A recent MIT study delivered sobering news for executives betting big on artificial intelligence: 95% of generative AI projects return zero value. Not a single dollar. For supply chain leaders facing pressure to "do something with AI," this statistic reveals a critical truth—most organizations are building AI castles on quicksand foundations.
The Hidden Infrastructure Problem Behind AI Failures
The root cause isn't the AI technology itself. Leading advisory firms like Deloitte and NTT Data consistently emphasize that success with artificial intelligence depends entirely on the quality of underlying data infrastructure. Yet most global enterprises approach AI deployment with fragmented systems that make meaningful results impossible.
Consider a typical scenario: A Fortune 5000 manufacturer operates with separate freight auditors for parcel shipments, European operations, North American over-the-road transport, and regional carriers. Meanwhile, African and Latin American invoices go unaudited entirely. Each auditor sends data in different formats, creating information silos that prevent comprehensive analysis.
When the CFO demands answers about total carrier spend, the response becomes "it's in the data lake somewhere, but we can't extract it." This fragmentation—what one industry expert calls the "Melvin with the red stapler" problem—represents the basement-level infrastructure issue that dooms AI initiatives before they begin. Companies end up with five different versions of reality from five different data sources, making AI-powered freight optimization impossible to achieve reliably.
Why Data Normalization Must Come First
According to supply chain technology leaders, companies typically invest nearly $10 million annually just to solve data normalization challenges across global operations. Most logistics organizations lack both the budget and expertise to tackle this foundational work internally, yet without it, AI applications consistently fail to deliver promised results.
The complexity extends beyond simple data consolidation. Successful AI deployment requires understanding fuel surcharges across every carrier, identifying when accessorial fees apply incorrectly, and recognizing patterns in operational inefficiencies. For example, one manufacturer discovered hundreds of packages shipping to identical addresses within 24-hour periods—an operational problem masquerading as a logistics issue.
Companies achieving AI success in supply chain operations share common characteristics: consolidated data platforms, standardized processes, and normalized information across all regions, currencies, and transportation modes. Without this foundation, even sophisticated machine learning algorithms produce unreliable recommendations.
A Practical Framework for AI Success
Supply chain executives can improve their odds of AI success by following a structured approach that prioritizes data quality over technological sophistication.
Start by identifying pockets of high-quality data within your organization. These may not represent your largest potential savings opportunities, but they provide the foundation for demonstrating AI value. Success with smaller initiatives builds organizational confidence and secures budget for larger transformations.
Next, define specific questions you want AI to answer rather than pursuing a generic "AI strategy." Questions like "What is my total landed cost down to the SKU level?" or "How do my carriers perform across cost, quality, speed, and carbon metrics?" provide clear targets for data infrastructure improvements.
Companies using Trax's Audit Optimizer have discovered that normalized data enables natural language queries that would be impossible with fragmented systems. However, this capability requires extensive upfront work to standardize formats, validate information, and create consistent definitions across all data sources.
Advanced Implementation Strategies
Once foundational data quality is established, supply chain organizations can deploy AI applications that deliver measurable results. Leading companies report success with exception handling automation, carrier performance optimization, and predictive cost modeling—but only after investing heavily in data infrastructure.
The key insight from successful implementations: artificial intelligence cannot create signal from noise. While AI excels at pattern recognition and decision optimization, it requires clean, consistent inputs to function effectively. Companies that attempt to bypass data normalization consistently join the 95% failure statistic.
Future-Proofing Your AI Investment
The supply chain leaders succeeding with AI treat it as essential infrastructure rather than experimental technology. They assign AI initiatives to executive compensation plans, fund employee education programs, and establish clear success metrics tied to business outcomes.
Most importantly, they recognize that AI deployment is a journey requiring patience and systematic execution. Quick wins in high-quality data areas build momentum for tackling complex challenges that deliver transformative results.
Ready to assess your organization's AI readiness? Trax's freight intelligence platform helps companies establish the data foundations necessary for successful AI deployment. Contact our team to discover how normalized freight data can transform cost optimization initiatives while avoiding the pitfalls that doom most AI projects.