On September 3, 2025, C4T brought together senior trade and customs leaders from industries including FMCG, med-tech, fashion, packaging, industrials, food and beverage, and sportswear to discuss one big question: how is AI actually being used in customs beyond classification?
Executive Summary
AI in customs is moving beyond classification. While most organisations are still in early pilots, practical applications are emerging in document intelligence, anomaly detection, contract checks, and scenario planning. The biggest obstacles are fragmented data, audit requirements, and dependence on brokers.
The consensus is clear: AI will not replace customs professionals, but it will augment them. Teams that start small, focus on explainability, and build measurable results will unlock faster clearance, lower costs, and stronger compliance. Over the next five years, AI will become an embedded co-pilot across customs operations, with humans still firmly in control.
A Snapshot of Today’s Operating Reality
AI adoption in customs is still in its early stages of development. Most organisations are experimenting with pilots or proofs of concept that target narrow processes such as document review or anomaly detection. A smaller number are embedding AI into daily decision support and operational monitoring.
The pace of progress depends heavily on data maturity. Companies with integrated ERPs or centralised data lakes can move faster, but others are showing that progress is possible without perfect foundations. Small, well-defined pilots often generate quick wins that justify further investment.
Auditability and governance remain non-negotiable. Customs leaders are clear that AI must operate transparently. Data lineage, model logic, and a strict separation between enterprise systems and public web sources are essential to maintain compliance and protect reputations.
How Can AI Be Used in Customs?
AI is reshaping customs operations by automating repetitive work, improving decision accuracy, and surfacing insights from large datasets. The technology is not about replacing human expertise but supporting it with faster analysis and better information.
Typical uses include:
- Analysing documents, invoices, and permits to extract, verify, and standardise data.
- Detecting anomalies or inconsistencies before they trigger audits or delays.
- Classifying goods and calculating origin or valuation data based on historical inputs.
- Scanning trade agreements, supplier contracts, and Incoterms for compliance risks.
- Predicting potential disruptions or regulatory changes using live data feeds.
- Supporting audit readiness through automated record-keeping and traceability.
As these systems mature, AI is expected to move further upstream, helping customs teams model duty impacts, manage broker performance, and contribute to wider supply chain and ESG strategies.
Key Applications of AI in Customs
Current applications go well beyond HS code suggestions. Organisations are:
- Testing AI for origin calculations, including Inward Processing, and for tracking regulatory updates.
- Applying AI to large datasets from government systems and brokers to detect compliance gaps.
- Piloting document validation for trade preferences and monitoring declaration quality.
- Developing in-house tools for duty optimisation, scenario planning, and litigation support.
- Using AI for document retention and quality checks, flagging red flags that would otherwise go unnoticed.
- Scanning contracts for Incoterms and building predictive trade scenario models.
- Combining data to ingest customs documentation and automate communication workflows.
- Building private LLM interfaces that allow secure, live queries across customs and trade data.
- Exploring AI-assisted audits and end-to-end declaration management as the next phase of automation.
Shared Challenges
Despite the clear potential, adoption remains uneven. Several common barriers stand out:
- Data fragmentation: Multiple ERPs and broker systems make normalisation slow and costly.
- Audit pressure: Authorities demand traceable logic and explanations; opaque AI models are not acceptable.
- Broker dependence: Reliance on intermediaries limits access to full datasets.
- Complex subcomponent classification: Still beyond the reach of many Machine Learning systems.
- Regulatory restrictions: Security and compliance controls can restrict AI deployment.
Where AI Is Delivering Value
The most visible benefits of AI in customs today lie in speed, accuracy, and visibility.
Document intelligence: AI validates supplier documents, extracts invoice data, and standardises information automatically. This reduces manual effort, cuts processing times, and improves data quality.
Exception detection: AI agents flag anomalies before they cause clearance delays or compliance breaches. By identifying issues early, teams can act faster and avoid penalties.
Contract and policy checks: AI scans procurement and sales agreements for key terms like Incoterms or preferential treatment, ensuring consistency and compliance.
Analytics and forecasting: By merging broker and ERP data, companies are building real-time dashboards and predictive tools to support planning, audits, and scenario modelling. Private LLMs enable secure, natural-language access to trade data without relying on public models.
Benefits for Customs
AI is already delivering measurable value across several dimensions:
- Efficiency - Automation of routine processes saves time and reduces dependency on external brokers.
- Accuracy - Standardised data inputs lower the risk of errors and audit failures.
- Compliance - Transparent models and structured data support stronger governance and faster responses to authority queries.
- Cost reduction - Less manual work and fewer third-party fees free up resources for strategic priorities.
- Speed - Faster document validation and declaration processing shorten clearance times.
- Insight - Integrated data enables scenario planning and informed decision-making.
These benefits extend beyond customs itself, supporting broader business goals in supply chain efficiency, risk management, and sustainability.
Making the Case for Investment
The strongest business cases highlight:
- Time and cost savings.
- Reduced manual checks and rework.
- Lower broker fees.
- Stronger audit readiness and reduced reliance on external advisors.
Risk reduction often drives investment decisions, but framing matters. Finance focuses on ROI, IT on integration and stability, and operations on speed and resilience. Small, auditable pilots with measurable results are the best way to build internal credibility and scale adoption.
Relationships With Customs Authorities
AI adoption is happening alongside regulatory digitalisation. Customs authorities across Europe are introducing new digital filing systems, even as paper remains for inspections. Proactive engagement, backed by clean data and documented processes, helps companies gain flexibility and trust when issues arise.
Skills and Teams
AI will not replace customs professionals, it will augment them. Future customs specialists will need to combine regulatory knowledge with data literacy, system understanding, and analytical skills.
However, automation brings an apprenticeship gap. As entry-level administrative work is automated, structured training and guided AI tools will be vital to ensure new professionals still gain practical experience. High-value decisions such as valuation, origin, and complex classification will remain human-led, supported by AI evidence and reasoning.
Looking Ahead: Five Years On
Within five years, AI will become an integral part of the customs stack. Routine document checks, anomaly detection, predictive alerts, and scenario planning will be embedded in daily operations. Broker dependence may decline as companies build internal expertise supported by automation.
Humans will remain central to decision-making. AI will serve as a co-pilot, supporting faster, smarter, and more consistent customs operations, but not as an autopilot. Explainability, governance, and accountability will remain essential.
Final Thoughts
AI will amplify, not replace, customs expertise. Teams that combine operational judgement with data-driven execution will move fastest and create the most value. The path forward is clear: start small, keep it auditable, prove value, and scale with confidence.