recruiting · media · workflow
NFL cuts average time-to-fill by 24% with Greenhouse AI-powered hiring features
The NFL's lean recruiting team was overwhelmed by thousands of monthly applications, suffered from slow and manual candidate communication with their former ATS, and faced disjointed internal collaboration that occurred outside official systems.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · High-volume application intake
Thousands of monthly applications arrive, creating the need to quickly identify the right candidates.
Tools used
GreenhouseTalent Rediscovery
Outcome
After implementing Greenhouse, the NFL reduced average time-to-fill by 24%, going from 63 days to 48 days, and improved candidate satisfaction from 67% in Q3 2024 to 93% in Q4 2024.
What failed first
The NFL's former ATS made candidate communication slow and manual, and did not support structured feedback or real-time hiring manager input, making data-driven decisions difficult.
Results
Time saved24%
Volume67% to 93%
Grounding & classification
Source type: vendor customer story
19 fields verified against source quotes, 4 dropped as unverifiable.
enterprise searchrecommendation systemresumefailure mode describedmetric backedproduction runtime claimedtools describedmediacustomer satisfactioncycle time reductionemployee productivityvendor customer storyrecruitinghuman review queueintake to triage