Distribution & Trade AI Intelligence

AX Compass

AI transformation in distribution and trade, grounded in verified evidence.

Distribution Cases Sources

At a glance

Retail AI is becoming a scaled investment market, not a side experiment. Precedence Research estimates the global AI-in-retail market at USD 14.03 billion and projects it to reach about USD 70.95 billion, implying 17.60% CAGR.
Usage is broad, but scaling remains selective. McKinsey reports regular AI use in at least one function at 88 percent, while only about one-third of organizations have begun to scale AI programs.
Korean trading companies see the need before active use has caught up. In the KITA-cited survey, 78.0% said AI adoption was necessary for efficiency, while 16.9% said they used AI to improve productivity or perform work actively.
The value gap makes proof discipline more important than enthusiasm. BCG's survey found Seventy-four percent of companies had yet to show tangible value from AI, and MIT NANDA reported that 95% of enterprise generative AI pilots failed to deliver measurable P&L impact.

16.9%

Korean trading-company employees reporting active productivity or work use of AI [source]

$320 million

Cumulative UPS ORION savings by end-2015 [source]

85%

CJ Logistics ocean-freight arrival prediction accuracy, up from 40% [source]

Why now

AI in retail, distribution, and trade has moved from experimentation into budget-scale infrastructure: the global AI-in-retail market is estimated at USD 14.03 billion and projected to reach about USD 70.95 billion.

Adoption alone is not the benchmark. McKinsey's survey shows 88 percent regular AI use in at least one function, but only about one-third of organizations have begun to scale AI programs; in Korea, the KITA-cited trading-company survey shows a gap between 78.0% need recognition and 16.9% active productivity or work use.

What this site contains

The site organizes twelve case studies across distribution and trade, connecting operating problem, AI intervention, quantified outcome, and applicability for Korean exporters and mid-market operators.

Every cited statistic is tied to a verified claim in `content/sources.json`, and each case separates company-reported results, reputable secondary reporting, and scope caveats before turning the evidence into an exhibit.

Start here

Use the distribution and trade case pages to compare where AI has already changed routing, forecasting, fulfillment, personalization, warehouse work, export targeting, and cargo visibility.

Then read the use-case map, roadmap, and methodology pages to translate those examples into adoption priorities, implementation horizons, and the verification rules behind the evidence.