The United States’ sweeping tariffs in April 2025 have triggered far-reaching disruptions across global supply chains. Industries from technology to pharmaceuticals now face increased risks, higher costs, regulatory complexity, and logistical bottlenecks. In this turbulent environment, artificial intelligence (AI) has become a tool for adaptation, yet its limitations are also exposed.
While AI can support supply chain and logistics managers to find optimal solutions, the technology cannot anticipate black swan events, like executive orders, abrupt labor strikes, and geopolitical escalations, such as the Red Sea shipping disruptions, which furthermore create complex ripple effects throughout global supply chains that AI models also cannot predict or resolve independently. In these situations, human judgment, agility, and creativity become the decisive factors in ensuring supply chain resilience, as people are uniquely equipped to interpret unfolding events, devise novel solutions, and adapt strategies in real time.
AI’s Expanding Role in Supply Chain Management
Many think AI is central to managing the volatility of protectionist trade policies. Advanced platforms enable companies to model new tariffs’ impact across their supply networks in real time, reroute sourcing, and optimize logistics. When the U.S. imposed a 50% tariff on Chinese electronics, some businesses shifted sourcing to Southeast Asia and Mexico within days, guided by AI-powered network analysis and risk assessments.
In the pharmaceutical sector, where tariffs threaten access to key ingredients from China and India, AI-driven scenario planning is helping drugmakers anticipate supply risks and adjust procurement before shortages develop. Logistics companies leverage AI to parse thousands of invoices and contracts, flag tariff-exposed components, and recommend alternative sourcing or pricing strategies. Small and medium-sized enterprises are also gaining access to affordable cloud-based AI platforms for scenario modeling and inventory optimization.
Many real-life case studies confirm the above and the possibilities and pitfalls of AI-driven tools and solutions to manage supply chain and logistics volatility and uncertainty.
Case Study 1: India’s Sudden Rice Export Ban (2024)
In July 2024, India, the world’s largest rice exporter and consistently leading global rice exports by a significant margin in both quantity and value, abruptly banned exports of non-basmati white rice to curb domestic food inflation. This overnight policy shattered global rice supply chains, particularly in Africa, the Middle East, and Southeast Asia. AI systems running commodity sourcing forecasts had assumed continuous export flows, optimizing for weather-driven disruptions rather than government interference.
As a result, food companies were unprepared for the shortages and price spikes. Procurement teams had to scramble for alternative suppliers in Vietnam, Thailand, and Pakistan, renegotiate contracts, and absorb or pass on massive cost increases. This situation underscored that political pressure, not just weather or demand, can upend supply chains, exposing AI’s blind spot to the human dimension of global trade. Governmental actions taken during the pandemic have also proven this point.
Case Study 2: The De Minimis Exemption Elimination (2025)
The elimination of the de minimis exemption, a provision that allowed low-value imports under $800 to bypass tariffs, exposed a stark limitation of AI. When the U.S. government ended this exemption, direct-to-consumer brands faced sudden, unexpected tariff increases on millions of small shipments. AI models, trained on years of historical tariff data and policy trends, failed to foresee this overnight regulatory shift, leaving companies exposed to new costs and administrative challenges. It was not AI systems, but human supply chain leaders and legal experts who interpreted the policy changes, reassessed sourcing strategies, renegotiated logistics contracts, and adapted operations in real time. This episode underscored that AI excels at optimizing within known parameters but cannot anticipate or manage the full scope of regulatory and geopolitical shocks.
Case Study 3: Baltic Sea Subsea Cable Cuts (2025)
In 2025, several critical undersea communication and power cables in the Baltic Sea were damaged in suspected sabotage. The severed cables disrupted real-time tracking systems, Internet-of-things (IoT) visibility, and cross-border logistics coordination across Northern Europe. AI models dependent on uninterrupted data flows for container routing and shipment tracking began failing as location updates stalled and IoT devices went offline. Automated customs clearance systems, reliant on real-time data, ground to a halt in affected regions. Human intervention became essential: teams rapidly deployed satellite-based communication fallbacks, manually tracked high-priority shipments, and reprioritized urgent cargo. This incident highlighted AI’s dependence on infrastructure, vulnerable to geopolitical disruptions, a blind spot that only human ingenuity could address.
The Human-AI Partnership
Despite AI’s advances, its effectiveness depends on the quality and integration of underlying data, a persistent challenge in fragmented or siloed organizations and across the supply chain and logistics industry. Even the most advanced digital twins and scenario planners are only as good as the data and assumptions they receive. The companies best positioned to navigate today’s volatility are those combining AI’s analytical power with classical optimization techniques and human judgment. Explainable AI frameworks and sustainability scoring are becoming standard features in leading platforms, helping companies assess suppliers for both cost and ESG (environmental, social, and governance) risks. These hybrid approaches deliver measurable results, including reduced customs delays, improved inventory placement, and billions in avoided tariff costs.
Turning Real-World Risk into Actionable Intelligence
A new generation of AI-powered platforms is emerging to address these challenges head-on. Roambee’s Global Trade Lane Risk Planning Platform exemplifies this evolution. Unlike static models that rely on historical or simulated data, Roambee’s solution draws on over a decade of firsthand geospatial intelligence from millions of shipments across air, ocean, rail, and road. The platform generates Lane Risk Scores based on actual in-transit performance, factoring in temperature excursions, customs delays, theft-prone corridors, and excessive dwell times, to provide a single source of truth for operational risk assessment.
These Lane Risk Scores are tailored to industry-specific vulnerabilities:
- Pharmaceuticals: Measured by environmental compliance and security during dwell times.
- Electronics and high-value goods: Focused on loss hotspots and hand-off points to mitigate pilferage and tampering.
- Perishables and food & beverage: Prioritize cold chain compliance and on-time delivery to preserve shelf life.
- Automotive and industrial machinery: Assess risks that could disrupt just-in-time delivery and halt production lines.
With these insights, supply chain leaders can plan alternate lanes by cost or duty impact and real-world risk. They can select carriers and forwarders with proven reliability, justify premium lanes or additional safeguards in high-risk zones, and proactively avoid disruptions before they affect operations or revenue. As Roambee’s CEO Sanjay Sharma notes, “You can’t optimize what you can’t measure—and in today’s world, you also can’t afford to move without knowing the risk”.
Conclusion: Intelligent Fusion for a New Era of Trade
The 2025 U.S. tariff wave has exposed AI’s strengths and limits in global supply chain management. AI-driven tools empower businesses to analyze new risks, reroute sourcing, and automate compliance quickly and efficiently. Yet, AI cannot anticipate every geopolitical shock nor overcome persistent challenges in data integration and ethical oversight.
As protectionist policies reshape the global trade landscape, the future of supply chain management will depend on the intelligent fusion of AI capabilities with classical optimization and strategic human judgment. Companies and countries that invest in technology, robust data integration, and ethical frameworks, while maintaining the irreplaceable value of human expertise, will be best positioned to thrive in this era of heightened tariffs and uncertainty.
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