Making Trade and AI Work Together: Insights from the WTO World Trade Report 2025

Introduction

Artificial intelligence (AI) is no longer a peripheral technology in international commerce; it is becoming the connective tissue that ties together supply chains, services trade, border processes, and market access. The World Trade Report 2025 positions AI as a general-purpose technology with economy-wide spillovers that can amplify trade-led growth—provided governments and firms invest in the foundations that let AI diffuse across borders. The report’s central thesis is clear: if the world keeps markets open for AI-enabling goods and services, strengthens rules for trusted cross-border data, and equips people and institutions with the right skills and infrastructure, AI can lower trade costs, expand exports (especially in digitally deliverable services), and make participation more inclusive for micro, small, and medium-sized enterprises (MSMEs). If countries retreat into fragmentation, the gains concentrate and inequalities widen.

This article synthesizes the report for trade practitioners and policymakers, with a particular focus on customs and border management. It explains how AI reduces operational trade frictions, where the largest growth effects are likely to materialize, what risks must be managed, and which policy choices—domestic and multilateral—will determine whether AI becomes a catalyst for broad-based prosperity or a wedge that deepens divides. Where relevant, the article connects these insights to day-to-day compliance activities—import declarations, export declarations, CDS declarations and ENS declarations—that traders and intermediaries must complete to keep goods moving.

How AI Changes the Trade Cost Equation

Every percentage point shaved from trade costs compounds across logistics, compliance, communications, and finance. AI targets each of these frictions at once:

Logistics and operations. Machine learning models forecast demand, optimize routes and load plans, and anticipate bottlenecks at ports and terminals. Computer vision accelerates tallying and damage checks. Predictive ETA signals allow forwarders, port operators, and haulage companies to orchestrate appointments and reduce idle time—cutting demurrage costs, improving fleet utilization, and smoothing peaks at customs inspection bays.

Regulatory compliance. Natural language processing (NLP) systems parse tariff schedules, preferential rules of origin, and agency-specific measures, then surface eligibility conditions and documentary requirements. Classification models assist with Harmonized System (HS) code selection, while risk engines cross-check declared values, provenance, and licensing data against historical patterns to flag anomalies before submission. For traders filing customs declarations through national platforms (e.g., the UK’s CDS), AI is increasingly embedded in pre-validation, thereby reducing rejections and amendments.

Cross-border communications. Multilingual chat and document translation break down language barriers in sourcing, after-sales support, and dispute resolution. Generative AI copilots summarize long email threads, purchase orders, and contracts; extract key dates; and prompt users for missing information—improving first-time-right rates in import and export declarations and reducing costly back-and-forth with brokers and authorities.

Financial frictions and contract enforcement. AI-driven credit scoring and fraud detection improve trade finance approval times; smart reconciliation tools reduce chargebacks and settlement delays. Combined, these tools shrink the cash conversion cycle, particularly for MSMEs operating on thin margins and tight working capital.

The World Trade Report projects that roughly half of the long-run uplift in global trade comes from these direct cost reductions, with the rest driven by productivity improvements and the rise of AI-enabled services themselves. Digitally deliverable services—consulting, software, cloud, data labeling, design, and more—stand out as the fastest-growing category because they travel over networks rather than physical corridors, yet their delivery relies on open markets for telecoms, compute, and data.

A Customs-First Lens: Where AI Delivers Immediate Value

Customs administrations and border agencies are already deploying AI across three layers: document intelligence, risk management, and assisted decision-making.

Document intelligence. OCR and layout-aware models extract structured data from invoices, packing lists, certificates of origin, SPS/health certificates, and transport documents. Entity matching links products, quantities, weights, Incoterms, and transport modes to declarations. For the UK, these capabilities translate into fewer errors at CDS entry and cleaner datasets for post-clearance audit, reducing amend/reject cycles and enabling faster release. For safety and security filings, AI also validates that ENS declarations conform to message schemas (e.g., element formats, mandatory fields) and catches inconsistencies that would otherwise trigger rejections or holds.

Risk management and targeting. Supervised models trained on historical seizures, under-valuation cases, and misclassification events learn risk signatures—shipment sizes that don’t fit declared product mix, country-pair anomalies, or supplier patterns that correlate with non-compliance. When fused with rules engines (for sanctions, dual-use controls, and embargoes), these models increase detection coverage without blanket inspection. Importantly, they also produce explanations—which features contributed to the risk score—so officers can adjudicate transparently.

Assisted decision-making. Classification copilots suggest HS codes with confidence intervals and retrievals from prior rulings. Rules-of-origin assistants assess preference eligibility under FTAs by mapping bills of materials to product-specific rules (PSRs), then generating checklists of required evidence. Valuation assistants compare declared values to peer shipments and price indices to flag outliers while preserving the declarant’s right to be heard.

For traders and brokers, the near-term benefits are concrete: fewer rekeying errors, faster validation, better predictability, and lower penalties. For administrations, AI helps stretch limited staff across rising volumes without sacrificing border integrity.

