International business operations cannot be complete without considering global trade compliance to maintain quality, such as legal and regulatory standards for goods and services of different countries. Product classification is one of the important elements of global trade compliance – the classification of goods according to systems based around the world (such as the Harmonized System (HS) code). The classification of the product is used to determine the tariffs, duties, and legal compliances; hence, businesses have to ensure no such penalties, delays, or shipment issues. The traditional product classification was done manually using systems that were highly prone to human error, inefficiency, and inconsistencies. However, automation in general, especially with artificial intelligence (AI) and machine learning (ML), has transformed the process. Large datasets and algorithms are used in automated product classification systems, which help in faster, more accurate, and consistent results, thus minimizing the risk of errors. These systems can link to other tools for trade compliance, creating a smooth and effective means of global trade. However, businesses struggle to implement automation in trade compliance due to overcoming technical complexities, resistance to change, the need for specialized training, and factoring them into medium to scaling automation with business growth. These challenges must be overcome with rigorous data governance, continuous employee training, and integrated systems. Though businesses must continue to adapt to new procedures in today’s globalized world and growing numbers of regulations, automation is here to stay as it continues to evolve, promising to ensure global trade compliance and giving businesses the ability to stay competitive in an increasingly complicated global market.

Automation, Trade Compliance, Product Classification, AI and Machine Learning, Global Regulations, System Integration

Akisik, O., & Gal, G. (2017). The impact of corporate social responsibility and internal controls on stakeholders’ view of the firm and financial performance. Sustainability Accounting, Management and Policy Journal, 8(3), 246-280.

Arora, M., & Baldi, A. (2015). Regulatory categories of probiotics across the globe: a review representing existing and recommended categorization. Indian journal of medical microbiology, 33, S2-S10.

Bansal, A. (2020). System to redact personal identified entities (PII) in unstructured data. International Journal of Advanced Research in Engineering and Technology, 11(6), 133. https://doi.org/10.34218/IJARET.11.6.133

Ben-Larbi, M. K., Pozo, K. F., Haylok, T., Choi, M., Grzesik, B., Haas, A., ... & Stoll, E. (2021). Towards the automated operations of large distributed satellite systems. Part 1: Review and paradigm shifts. Advances in Space Research, 67(11), 3598-3619.

Capela, J. J. (2015). Import/export kit for dummies. John Wiley & Sons.

Chatelus, R., & Heine, P. (2016). Rating correlations between customs codes and export control lists: Assessing the needs and challenges. Strategic Trade Review, 2(3), 34-67.

Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492.

Collins, A., & Halverson, R. (2018). Rethinking education in the age of technology: The digital revolution and schooling in America. Teachers College Press.

Cooper, L. A., Holderness Jr, D. K., Sorensen, T. L., & Wood, D. A. (2019). Robotic process automation in public accounting. Accounting Horizons, 33(4), 15-35.

Estlund, C. (2018). What should we do after work? Automation and employment law. The Yale Law Journal, 254-326.

Ferraro, G., & Brody, E. K. (2015). The cultural dimension of global business (1-download). Routledge.

Fiksel, J., & Fiksel, J. R. (2015). Resilient by design: Creating businesses that adapt and flourish in a changing world. Island Press.

Grant, O., & Agoro, H. (2021). Trends in Network Compliance and Regulatory Challenges.

Hajkowicz, S., Reeson, A., Rudd, L., Bratanova, A., Hodgers, L., Mason, C., & Boughen, N. (2016). Tomorrow’s digitally enabled workforce: Megatrends and scenarios for jobs and employment in Australia over the coming twenty years. Australian Policy Online.

Keilty, P. (2018). Tedious: feminized labor in machine-readable cataloging. Feminist media studies, 18(2), 191-204.

Kipkoech, B. J. (2020). Effect of Customs procedures on the performance of clearing and forwarding agents operating at Customs Entry Points: a case of Inland Container Depot Nairobi.

Knudsen, J. S., & Moon, J. (2017). Visible hands: Government regulation and international business responsibility. Cambridge University Press.

Kohl, T., Brakman, S., & Garretsen, H. (2016). Do trade agreements stimulate international trade differently? Evidence from 296 trade agreements. The World Economy, 39(1), 97-131.

Kommineni, H. P. (2020). Automating SAP GTS Compliance through AI-Powered Reciprocal Symmetry Models. International Journal of Reciprocal Symmetry and Theoretical Physics, 7, 44-56.

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in industry, 81, 11-25.

Liu, H. W., & Lin, C. F. (2020). Artificial intelligence and global trade governance: a pluralist agenda. Harv. Int'l LJ, 61, 407.

Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H. (2020). Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and software technology, 127, 106368.

Martinez, V. R. (2020). Complex compliance investigations. Columbia Law Review, 120(2), 249-308.

Mukherjee, A., & Kapoor, A. (2018). Trade Rules in E-commerce: WTO and India (No. 354). Working Paper.

Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230

Paasivaara, M., Behm, B., Lassenius, C., & Hallikainen, M. (2018). Large-scale agile transformation at Ericsson: a case study. Empirical Software Engineering, 23, 2550-2596.

Pasquale, F. (2019). A rule of persons, not machines: the limits of legal automation. Geo. Wash. L. Rev., 87, 1.

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., & Barnes, P. (2020, January). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 33-44).

Sabatucci, L., & Cossentino, M. (2019). Supporting dynamic workflows with automatic extraction of goals from BPMN. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 14(2), 1-38.

Sadeghi, V. J., Nkongolo-Bakenda, J. M., Anderson, R. B., & Dana, L. P. (2019). An institution-based view of international entrepreneurship: A comparison of context-based and universal determinants in developing and economically advanced countries. International Business Review, 28(6), 101588.

Singh, V., Unadkat, V., & Kanani, P. (2019). Intelligent traffic management system. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 7592-7597. https://www.researchgate.net/profile/Pratik-Kanani/publication/341323324_Intelligent_Traffic_Management_System/links/5ebac410299bf1c09ab59e87/Intelligent-Traffic-Management-System.pdf

Soeteman-Hernandez, L. G., Apostolova, M. D., Bekker, C., Dekkers, S., Grafström, R. C., Groenewold, M., & Noorlander, C. W. (2019). Safe innovation approach: Towards an agile system for dealing with innovations. Materials Today Communications, 20, 100548.

Solihin, W., & Eastman, C. (2015). Classification of rules for automated BIM rule checking development. Automation in construction, 53, 69-82.

Song, B., Yan, W., & Zhang, T. (2019). Cross-border e-commerce commodity risk assessment using text mining and fuzzy rule-based reasoning. Advanced Engineering Informatics, 40, 69-80.