Recruiting · Production

NFL cuts average time-to-fill by 24% with Greenhouse AI-powered hiring features

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
High-volume application intake
Trigger
Thousands of monthly applications arrive, creating the need to quickly identify the right candidates.
▾ source quote
“sift through thousands of monthly applications”
2
AI Talent Filtering surfaces candidates
Ai action
AI-powered Talent Filtering helps the team quickly surface qualified candidates using smart, keyword-driven suggestions based on each job description.
▾ source quote
“AI-powered Talent Filtering helps the team quickly surface qualified candidates using smart, keyword-driven suggestions based on each job description tailored to their unique Greenhouse data – making sourcing faster and more intuitive”
3
Talent Rediscovery finds past candidates
Ai action
The NFL taps into its existing database to find strong past candidates, streamlining seasonal rehiring.
▾ source quote
“With Talent Rediscovery, the NFL taps into its existing database to find strong past candidates, streamlining seasonal rehiring and reducing time spent building pipelines from scratch”
4
Automated candidate communication
Output
Automated email templates enable personalized messages at scale, paired with auto-reject rules ensuring at least a two-day buffer before rejections.
▾ source quote
“Automated email templates make it easy to send thoughtful, personalized messages at scale. Paired with auto-reject rules, the team ensures at least a two-day buffer before rejections, promoting a more respectful candidate experience”
5
Centralized scorecard feedback
Human review
Interview scorecards and candidate notes keep feedback centralized and consistent, supporting structured, unbiased evaluations.
▾ source quote
“Interview scorecards and candidate notes keep feedback centralized and consistent. Built-in attributes support structured, unbiased evaluations – helping their hiring team make faster, more confident, data-driven decisions”
Reported 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.

Reported metrics
Average time-to-fill reduction24%
Time-to-fill absolute changefrom 63 days to 48 days
Candidate satisfaction rate improvement67% to 93%
Application sorting timetakes half the time
Reported stack
GreenhouseTalent Rediscovery
◆ Does this fit your context?

Compare to your context

Tell us your scale, team, and constraints. We'll show what changes at your size, what fails at your scale, and whether this case is a fit, needs adaptation, or won't scale to you. Free demo, no signup.

Compare to your context →
~30 seconds · free
Source
https://www.greenhouse.com/customer-stories/the-nfl-dominates-high-volume-hiring-cuts-time-to-fill-by-24-percent-with-greenhouse-ai-powered-features
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 tools did this team use?

Greenhouse, Talent Rediscovery.

What results were reported?

Average time-to-fill reduction: 24%; Time-to-fill absolute change: from 63 days to 48 days; Candidate satisfaction rate improvement: 67% to 93%; Application sorting time: takes half the time (source-reported, not independently verified).

What failed first in this deployment?

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.

How is this recruiting AI workflow structured?

High-volume application intake → AI Talent Filtering surfaces candidates → Talent Rediscovery finds past candidates → Automated candidate communication → Centralized scorecard feedback.

Related recruiting cases

WHAT TO DO WITH THIS

Now compare it to your context

This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.

Browse the Recruiting cases →