From Firefighting to Foresight: Why Trade Expertise Must Lead the Digitalization of Trade Compliance

Geopolitical tensions, industrial policy, export controls, sanctions regimes, and the resurgence of tariffs have fundamentally reshaped the trade landscape. Rules are not simplifying; they are multiplying, diverging across jurisdictions, and constantly changing. Trade compliance is no longer a periodic check — it is a continuous operational undertaking embedded in every sourcing decision, shipment, and supplier contract.

In response, businesses have acted rationally: hiring more trade lawyers, customs specialists, and compliance professionals. This is not merely about regulatory adherence. Trade compliance today is more financially material than ever. It directly affects landed cost, sourcing strategy, and market access. Tariffs and other trade-related measures have increased substantially, turning trade compliance decisions into bottom-line matters rather than administrative ones.

Yet, despite heavy investment in Enterprise Resource Planning (ERP) systems and global supply chain platforms, many trade compliance interventions remain manual. Most legacy systems fail to keep pace with round-the-clock regulatory changes. Trade master data — HS codes, origin information, licensing requirements, broker instructions — is frequently incomplete or poorly maintained. Compliance teams spend their days resolving shipment holds, correcting entries, and responding to audits. They remain reactive organizational firefighters when the business needs strategic advisors.

This is the gap digitalization is meant to close.

The AI Promise — and its Limits

Like almost every professional discipline, trade compliance is now looking to artificial intelligence (AI) for transformation. The appeal is obvious: AI systems can process vast regulatory datasets, screen transactions in real time, and embed compliance checks directly into operational workflows.

But current AI models, even the most sophisticated large language models, cannot (yet) perform trade compliance on their own. Trade compliance is not purely a data issue. It is a legal, interpretive, and context-dependent discipline in which the same product can receive different treatment depending on its origin, end-use, and the political relationship between the two countries on a given day.

At Besso, we recognized this early. Building effective digital compliance tools requires more than technical capability — it demands sustained collaboration between machine learning and trade law experts. Algorithms must be trained not only on structured data, but on the logic of customs rules, the interpretation of legal texts, and the practical realities of how goods cross borders. This cross-functional collaboration supports the explicability of algorithmic output, which is central to Besso’s business model.

As governments increasingly regulate market access, protect national security interests, and enforce economic policy through trade-related measures, trade policy is rapidly expanding into new domains. Trade topics now include critical technologies, sustainability requirements, supply chain transparency, and provenance. These changes require constant research and updating of knowledge bases, which tend to be multiple and diffuse.

Well-designed systems can embed regulatory intelligence into daily operations, ensuring that, for example, classification, origin analysis, and denied-party screening happen upstream rather than at the border. Nevertheless, those systems must be built on validated legal understanding. Without that foundation, automation does not reduce errors — it scales them.

The Black Box Problem

There is a growing and underappreciated risk in treating AI-driven compliance tools as opaque solutions. A system that produces answers without traceable reasoning is fundamentally incompatible with a domain where every declaration must be defensible to regulators.

Incorrect tariff classifications, misapplied origin rules, or outdated licensing checks do not remain technical errors — they become legal liabilities. Human expertise remains essential—not to perform repetitive checks, but to design, validate, and refine the systems performing them. Automation without expert oversight can therefore increase regulatory risk rather than reduce it. The answer is not less technology, but technology with visible reasoning and continuous human validation.

From Cost Centre to Competitive Advantage

When properly implemented, AI does not eliminate compliance teams — it elevates them. As routine validations get automated, compliance professionals shift toward higher-value work: optimizing sourcing decisions, identifying duty-saving opportunities, supporting market expansion, and advising leadership on regulatory risk.

In this model, trade compliance evolves from a defensive necessity into a genuine source of competitive advantage and strategic value. Teams stop focusing on mundane tasks and start advising on how to structure supply chains to minimize cost and risk. Digital tools are enablers of that transformation. But they only work when grounded in rigorous trade knowledge.

The Human-in-the-Loop Prerequisite

As trade becomes more digitalized, demand is growing for professionals who understand both regulation and technology — people who can interpret legal instruments, structure compliant data environments, and guide the responsible deployment of AI. A strong foundation in trade law and policy is not being made obsolete by technology. It is the prerequisite for making technology usable.

AI is unlikely to replace trade experts. However, experts equipped with the right digital tools will redefine how international trade operates. The next five years will be shaped not by automation alone, but by how well we combine technology that processes information with people who understand what it means.

About the author: Kholofelo Kugler

Kholofelo Kugler is Head of Trade Law at Besso, where she leads the company’s initiatives to automate trade law and support businesses in optimizing duty savings and managing rapidly evolving global trade regulations. Her work focuses on applying technology and data-driven tools to enhance how companies navigate international trade regulation and compliance.

Previously, she served as Counsel at the Advisory Centre on WTO Law (ACWL) in Geneva, Switzerland, where she represented governments in WTO dispute settlement proceedings and provided legal advice and training on World Trade Organization law. She was also a PhD Fellow in the ERC-funded project “Trade Law for the Data-Driven Economy (Trade Law 4.0)” at the University of Lucerne, Switzerland, where she researched the intersection of international trade law, the digital economy, and AI regulation.

Kholofelo is actively involved in international trade governance and dispute settlement. She was a member of the Task Force on the Mandatory Review of the African Continental Free Trade Area (AfCFTA) and the AfCFTA Digital Trade Task Force, contributing to policy development on digital trade and African economic integration. She is also included on the panelist rosters for dispute settlement under the EU–SADC Economic Partnership Agreement, the SACU–MERCOSUR Free Trade Agreement, and the European Union’s list of chairpersons for FTA disputes.

In addition to her professional work, she lectures on trade law, digital trade law, and African regional integration at academic and training institutions across Africa, the Caribbean, Europe, and the United States.

Kholofelo is also an alumna of the World Trade Institute (WTI) at the University of Bern. Her studies in international trade law and policy provided her with a strong foundation from which to launch her career.