The Growth Picture: Where Trade Expands Most

The long-run simulations are strongest in two areas:

Digitally deliverable services. AI amplifies both the supply (more to trade) and the tradability (easier to deliver cross-border) of services such as software, design, data analytics, and remote diagnostics. Because these services often embed into goods (digital twins, embedded analytics), the gains spill into manufacturing exports as well.

Manufacturing value chains. AI-enabled design-for-manufacture, predictive maintenance, and dynamic inventory reduce unit costs and lead times. As firms modularize production and manage complexity better, export declarations rise along longer, more differentiated supply chains. Over time, productivity spillovers lower prices and increase variety, both of which drive additional trade.

Critically, the report emphasizes that the distribution of these gains depends on access: cloud compute, connectivity, energy reliability, skilled talent, and open services markets. Economies that remove frictions in these enablers will capture a larger share of the upside—even if their current industrial base is smaller.

Distributional Implications: Who Gains, Who Risks Being Left Behind?

Firm size. MSMEs stand to benefit disproportionately from AI tools that “productize” expertise—classification, valuation checks, and FTA guidance—that previously required in-house specialists or costly consultancy. Lower fixed costs mean more MSMEs can export, use customs declarations confidently, and claim preferences accurately.

Skill composition. AI complements many medium- and high-skill tasks (audit, analysis, drafting) while automating routine components of those tasks. For customs and compliance roles, this shifts work toward judgment, oversight, and stakeholder engagement rather than data wrangling. Upskilling in data literacy, model oversight, and trade law interpretation becomes essential.

Geography. Without investments in digital infrastructure and energy, the productivity and cost benefits cluster in countries and cities with reliable compute and connectivity. The result could be concentration of AI-intensive activity in a handful of hubs—unless policy counterbalances this through openness to services trade, pro-competition rules, and targeted capacity building.

Market structure. Upstream inputs to AI—semiconductors, servers, networking gear—are capital-intensive and concentrated. Export controls and supply disruptions can reverberate through the AI stack and, by extension, the efficiency of border systems. Diversification of supply, investment in energy-efficient data centers, and participation in plurilateral technology agreements help reduce this vulnerability.

AI, Trade, and Inclusive Growth: Opportunities and Challenges

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Opportunities: Catalyzing Trade-Led Growth

AI and trade can synergize to drive inclusive growth by lowering the fixed and variable costs of participating in cross-border markets. As logistics become more predictable, compliance more automated, and communications more fluid, the export threshold for small firms falls. For developing economies, access to cloud-delivered AI helps leapfrog missing capabilities—classification expertise, legal analysis, forecasting—so that firms can reach new buyers with less up-front investment.

Reducing Trade Costs

Model-based planning, ETA prediction, and dynamic appointment systems reduce warehousing and transport costs. NLP-powered copilots compress the time spent on tariff lookups, preference rules, and documentary checklists. Multilingual tools cut vendor management and claims handling time. Together, these gains translate into fewer border delays and lower total landed cost—especially salient for CDS declarations in the UK and for safety and security ENS declarations across the EU/UK where structured, schema-compliant data is crucial.

AI Use in Customs

Administrations deploy AI for HS code assistance, anomaly detection in declarations, targeting and selectivity, and chatbot-style query handling for traders. The value is largest when models are paired with human-in-the-loop review and audit trails. On the trade side, brokers and compliance teams integrate document AI into pre-arrival filing, ensuring consistency between invoice narratives, packing lists, and declaration data. As adoption spreads, both sides share cleaner data earlier in the process, which supports pre-clearance and trusted-trader programs.

Expanding Global Trade

Lower frictions and higher productivity compound over time. As AI-intensive services rise, they pull related goods trade (devices, sensors, networking) and embedded services with them. Industries with high knowledge content (medical devices, electronics, machinery) see the biggest boost, because AI helps manage complexity and customization at scale.

Export Growth by Income Level

High-income economies lead in early gains due to existing infrastructure and skills, but targeted catch-up—investment in broadband and grid reliability, openness to computer/telecoms services, and skills programs—lets middle- and low-income economies accelerate. Where administrations digitize border processes, align with WCO data models, and allow trusted cross-border data flows with safeguards, MSMEs’ share of exports tends to grow.

Practical Playbook for Traders and Intermediaries

1) Build an “AI-ready” declaration stack.
Consolidate master data (product catalogs, HS mappings, supplier details, licensing flags) into a single source of truth. Adopt document AI to pre-extract line items from invoices and packing lists. Use classification copilots with confidence scores and trigger human review below a threshold. For the UK, validate schema compliance with CDS before submission to reduce amend/reject cycles. For movements requiring pre-arrival safety filings, set up automated quality checks for ENS declarations.

2) Shift compliance left.
Run tariff, sanctions, dual-use, and preference screens at quotation and purchase order stages, not just at entry. Generate a living evidence pack for rules of origin (supplier declarations, PSR mapping, transformation records) as part of order execution, so preferential claims are defensible if audited.

3) Instrument your processes.
Capture metadata for every step—classification rationale, valuation benchmarks, origin rule checks, exception outcomes. Feed this back into models to improve performance over time. The outcome is a measurable reduction in penalties and a higher first-time-clearance rate.

