Case Study
FedEx: How AI Cleared the Traffic Jam Before Project Kickoff
A unified intake model detected duplicates, routed requests, drafted briefs, and gave leaders one view of global initiatives.
AI produced the largest leverage before execution began: it improved the quality, routing, and visibility of demand entering the portfolio.
One request could require up to eight hours of coordination, while fragmented forms and tracking created duplicate work and poor leadership visibility.
Projects often lose time before anyone calls them projects. A request arrives by email, another through a form, and a third is apparently living a full life inside a spreadsheet. FedEx's case concentrated on that messy front door. AI inspected submissions, looked for duplicates, routed work, and generated first-draft artifacts so specialists could spend less time translating requests into work.
Asana AI Studio analyzed intake, identified possible duplicates, categorized and routed work, while AI Teammates drafted strategic and technical artifacts.
The metrics are published by Asana, not independently audited here. The operational lesson still travels well: standardize intake before adding intelligence; define routing rules and human escalation; then measure lead time, duplicate demand, administrative effort, and visibility. Automating chaos merely gives chaos excellent response time.