On the surface, job descriptions seem harmless. Write a few paragraphs about responsibilities, qualifications, and expectations, and move to the next task on your to-do list.
But under the hood, job descriptions influence far more than candidate interest.
How they’re written, maintained, and governed determines:
That’s why regulators, auditors, and plaintiffs’ attorneys don’t ask what you meant to write. They ask what your job descriptions say—and whether those definitions are applied consistently.
What’s more, when those definitions aren’t consistent across teams and locations, it usually means your job data isn’t aligned, or worse, sitting in different “FINAL” versions across various desktops.
Not confident. Not defendable.
I’m here to tell you that there is another way to maintain compliance that doesn’t involve rigorous manual labor, one-off projects, or point-and-shoot efforts. And it starts with managing your job data.
Most HR leaders understand the goal: avoid discrimination, comply with employment laws, and hire equitably.
But despite their reach, most companies still see job descriptions as loosely managed documents that are edited by committee, copied across teams, and updated reactively.
I’m sure you see the gap: In practice, compliance is evaluated through documentation, consistency, and outcomes.
In other words, if you aren’t practicing job description management and governing your job information systematically, you are setting yourself up for a compliance nightmare.
That’s because inconsistent or poorly structured job descriptions create:
And intent doesn’t matter much here. You can have the best DEI goals in the world and still end up with inequitable outcomes if job content is inconsistent across teams, geographies, or time.
Long story short, if job descriptions vary materially for the same role family or level, or if they change without lineage or approval, it becomes difficult to demonstrate that hiring and pay decisions were job-related and applied fairly.
JDX+ ensures your job templates are anchored to your architecture, so all requirements and decisions are consistent and fair.
Most HR and TA leaders aren’t trying to cut corners. Job descriptions evolve because businesses evolve (think new priorities, managers, regions, and laws). The problem is that, over time, those reasonable changes accumulate into inconsistency.
And inconsistency is exactly what compliance frameworks, auditors, and attorneys are trained to spot.
Job descriptions frequently become “Exhibit A” in:
Investigators and attorneys look for patterns:
Fair, compliant, and effective hiring therefore depends on whether job descriptions are treated as governed job information—structured, consistent, and defensible across systems, cycles, and jurisdictions.
In my experience, there are a few common reasons job descriptions aren’t compliant. Notably, AI has entered the arena, and as this technology continues to grow (and grow), HR teams need to be especially careful with how they’re using it.
Degree requirements, years of experience, or preferred skills are often added by habit rather than necessity. Over time, these inflate expectations, narrow candidate pools, and increase disparate impact risk...all without improving performance.
The U.S. Equal Employment Opportunity Commission (EEOC) makes clear that employment decisions must be job-related and consistent with business necessity, and documentation is a core part of how that standard is evaluated.
When organizations cannot show consistent, job-related requirements across similar roles, they weaken their ability to defend hiring and pay decisions—regardless of intent.
Note: Misaligned expectations also undermine internal mobility, as incumbents may not qualify on paper for roles they could perform.
Define what is truly job-related:
At JDX, our job template has “What You’ll Do,” What You’ll Have,” and “As a Bonus, You’ll Have” sections.
Under disability accommodation laws, employers must ensure their job descriptions are ADA compliant by distinguishing between essential and non-essential functions.
Job descriptions that are generic, overly broad, or describe the person they want to hire vs. desired outcomes of the role make this distinction harder and weakens your ability to defend decisions later.
Include:
JDX+ gives each job a “confidence score” to alert you that essential information is missing or misaligned, helping you further complete job descriptions and maintain compliance.
Pay transparency laws raise the bar further by turning job descriptions and postings into regulated artifacts.
Many state statutes require “good faith” salary ranges tied to the actual job, not aspirational titles or loosely defined scopes. (Washington State’s law is a clear example, explicitly linking posting requirements to job-specific pay ranges and benefits disclosures.)
When job descriptions use senior-sounding titles for mid-level roles—or describe materially different scopes for the same level—you create downstream problems:
This is especially risky in jurisdictions where pay ranges must be job-specific and defensible.
Adhere job description templates to job architecture and compensation bands. Don’t let titles and scopes shift without a review.
JDX+ ensures job architecture decisions enter an approval process, with all decisions user-stamped, time-stamped, and audit-ready.
AI has made job description drafting faster, but speed without controls introduces new risk.
Ungoverned AI can:
Used correctly, AI can be a powerful accelerator.
JDX COO AJ Naddell says one issue isn’t AI itself, but that “AI amplifies and magnifies bad data and, then, bad results. It becomes ‘garbage in, garbage out.’”
The second issue comes down to governance. AI should operate within your job architecture, templates, and approval workflows, not around them. It should assist humans, not bypass the systems that keep job information fair and defensible.
In other words, make sure you have:
Note: This is the posture behind JDX+: AI-assisted job description creation that is architecture-aware, policy-bound, and audit-ready.
If the goal is fairness and compliance, not just better copy, job descriptions need to be managed like enterprise data.
HR teams always start with good intentions: review job descriptions for biased language, remove exclusionary terms, and encourage more inclusive phrasing.
That work is necessary. It’s also insufficient on its own.
Here’s why one-off fixes don’t scale:
Language absolutely matters. But without structure, approvals, and version control, even the best wording degrades over time. Bias re-enters not because people are careless, but because the system allows it.
To support fair and compliant hiring at scale, job descriptions need to be managed as enterprise job data, with controls comparable to other critical HR information.
That includes:
Importantly, progress on fairness and inclusion becomes repeatable that’s not dependent on heroic manual effort or lost when a new manager copies an old template.
Create a coherent job architecture in JDX+ with workflows, versioning, and approvals that ensure bands, mobility, and defensibility.
It’s tempting to treat job descriptions as a writing exercise. Tweak the language. Update a template. Add a checklist.
But fair, compliant, and effective hiring requires something more durable: governed job information that holds up across hiring cycles, compensation reviews, audits, and AI-assisted workflows.
Language is part of the solution. Structure is the foundation.
And when job descriptions are treated as the strategic assets they are, hiring doesn’t just feel better. It actually gets better.