engagement and consumer outreach. Predicting prices and optimal selling times, building a dynamic pricing model, predicting the best time to enter the market, translation capabilities for diverse stakeholders, generating marketing content and highlighting transparency and building consumer trust and loyalty with real-time updates and sustainability messaging all elevate a firm as a top choice for informed and increasingly sustainability-minded consumers.Overall, artificial intelligence can transform an isolated, reactive and slower decision-making model into one that is anticipatory, evidence-based, holistic and rapid. By providing guidance on achieving long-term sustainability, increasing efficiency and improving resilience, artificial intelligence can take strategic decision-making to the next level.However, there are barriers to implementing artificial intelligence in the agricultural sector, including affordability, limited digital literacy, insufficient infrastructure and concerns about data use, which must be addressed before the tool can be integrated en masse.The research put forward in the article offers several practical implications:The agricultural sector’s current strategic decision-making process does not optimally account for the entire supply chain, climate change and changing market demand. In the article by Gupta (2026), the author identifies many ways in which artificial intelligence can transform the agricultural decision-making process, from crop selection to customer outreach, highlighting the tool’s capacity to improve sustainability and efficiency if affordability and literacy barriers can be overcome.This review is based on “AI-augmented sustainability management in agri-food supply chain: rethinking decision-making in the era of algorithms” by Sandeep Kumar Gupta, published in Journal of Agribusiness in Developing and Emerging Economies.
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