Authors: Wani Ishwari Dnyaneshwar, Professor Abhishek Popat Bangar, Professor Bhosale S. B., Dr. A. A. Khatri
Abstract: Cross-border trade remains costly and uneven. Exporters, especially small firms in developing regions, face language barriers, limited market knowledge, complex customs and compliance procedures, fragmented logistics and unequal access to digital tools. Artificial intelligence (AI) is widely proposed as a way to lower these costs and to improve global marketing through translation, demand forecasting, segmentation, personalisation and process automation. This paper presents a focused narrative review that synthesises five sources: a causal field study of machine translation on an online trade platform, a strategic framework for AI in marketing, a bibliometric review of 1,580 AI-in-marketing papers, a systematic review of 64 studies of AI in supply chain management, and the World Trade Organization’s World Trade Report 2025. The review examines three connected themes: AI techniques and marketing frameworks relevant to trade, the strength of the empirical evidence, and the conditions that matter for deployment by small exporters. The only causal study reports that a machine translation upgrade raised exports on eBay by 10.9%, which its authors describe as equivalent to reducing the distance between trading countries by 26.1%. The WTO projects that AI could raise cross-border trade in goods and services by nearly 40% by 2040, but only with enabling policies, and warns that unequal access could widen divides. However, the technical literature is dominated by model-building, few studies test usability or generalisability, and most are not specific to an industry or country. The review contributes a comparative table, a conceptual architecture for an AI-enabled export platform, and a prioritised agenda of research gaps.
