Economics, Business and Management: Recent Advances Vol. 4

Agribusiness management is entering a period in which competitive performance can no longer be separated from climate exposure, digital capability, resource productivity and the distribution of value across supply-chain actors. This critical narrative review examines how artificial intelligence, climate resilience, circularity, digital markets and sustainable business-model innovation are reshaping managerial choices in agrifood systems. Literature was selected through live scholarly searching and bibliographic verification, with emphasis on peer-reviewed evidence published from 2015 to 10 July 2026 and inclusion of earlier foundational studies where conceptually necessary. The evidence indicates that these domains should not be treated as independent technology or sustainability agendas. Artificial intelligence can improve sensing, prediction and targeting, yet field-level sustainability gains remain context dependent and are constrained by data quality, interoperability, skills, ownership and power asymmetries. Climate resilience requires redundancy, diversity, adaptability and transformative capacity, which can conflict with short-term efficiency objectives. Circularity can convert losses and by-products into resources, but outcomes depend on life-cycle boundaries, quality assurance, logistics and viable markets. Digital platforms can reduce information and coordination frictions while simultaneously concentrating control over data, standards and market access. Sustainable value creation therefore depends on business models capable of combining financial viability with environmental integrity, resilience and fair participation. An integrated management architecture is proposed around five linked functions: sense, decide, coordinate, regenerate and account. The review argues that future agribusiness advantage is more likely to arise from orchestrating these complementary capabilities than from adopting individual technologies in isolation. Priority research needs include longitudinal causal evaluation, interoperable data governance, resilience-adjusted performance measurement, distributional impact assessment and comparative evidence from smallholder and emerging-market contexts.