4) Use copilots as training wheels for new staff.
Generative assistants can propose HS candidates, draft responses to customs queries, and summarize regulatory updates. Pair this with a governance framework: role-based access, prompt templates, and required human approvals on sensitive decisions.

5) Align with trusted programs.
AI-supported internal controls—document consistency checks, automated reconciliation, and anomaly alerts—strengthen the case for Authorized Economic Operator (AEO) status and other trusted-trader schemes that confer fewer inspections and faster lanes.

Policy Priorities: Turning Potential Into Broad-Based Gains

Keep markets open for AI-enabling goods and services. Lowering tariffs and non-tariff barriers on servers, networking gear, and semiconductors reduces the cost of digital infrastructure. Liberalizing computer, telecoms, and data processing services—as long as privacy and security safeguards are credible—improves access to cloud AI for firms in all economies.

Renew and clarify digital trade disciplines. Predictable rules for cross-border data flows, e-transmissions, and source code disclosure reduce uncertainty. Interoperability among privacy regimes—via adequacy decisions, standard contractual clauses, or trusted data frameworks—lets firms operate multi-jurisdictional supply chains without duplicative compliance.

Invest in energy and connectivity at the border. AI-enabled customs depends on reliable power and network links. Border posts and ports need contingencies (microgrids, renewable integration) and hardened connectivity to keep single windows, risk engines, and data exchanges available during peaks.

Build skills and institutional capacity. Upskill officers and brokers in data literacy, model oversight, and the legal contours of algorithmic assistance. Develop model governance (validation, drift monitoring, red-team testing) and maintain clear rights of review and appeal for traders.

Encourage competition and open ecosystems. Guard against vendor lock-in by adopting interoperable data standards (WCO Data Model, UN/CEFACT), open APIs, and procurement that rewards explainability and portability. Support open-source components where feasible to broaden access and spur local innovation.

Governance, Trust, and Due Process

AI at the border touches sensitive decisions—classification, value determination, admissibility—that affect legal obligations and commercial outcomes. Trust requires:

Transparency and explainability. Systems should surface the features that drove a risk flag or a classification suggestion, alongside links to legal texts and prior rulings. Explanations must be comprehensible to non-technical users.

Human oversight and contestability. Ensure that officers can override model outputs with reasoned justifications and that traders can challenge decisions through established channels.

Data protection by design. Limit personal data retention, segment training data from live decision logs, and audit third-party models for privacy, bias, and leakage risks.

Robust testing and auditing. Validate models on local data, stress-test against adversarial inputs (e.g., manipulated invoices), and monitor real-world outcomes for drift. Publish model cards that document purpose, limitations, and performance metrics.

What This Means for the UK Trade Community

For the UK specifically, the shift from CHIEF to CDS cemented a modern, API-driven backbone for customs declarations. That opens a broad surface for AI-assisted pre-validation, classification, and risk checks before submissions hit the platform. Traders who systematize data quality upstream will see fewer query letters, less rework, and faster release. Those who also automate eligibility checks for preferences and keep living evidence packs will improve their success rate in claiming duty reliefs—while staying audit-ready.

If your business files import declarations, export declarations, CDS declarations, or ENS declarations regularly, the path is pragmatic:

1. Connect your ERP/OMS to a document-intelligence layer that extracts and reconciles line-level details. Customs Declaration UK platform provides that capability.
2. Implement HS and origin copilots with thresholds for human review.
3. Automate schema and business-rule checks prior to CDS/ENS transmission.
4. Close the loop by capturing reasons for any customs queries and feeding them back into your models and SOPs.

Where to Start: Resources and Next Steps

• For practical guidance on UK filings and to streamline your customs declaration workflows end-to-end, explore Customs Declarations UK (CDUK)— a modern, self-service platform that supports import and export submissions with built-in validations and templates. It’s a straightforward way to scale filings while you add AI assistance over time.
• If your business is scaling exports and wants to minimize amendments and penalties, review best practices for import declarations and export declarations and consider pilot-testing AI document checks on a subset of lanes before rolling out across all products.

Conclusion

The World Trade Report 2025 offers a pragmatic roadmap: AI is already reducing operational frictions and, if supported by open markets and sound governance, can unlock sizable gains in trade and real incomes. The customs domain sits at the heart of this transition, turning messy documentation and fragmented processes into structured, machine-verifiable flows. Done well, AI makes border processes faster, more predictable, and fairer—expanding participation for MSMEs and emerging economies. Done poorly, AI concentrates advantages and hardens divides.

The strategic choice is ours. Keep channels open for AI-enabling goods and services. Invest in energy, connectivity, and skills at border nodes. Embed explainability and due process in every model that touches a legal decision. Align data formats so public and private systems interoperate. If we do, import declarations, export declarations, CDS declarations, and ENS declarations become less of a compliance tax and more of a streamlined, data-driven handshake that lets goods and services flow with confidence. That is what it looks like when trade and AI truly work together—to the benefit of all.

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