AI in the Chain

Overview

In an increasingly unpredictable trade landscape, supply chain teams need more than spreadsheets—they need an intelligence engine that can forecast risk, find optimal strategies, and drive smarter decisions. AI has become that engine, blending data, analytics, and automation to transform trade compliance from reactive to proactive. This playbook details how to create an AI-powered framework to automate HS code classification, simulate tariffs, and ensure compliance in a rapidly evolving global market.

Step 1: Automate HS Code Classification

Why it matters:
Manual classification is time-consuming and error-prone, leading to fines and shipment delays. AI accelerates classification while improving accuracy.

How to do it:

• Leverage AI tools to translate product descriptions into HS codes.
• Use customs APIs to retrieve applicable duty rates in different regions.
• Integrate this automated workflow into your ERP system.

Prompt example:
“Assign an HS code for ‘wireless noise-cancelling headphones’ and show applicable U.S./EU tariffs.”

Step 2: Simulate Tariff Scenarios

Why it matters:
Tariff regimes shift fast, and static models can’t keep up.

How to do it:

• Collect historic tariff changes and commodity data.
• Build ML models (e.g., XGBoost, LightGBM) to predict the probability and size of future tariff changes.

Example scenario:
“Estimate Q4 2025 tariff impact on smartwatches from Vietnam.”

Step 3: Build a Compliance Alert Engine

Why it matters:
Compliance failures can create huge costs and damage reputation.

How to do it:

• Use AI frameworks to create prompts that scan shipments for red flags.
• Schedule daily checks via workflow automation tools.
• Alerts flag:
  • Missing or mismatched HS codes
  • Countries at risk due to new regulations
  • Expiring trade agreements

Output:
Slack or email alerts to the compliance team.

Step 4: Duty Optimization with AI

Why it matters:
Companies leave millions on the table by not exploring sourcing alternatives.

How to do it:

• Use AI to simulate alternative suppliers or trade routes.
• Build cost models that incorporate total landed costs and tariff impacts.

Example prompt:
“Suggest suppliers for LED lighting with minimal tariffs given current trade policies.”

Step 5: Create a Trade Digital Twin

Why it matters:
Without scenario planning, companies can’t pivot fast enough.

How to do it:

• Build a digital twin of your supply chain.
• Feed it real-time tariff and customs data.
• Model alternative scenarios: e.g., 25% tariff on EU textiles, 10% duty relief in India.

Data Governance and Risk Mitigation

AI is only as good as the data that fuels it:

• Validate supplier data, shipment logs, and customs information.
• Automate cleansing and standardization to avoid data gaps.
• Create governance frameworks to ensure data reliability.

Real-World Examples

• Maersk uses AI to spot suspicious deviations in container routes, preventing smuggling.
• DHL scans invoices for anomalies, catching suspicious jumps that hint at fraud.
• Retailer X rapidly switched 20% of sourcing to Vietnam after a new 25% China tariff—AI made it possible in days, not weeks.

Insights from the Field

A recent report highlighted how trade crime is on the rise as tariffs mount. Smugglers, counterfeiters, and rogue suppliers thrive in uncertainty. AI counteracts this by:

• Tracking shifts in supplier volumes: AI can monitor historical and current volumes, highlighting any sudden changes that might signal illegal or suspicious activity.
• Identifying fraudulent documentation: Using NLP and ML models, AI can parse shipment documents, customs declarations, and invoices for inconsistencies, missing data, or suspicious modifications.
• Flagging suspicious invoice structures: AI can analyze invoice data for patterns that deviate from normal business practices, such as unusually high or low prices, sudden volume spikes, or missing trade identifiers.
• Conducting cross-checks: AI can compare data across suppliers and historical shipments, verifying consistency and authenticity.

Prompts to support validation:

• “Compare historical shipping volumes for Supplier ABC and flag sudden spikes.”
• “Analyze invoice text for missing trade identifiers or discrepancies.”
• “Check supplier documentation for authenticity, focusing on new suppliers added in Q2 2025.”
• “List trade partners that show abnormal invoice patterns since the latest tariff changes.”

This proactive, data-driven approach minimizes human error and boosts confidence in compliance.

Future Outlook: The Next Wave of AI-Driven Compliance

• Predictive Monitoring: AI will scan for subtle patterns that hint at regulatory shifts.
• Automated Negotiation: AI agents will suggest optimal contract clauses tied to real-time tariff data.
• Dynamic Risk Scoring: AI will create live risk scores for every shipment.

Key Prompts to Start

• “Find top 10 suppliers impacted by new EU carbon tariffs.”
• “Generate negotiation clauses for volatile commodity pricing.”
• “Assess tariff exposure for Q4 across electronics categories.”

Conclusion

AI is no longer a luxury—it’s a necessity for trade compliance and risk management. By automating manual tasks, simulating future scenarios, and spotting hidden risks, AI empowers supply chain teams to transform trade volatility into opportunity.

References

• Bloomberg (2025). “Trump’s Global Tariffs Blocked by U.S. Trade Court” – https://www.bloomberg.com/news/articles/2025-05-28/trump-s-global-tariffs-blocked-by-us-trade-court
• World Customs Organization (2025). https://www.wcoomd.org
• Harvard Business Review (2024). “What Sets AI-Driven Companies Apart” – https://hbr.org/2024/11/what-sets-ai-driven-companies-apart
• Trade Crime Is Soaring, U.S. Firms Say, as Trump’s Tariffs Incentivize Fraud Trump’s Tariffs Drive a Rise in Trade Crime – The New York Times