A mid-market logistics company whose CIO has been told to modernize the data warehouse before any AI can run should rebuild it one decision at a time, starting with the tables a single decision such as lane pricing needs, and defer the rest until that decision runs on the new data.
Gartner surveyed 140 senior supply chain leaders at organizations with annual revenues of 250 million dollars or more from October to November 2025 and published the results on April 29, 2026; its newsroom blocks automated readers, so the link is to an independent article carrying the wording quoted here, checked against an archive. In it, 56% of chief supply chain officers say integrating AI with legacy systems and processes is a major challenge.
The release gives no industry breakdown, so how far it reaches a carrier or brokerage is not stated. Gartner's advice is to evolve rather than replace the technology stack, building a unified data layer and agentic layer that sits atop legacy systems. A warehouse built decision by decision is that data layer, laid over the transportation management system, the accounting system and the quoting tool the company already runs.
TechTarget, reporting on July 1, 2025 a Gartner analyst's account of the firm's data and analytics trends, described semantic models as metadata management tools that enable organizations to define key metrics and standardize the terms that describe their data, and quoted the analyst's summary, that all roads point to metadata. For lane pricing the terms are few: what a lane is, a pair of three-digit postal zones or of metro areas; what cost includes, carrier or driver pay, the fuel surcharge and accessorial charges; and what on time means, measured against the appointment or the promised date.
At a truckload carrier or brokerage, the pricing manager owns those definitions and the decision. Whoever handles data under the CIO, an engineer or the BI lead alongside other work, loads only the tables the decision reads: every load of the past two years with its lane, equipment, pickup date, quoted price, whether the quote was won, cost as defined and on-time result, drawn from the three source systems. The controller signs the reconciliation of one month of loads to the revenue and purchased transportation cost in the financial statements, and until it ties, the pricing desk keeps quoting from the old reports. Every request for another subject area goes on a list ordered by the decision it serves. No source here prices the three months in dollars or headcount; the one decision and the fixed timeline are the only bound.
In the first month the pricing manager and the CIO choose the decision and write the definitions and list the source fields. In the second that person loads the tables and the controller reconciles the month. In the third the pricing desk quotes from the new tables and the CIO reports quote turnaround and won loads to the CEO against the same month a year earlier; then the next decision is chosen: carrier selection, with its own definitions.
A separate global Gartner survey of 353 data, analytics and AI leaders, taken from November through December 2025 and published April 16, 2026 (linked to a copy dated two days later), found that organizations reporting successful AI initiatives invest up to four times more (as a percentage of revenue) in foundational areas, such as data quality, governance, AI-ready people and change management, than those with poor outcomes, and that only 39% of technology leaders are confident their current AI investments will have a positive impact on financial performance.
