Geopolitical uncertainty has reached levels not seen since the wars in Afghanistan and Iraq, with the Geopolitical Risk Index spiking as conflicts, tariffs, and policy volatility disrupt global supply chains. Companies are accelerating artificial intelligence deployment in response—71% report faster AI adoption due to current trade tensions—but a troubling disconnect exists between executive expectations and the realities of implementation.
Key Takeaways
- 71% of companies accelerate AI deployment due to geopolitical uncertainty, but executives and technical teams have vastly different implementation expectations
- Only 22% of organizations possess IT infrastructure capable of supporting AI workloads, with legacy systems and fragmented data representing primary obstacles
- C-suite leaders are twice as likely to view AI implementation as easy compared to employees responsible for actual deployment
- Companies with defined AI strategies are 3.5 times more likely to achieve returns, yet only 22% have developed such strategies
- Data infrastructure modernization takes years—not months—requiring phased approaches that balance disruption with capability building
Executives Dream While Technical Teams Struggle
Research from Economist Impact surveying over 800 supply chain leaders reveals a stark confidence gap. Two-thirds of C-suite executives expect financial returns from AI within 12 months, but less than half of junior leaders share this optimism. More significantly, non-executives are twice as likely as their bosses to say AI implementation will be difficult across multiple use cases.
This perception gap isn't theoretical—it reflects fundamental technical barriers that executives systematically underestimate. Less than half of C-suite leaders consider legacy systems a critical challenge to AI implementation, even though their subordinates identify outdated IT infrastructure as the primary obstacle.
The 22% Reality Check
Only 22% of organizations report that their current IT architecture can fully support AI workloads. The remaining 78% face significant data integration challenges that delay or derail AI initiatives regardless of algorithmic sophistication.
Supply chain AI requires real-time access to unified datasets combining internal operations data with external information from suppliers, carriers, geopolitical risk feeds, and climate monitoring systems. Most organizations instead maintain data in fragmented silos—stored across hundreds of formats in incompatible systems that AI models cannot access effectively.
Companies must consolidate various data pipelines, storage systems, and classification schemes before AI delivers value. As firms grow, they typically do the opposite: accumulating data in departmental silos that render it inaccessible to enterprise-wide AI applications.
Implementation Takes Years, Not Months
Industry leaders undertaking data modernization discover the magnitude of this challenge. One major technology manufacturer took 18 months to migrate just 700 internal projects and 35 ERP systems to cloud infrastructure. A financial services company required nine years to close internal data centers and complete cloud migration.
These timelines create dangerous mismatches between executive expectations and operational reality. Senior leaders may approve AI initiatives that technical teams view as premature, leading to costly failures that neither address geopolitical vulnerability nor justify investment.
According to research from Georgia Tech, without proper data quality both within and outside organizations, even the best AI forecasting methods cannot deliver real value. Data normalization across geographies, currencies, transportation modes, and business units represents the essential—but unglamorous—prerequisite for AI success.
The Strategy Gap
Despite widespread enthusiasm for AI, only 22% of businesses have defined AI strategies. Companies with clear strategies are 3.5 times more likely to achieve returns on AI investments, yet most organizations rush into deployment without addressing foundational data quality issues.
Nearly half of respondents cite organizational inertia as a significant barrier to AI. Without defined strategies, clear mandates, and cross-functional coordination, even promising pilots fail to scale beyond initial tests.
The Path Forward
Supply chain leaders must prioritize modernizing data infrastructure before selecting algorithms. This means consolidating enterprise resource planning systems into centralized warehouses, standardizing data formats across business units, and establishing governance frameworks that enable real-time AI access to operational information.
Organizations should begin with measurable, high-impact applications like predictive analytics, where data requirements are clearly defined. Success in these focused areas builds organizational confidence while providing templates for broader deployment.
The geopolitical environment demands supply chain agility that AI can enable—but only after companies address the unsexy work of data engineering that makes intelligent systems possible.
Ready to build AI-ready supply chain infrastructure? Contact Trax to discuss how normalized freight data creates the foundation for predictive intelligence and geopolitical resilience.