In this week’s episode of The JD Fix, I shared how one healthcare organization tackled a challenge that would make most TA teams sweat:
A library of more than 4,000 job descriptions
A team of fewer than 10 people
And the pressure to keep recruiting operations moving while cleaning up the entire JD library
Sound familiar?
The Problem: Inconsistency Everywhere
This team had thousands of live job descriptions across their system. The issues ranged from small annoyances to major blockers:
Inconsistent formatting (some with headers, some without)
Missing sections that left candidates guessing
Subtle bias baked into language choices
Hours of manual work whenever a recruiter needed to update or create a JD
And on top of that, they were working in healthcare—a high-volume, high-pressure space where candidate experience really matters.
The Fix: Simple Templates + Scalable Tech
Here’s the twist: they didn’t overcomplicate it.
Instead of creating dozens of department-specific templates, they went with just two:
A global template
A special projects template
With these, Ongig’s AI-assisted templating engine helped them roll out standardized, candidate-friendly job descriptions. Every posting had:
Clear sections
Readable headers
Applicant-first language
The Results: Hours Saved, Quality Up
Before Ongig, optimizing a single job description could take 30 minutes—or longer with multiple review cycles.
After Ongig? Under 7 minutes.
That’s hundreds of hours saved.
And the numbers prove the quality jump:
Average JD score climbed from 70 → 90+
Gender bias scores improved from 64 → 98.3
20% of jobs with “unmatched content” were automatically flagged, so the team only had to review the ones that mattered
Building a Sustainable System
This wasn’t just a one-time cleanup.
The team built a process that stuck. Anytime a recruiter (or even a hiring manager) tried to customize a posting in Taleo, Ongig automatically kept formatting and sections consistent.
Now, they’re exploring:
Full automation of their JD workflows
Deeper Taleo syncing
AI-generated net-new JDs built directly from templates
And they’re doing it all with fewer than 10 people.
Takeaway
You don’t need a massive TA team to fix your JD library. You need:
The right tools
A simple process
The ability to focus where it matters
This healthcare org proved it. They saved time, improved inclusivity, boosted readability, and set themselves up for long-term success.
If you’re staring down your own mountain of messy job descriptions, take a page out of their playbook.
👉 Want to see how Ongig can help your team do the same? Request a demo
