How trade compliance is moving beyond automation toward AI-driven decision infrastructure
14 Feb 20266 min read
Summary
- Compliance failures now ripple across just-in-time operations, reputations, and market access. As regulations, tariffs, and enforcement shift faster, compliance must manage volatility at scale rather than react after border disruptions occur.
- Generative AI is useful for explanation and drafting, but trade compliance demands deterministic, source-bound systems. Agentic AI, designed to execute workflows and pull from authoritative regulatory sources, enables reliable classification, documentation checks, and real-time regulatory monitoring.
- Compliance teams will increasingly supervise AI-driven processes, handle exceptions, and make judgment calls, turning compliance into a strategic enabler rather than a bottleneck.
The first inaugural webinar of the International Trade Trust Compliance Network (ITTC) opened with a timely provocation: trade compliance is one of the most misunderstood functions in cross-border trade, and it is about to change faster than most organizations are prepared for.
Mike Yap, a trade compliance leader with more than 25 years of experience across strategic leadership and international markets, laid out a practical roadmap for what comes next. His message was that AI will not simply make compliance teams faster, but it will reshape how compliance work is structured, who does what, and what “good” looks like in a time of constant volatility.
Mike Yap, a trade compliance leader with more than 25 years of experience across strategic leadership and international markets, laid out a practical roadmap for what comes next. His message was that AI will not simply make compliance teams faster, but it will reshape how compliance work is structured, who does what, and what “good” looks like in a time of constant volatility.
Why trade compliance is hard even when you are doing it right
Trade compliance is often invisible when it works. When shipments move smoothly, few people notice the classification checks, documentation verification, and regulatory decision-making that happened upstream. However, the moment something goes wrong, the function becomes painfully visible.
Yap framed the challenge with an everyday comparison. Booking a ship or arranging a truck is straightforward. The complexity shows up in the layers of customs requirements, product rules, origin declarations, and documentation standards that must be correct before a shipment ever crosses a border to the customer. And this is not optional work, this trade compliance mechanism exists because states protect sovereignty through revenue collection, safety controls, anti-contraband enforcement, and standards like food safety.
Yap emphasized that the cost of failure is not limited to duties and tariffs. For companies built on just-in-time operations, a single hold at the border can cascade into a multitude of other problems causing volatility.
The worst-case scenario escalates further to reputational damage and even blacklisting because these can travel quickly through media and network. That is when compliance failure becomes a business risk.
Yap framed the challenge with an everyday comparison. Booking a ship or arranging a truck is straightforward. The complexity shows up in the layers of customs requirements, product rules, origin declarations, and documentation standards that must be correct before a shipment ever crosses a border to the customer. And this is not optional work, this trade compliance mechanism exists because states protect sovereignty through revenue collection, safety controls, anti-contraband enforcement, and standards like food safety.
Yap emphasized that the cost of failure is not limited to duties and tariffs. For companies built on just-in-time operations, a single hold at the border can cascade into a multitude of other problems causing volatility.
The worst-case scenario escalates further to reputational damage and even blacklisting because these can travel quickly through media and network. That is when compliance failure becomes a business risk.
AI is not one thing, and that matters
A central value of Yap’s presentation was clarity. Many organizations use “AI” as a catch-all label, but his argument depended on distinguishing two different categories of AI capability.
He explained that generative AI is what most people recognize today: tools that produce text, images, and content through large language models like ChatGPT and Gemini. It is strong at pattern recognition and synthesis. It can help teams draft documents, summarize policies, or explain complex concepts in plain language, but it is not designed to run crucial business logistics operations.
Agentic AI is different. It is goal-directed and action-oriented without the need to feed it prompts. It is built to execute workflows, handle repetitive processes, and trade compliance terms; agentic AI can be used to process documents, extract data from invoices, validate required fields, perform classification tasks, and route exceptions for review.
Generative AI can produce different answers to the same question across different days. However the output can be well-written and persuasive, while still being factually shaky and outright wrong because of context issues or hallucination by the model. In trade compliance, that variability is unacceptable because one incorrect interpretation of a duty rate, an effective date, or a classification can trigger financial exposure and customs scrutiny.
So Yap explained that the goal is not to “use AI.” The goal is to make AI outputs as deterministic as possible in operational settings. How does that happen? He described an approach where AI is guided to trusted sources and constrained by design. For example, instead of allowing a model to pull from broad, uncertain information, an AI workflow in the compliance context can be instructed to retrieve and interpret information from authoritative regulators such as a national customs website or data from the company itself. The system can then cite where it derived the answer and, importantly, pull the latest version of the policy at the moment the query is made.
In practice, this means AI becomes less like a creative chatbot and more like a controlled compliance worker that follows a rules-based sourcing discipline.
He explained that generative AI is what most people recognize today: tools that produce text, images, and content through large language models like ChatGPT and Gemini. It is strong at pattern recognition and synthesis. It can help teams draft documents, summarize policies, or explain complex concepts in plain language, but it is not designed to run crucial business logistics operations.
Agentic AI is different. It is goal-directed and action-oriented without the need to feed it prompts. It is built to execute workflows, handle repetitive processes, and trade compliance terms; agentic AI can be used to process documents, extract data from invoices, validate required fields, perform classification tasks, and route exceptions for review.
