Free Trade Agreements (FTAs) are designed to reduce or eliminate trade barriers between participating countries by applying preferential tariffs on imports and exports. Prominent examples include the United States-Mexico-Canada Agreement (USMCA), the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), and the EU-Canada FTA (CETA). To benefit from these agreements, products must meet specific rules of origin, proving they are genuinely produced within member countries.
Attempts to harmonize global trade rules, such as the Doha round of the World Trade Organization (WTO), have largely failed. This has left businesses navigating a complex web of bilateral and multilateral trade agreements with conflicting rules, often referred to as the “spaghetti bowl” phenomenon. Compliance with these agreements poses significant challenges.
Impact of Trade Wars and Geopolitical Tensions
Recent trade wars, notably between the United States and China, have reshaped global trade dynamics. These conflicts have led to protective measures, tariff hikes, and shifts in trade alliances, affecting not only the primary countries involved but also causing ripple effects across other continents. Geopolitical weaponization of trade has also intensified, with restrictions on Ukraine grain exports, tariffs on electric vehicles, and lingering COVID-19 protections.
As market access regulations become more restrictive, companies face increasing hurdles in shipping supplies and products across borders. These complexities can result in misaligned supply chain setups and suboptimal manufacturing decisions.
Underutilization of Trade Preferences
In this challenging environment, it is crucial for companies to fully utilize the benefits of preferential trade deals. However, trade statistics reveal that approximately one-third of eligible exports fail to take advantage of reduced or exempted duties from FTAs, costing companies billions. Understanding the barriers to utilization is key to capturing these benefits:
- Complex Rules of Origin: These require detailed documentation and specific calculations to prove a product’s origin, involving criteria such as regional value content, component value or weight, and tariff classification changes.
- Company Size: Larger companies tend to utilize trade preferences more effectively due to better resources and more sophisticated supply chain management.
- Company Sector: Industries such as agriculture and textiles face particularly high hurdles in utilizing FTA preferences due to complex rules of origin, extensive documentation, and calculations involving various data points.
Structural Issues in Trade Compliance
Many companies still perform trade compliance checks manually, increasing the likelihood of errors and inefficiencies. While trade compliance automation solutions exist within Enterprise Resource Planning (ERP) systems or as add-ons, they are often expensive and inflexible. These systems typically involve hardcoded rules and workflows for specific import or export flows, limiting their adaptability to changing regulations. Additionally, they often lack built-in regulatory intelligence, requiring companies to purchase costly regulatory data from external providers.
For many companies, the issue is compounded by insufficient quality of master data. Incomplete or incorrect data not only hinders the effective utilization of trade preferences but can lead to potential compliance issues and fines. Experts in trade compliance are hard to come by, and their time is often wasted on locating the right information, tracking regulations, and conducting manual checks.
The AI Opportunity
The trade compliance landscape is ripe for the application of Machine Learning techniques, particularly Generative AI (GenAI). The inflexibility of current systems, the scarcity of built-in regulatory intelligence, and the overwhelming manual workload present ideal conditions for AI intervention. However, this is unlikely to be a use case where one can simply ask ChatGPT for a reliable answer. The historical data needed to correctly train models is highly confidential and well-protected, outside the training purview of language models. Even if available, historical data reflects structural issues in trade compliance, so models might perpetuate past errors. Additionally, assessing whether AI performs the task correctly requires expert supervision and validation.
A combined approach of human expertise and AI is essential. Generative AI can automate and streamline data-intensive aspects of trade compliance, such as real-time regulatory updates and complex documentation requirements. Meanwhile, human experts are crucial for overseeing these processes, providing strategic insights, and handling complex interpretations and judgment calls that AI alone cannot manage.
Embracing AI for Trade Compliance
Given the geopolitical context and potential savings, AI-driven solutions are a priority for many corporations. New AI-driven solutions are emerging, although the landscape remains fragmented. Innovators and early adopters of next-generation AI solutions are likely to be rewarded significantly. The potential benefits are substantial.
By adopting these strategies and leveraging AI, companies can transform their approach to trade compliance. This will help navigate the complexities of global trade, ensure compliance, and maximize financial benefits. Companies that embrace these technologies will be better positioned to compete in the global market, maintaining access to crucial markets and optimizing their trade operations. With automation and AI, trade compliance can become progressively more effective and cost-efficient.
