A fundamental shift is occurring in how global enterprises approach strategic decision-making. While many teams still struggle with manual invoicing, siloed systems, and months of reconciliation, industry leaders are moving ahead. By consolidating transportation spend into a single source of truth, these organizations are informing board-level strategy with defensible, real-time data. The gap between the two is widening, and the competitive disparity has already arrived.
For decades, supply chain leaders have made decisions without a complete unified view of their data. Not due to lack of effort, but because their systems were never designed to work together. ERP systems, transportation management systems, and procurement platforms often operate independently, forcing teams into manual reconciliation and data normalization. The result is slower decision-making and strategy built on partial information rather than clarity.
In my years as a consultant, the most challenging part of any current-state assessment wasn't analyzing the data. The constraint was data credibility, not analytical capability. Even with advanced models, Fortune 500 engagements required extensive caveating because source data could not be verified. Operating at 80 percent confidence is tolerable until decisions involve hundreds of millions in capital.
The Practical AI Breakthrough
Artificial intelligence has accelerated rapidly, but acceleration alone is not the story. What changed in 2025 was the shift from ambition to execution. Much of the market continues to sell broad AI visions that are not yet capable of consistent delivery, a gap reflected in research showing the majority of generative artificial intelligence initiatives fail to reach production.
At Trax, we take a different approach. We invest in both long-term innovation and practical, production-ready use cases. The moonshots matter, but they are not where value is realized today. Real progress comes from applying artificial intelligence to foundational problems such as data ingestion, normalization, and automated exception resolution. These capabilities are live, measurable, and already in use by our customers.
This is where artificial intelligence moves from promise to impact.
Take data normalization as a concrete example. Carriers submit invoices in dozens of formats. Internal systems capture and classify information differently. Purchase orders follow one structure, general ledgers another. Historically, teams were forced to sit between these systems, manually rekeying and reconciling data. Each touch introduced risk, and those errors compounded as data flowed downstream into audits, reporting, and decision-making.
Modern artificial intelligence changed this by adding context, not just extraction. Traditional OCR could identify fields on a page. Today’s models understand what the data represents, how it relates to other records, and how to translate it into a consistent structure. The result is not conceptual value but operational impact. Data that once required weeks of preparation for quarterly bid events or audits is now available in near real time, with materially higher accuracy.
From Plans to Actuals
The most significant shift of 2025, though, wasn't technological. It was conceptual.
Transportation management systems excel at planning. They tender loads, execute bids, and optimize routes based on known variables. But supply chains don't operate in theory. A typhoon hits Southwest Asia, and suddenly that planned ocean freight from Hong Kong to the Port of Los Angeles becomes an emergency air shipment. Your TMS system isn't designed to systematically capture what actually happened versus what was supposed to happen.
This gap between plan and reality represents the single most prominent blind spot in supply chain decision-making. It's why executives struggle to answer seemingly simple questions: What's my total cost to serve this customer? Where are the key opportunities to optimize my current business? How do I integrate an acquisition into my operations efficiently?
Real progress depends on data rooted in the general ledger, capturing what was actually paid rather than what was planned. This connection to the GL provides a level of financial accuracy that other data sources are unable to match. When an executive green-lights a hundred-million-dollar distribution center, they cannot rely on 'directional' data, they need strong confidence.
The Strategic Elevation
Throughout 2025, freight audit continued its shift from a transactional function to a strategic capability with executive visibility. Not because invoice validation suddenly became more important, but because leaders recognized the data it produces as critical enterprise infrastructure. Freight audit represents the system of record for logistics actuals, capturing what truly occurred across modes, regions, and carriers, not what was planned or assumed
While many providers stop at audit, Trax treats freight audit as a data foundation. By capturing and standardizing every invoice, shipment, and charge across regions and modes, Trax delivers a trusted system of work for transportation actuals that powers higher-order analysis and decision-making.
Can we increase service levels to our highest-value customers without increasing costs? Where are inefficiencies hiding in our network? As we grow through acquisition, how do we operate as efficiently as possible? These aren't tactical queries. They're strategic imperatives that require data executives to trust implicitly.
Looking Forward
The year ahead won't be defined by new technology as much as by new applications of existing capabilities. Natural language query systems will make complex data accessible to stakeholders who aren't data scientists. Integration capabilities will continue breaking down the walls between previously isolated functions. Speed will emerge as the critical competitive advantage—not just in execution, but in decision-making.
The freight audit function itself will continue its transformation. Traditional managed service models, where dozens of people manually review invoices, will increasingly give way to automated systems that capture granular data. The winners won't be those who simply reduce costs, though that remains table stakes. They'll be the organizations that recognize transportation data as a strategic lever, to inform broader organizational strategies.
The competitive advantage now shifts to those who can leverage that data to drive the next phase of growth and execute on the insights it reveals.
About the Author:
Mark Savini is Senior Director, Head of Solutions Consulting and Go-To-Market Lead at Trax Technologies, where he guides enterprise organizations in transforming transportation data into strategic advantage.