Generative AI can produce different answers to the same question across different days. However the output can be well-written and persuasive, while still being factually shaky and outright wrong because of context issues or hallucination by the model. In trade compliance, that variability is unacceptable because one incorrect interpretation of a duty rate, an effective date, or a classification can trigger financial exposure and customs scrutiny.
So Yap explained that the goal is not to “use AI.” The goal is to make AI outputs as deterministic as possible in operational settings. How does that happen? He described an approach where AI is guided to trusted sources and constrained by design. For example, instead of allowing a model to pull from broad, uncertain information, an AI workflow in the compliance context can be instructed to retrieve and interpret information from authoritative regulators such as a national customs website or data from the company itself. The system can then cite where it derived the answer and, importantly, pull the latest version of the policy at the moment the query is made.
In practice, this means AI becomes less like a creative chatbot and more like a controlled compliance worker that follows a rules-based sourcing discipline.
Humans stay accountable, even when AI is involved
During the Q&A, a key question surfaced: if AI gets it wrong, who is accountable?
Yap explained that accountability remains with the declarant and the company making the declaration. AI may support the process, but it does not carry legal responsibility for the results of its responses. This is especially relevant for teams considering automation at scale. The internal governance model cannot outsource accountability to a tool.
What AI can do, however, is strengthen defensibility. This means capturing what sources were used and what information was present at the time of a decision, AI systems can support audit trails and help organizations demonstrate reasonable reliance on published rules, even when a regulator’s guidance changes later.
Yap also pushed the audience to think beyond today’s popular buzzwords. Early conversations around AI spawned a fascination with “prompt engineering.” That may matter for content workflows, but in operations of supply chain and logistics, the emerging role looks different.
He described a shift toward hybrid roles, where humans supervise AI-driven processes rather than manually execute every step. This looks like people handling exceptions, approving edge cases, tune workflows, and making final decisions when data or context is ambiguous. This is closer to an AI supervisor function than a prompt engineer function.
Yap explained that accountability remains with the declarant and the company making the declaration. AI may support the process, but it does not carry legal responsibility for the results of its responses. This is especially relevant for teams considering automation at scale. The internal governance model cannot outsource accountability to a tool.
What AI can do, however, is strengthen defensibility. This means capturing what sources were used and what information was present at the time of a decision, AI systems can support audit trails and help organizations demonstrate reasonable reliance on published rules, even when a regulator’s guidance changes later.
Yap also pushed the audience to think beyond today’s popular buzzwords. Early conversations around AI spawned a fascination with “prompt engineering.” That may matter for content workflows, but in operations of supply chain and logistics, the emerging role looks different.
He described a shift toward hybrid roles, where humans supervise AI-driven processes rather than manually execute every step. This looks like people handling exceptions, approving edge cases, tune workflows, and making final decisions when data or context is ambiguous. This is closer to an AI supervisor function than a prompt engineer function.
The takeaway for compliance and supply chain leaders
For leaders, the near-term win of AI is the reduced manual workload, accelerated document handling, improved anomaly detection, and being able to do regulatory monitoring closer to real time.
But the bigger advantage comes from reimagining compliance as a strategic capability. If AI can help teams manage complexity at scale, identify risks earlier, and execute processes with fewer errors, compliance can stop being a bottleneck for organizations but becomes a driver for expansion.
In a world where tariffs, countermeasures, and regulatory updates can change quickly, Yap’s core message is that the organizations that treat AI as operational infrastructure, not a novelty tool, will be the ones that keep moving when others get stuck at the border.
But the bigger advantage comes from reimagining compliance as a strategic capability. If AI can help teams manage complexity at scale, identify risks earlier, and execute processes with fewer errors, compliance can stop being a bottleneck for organizations but becomes a driver for expansion.
In a world where tariffs, countermeasures, and regulatory updates can change quickly, Yap’s core message is that the organizations that treat AI as operational infrastructure, not a novelty tool, will be the ones that keep moving when others get stuck at the border.
About Mike Yap
Mike Yap has over 25 years of experience in trade compliance, strategic leadership, and international market expansion. He has led teams supporting enterprises in navigating complex compliance requirements, scaling cross-border transactions, and implementing digital initiatives to strengthen trade operations. His background spans business analysis, consulting, and business intelligence, grounding his work in practical, data-driven execution. Mike holds a Bachelor of Arts in Marketing and Media from Murdoch University.
About International Trade Trust Compliance Network
The International TradeTrust Compliance Network (ITTC Network) is a Singapore-headquartered not-for-profit organisation dedicated to advancing global supply chain compliance through collaboration, capability building, and policy leadership.
ITTC Network brings together regulators, industry leaders, institutions, and compliance professionals to strengthen trust, transparency, and professionalism in international trade. Its work spans the full spectrum of trade and supply chain compliance, including customs and trade compliance, export controls, product safety, sustainability, risk governance, and trade remedies such as tariffs and anti-dumping measures.
ITTC Network brings together regulators, industry leaders, institutions, and compliance professionals to strengthen trust, transparency, and professionalism in international trade. Its work spans the full spectrum of trade and supply chain compliance, including customs and trade compliance, export controls, product safety, sustainability, risk governance, and trade remedies such as tariffs and anti-dumping measures.