The State of Job & Skills Data Governance 2026

The High-Coverage Paradox

When we launched this survey, we expected to find skills data governance gaps. What we didn't expect was the paradox at the heart of enterprise skills strategies.

Organizations that have achieved 75%+ skills inventory coverage—the supposed "winners" in the skills race—are the most likely to say their current approach is failing. 92.6% are planning to completely rebuild how they govern skills and  job data in the next 12–18 months.

Think about that. These aren't laggards. These are organizations that invested early, pushed through the tedious work of cataloging skills, and built inventories that most enterprises can only aspire to. Yet they're dissatisfied enough to start over.

The reason reveals the central tension in enterprise skills data: you can achieve coverage through heroic, manual effort but you can't sustain it without governance.

The State of Job & Skills Data Governance 2026, based on 227 HR & Talent leaders (76% senior management or executives) at U.S. organizations with 1,000–10,000 employees, exposes the gap between coverage achievement and governance maturity—and why it matters for equity, mobility, defensible compensation, and ultimately, enterprise transformation.

The bottom line: Coverage gets you the data. Governance gets you the ROI.

Section 1: Where Enterprises Stand Today

Coverage Reality: The Baseline Numbers

When we asked, "What percentage of roles have an up-to-date skills inventory (updated in the last 12 months)?", the results confirmed what industry experts suspected: comprehensive skills visibility is rare.

Coverage Distribution (n=227):

• <25%: 2.6% (6 orgs) — minimal coverage
• 25–49%: 26.0% (59 orgs) — early stage
• 50–74%: 47.1% (107 orgs) — the plurality (typical enterprise)
• 75–89%: 20.7% (54 orgs) — high coverage
• ≥90%: 3.1% (7 orgs) — near-universal coverage
• Don't know: 0.4% (1 orgs)

Key insight: Only 24% have high coverage (≥75%) and just 3.1% approach universal coverage. The typical enterprise sits at 50–74% coverage—enough to have invested significantly, but not enough to feel complete.

This aligns with external research: Mercer found only 42% of companies feel their job frameworks meet current needs. Our data suggests enterprises are mid-journey (past the pilot stage but far from the finish line).

The Identification Problem

According to Paul Smith, JDX's Solutions Consultant and an industry practitioner with deep skills architecture experience:

"It's not necessarily that you can't put a bunch of skills in a database. It's what skills do we need? The identification part's the hard part. You can only find those by identifying essential functions in a job. That's why you need a governed job database first—that's step one."

This explains a critical insight: many organizations built skills inventories by starting with the people (what skills do employees have?) rather than the jobs (what skills do roles require?). Without governed job data as the foundation, skills inventories become disconnected from actual workforce needs.

Business implication: Without a governed, enterprise-wide skills repository, HR cannot confidently identify internal skills gaps or mobilize talent, undermining both mobility and equity initiatives. It's hard to ensure fair opportunity when you don't know which skills exist or which ones actually matter.

 

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Operating Model: Projects vs. Programs

When we asked, "How do you currently manage workforce skills data?", the results validated a core hypothesis: most organizations treat skills as projects, not programs.

Current Operating Model (n=227):

• Ongoing governed program: 31.7% (72 orgs)
• Primarily project-based: 30.0% (68 orgs)
• Decentralized (each function manages independently): 21.6% (49 orgs)
• We do not actively manage skills data today: 16.7% (38 orgs)

The scaling wall: 51.6% manage skills data in ways that don't scale—project sprints, functional silos, or no management at all. Only about one-third have the continuous governance required to maintain skills data over time.

This is the process debt at the heart of the High-Coverage Paradox. Organizations achieved coverage through one-time projects, consultant engagements, and cross-functional task forces. They got the rows filled in. But without ongoing ownership, approvals and reconciliation, that coverage can't be sustained.

What "Governed Program" Means (and Why It Matters)

We asked which specific governance practices exist for job/skills data. The results show that even among those claiming "governed programs," practices are often incomplete:

Governance Practices in Place (multi-select, n=227):

• Clear ownership of job/skills content: 59.9% (136 orgs)
• Formal approval workflows: 55.9% (127 orgs)
• Version history / lineage tracking: 53.3% (121 orgs)
• Synchronized systems (HRIS ↔ ATS ↔ Comp ↔ LMS): 39.2% (89 orgs)

The governance gap: While virtually every organization has some governance element, comprehensive governance is rare:

• 40.1% lack clear ownership (no one accountable end-to-end)
• 44.1% lack formal approvals (changes can happen "off-system")
• 46.7% lack version history (can't answer "who changed what, when, why?")
• 60.8% lack synchronized systems (manual updates, disconnected platforms)

What this means: Partial governance is common; comprehensive governance is rare. The result? Skills data isn't fully trustworthy or usable across its lifecycle, directly threatening defensibility (explaining pay differences) and efficiency (manual reconciliation work).

 

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Section 2: The High-Coverage Paradox—Why Having the Data Isn't Enough

This is where the survey revealed its most compelling insight.

Of the 54 respondents (23.7% of sample) who report 75–89% or ≥90% skills inventory coverage, 92.6% are planning major governance changes in the next 12–18 months.

Let that sink in. The organizations that "won" the coverage race—that invested years and significant resources building skills inventories most enterprises can only aspire to—are overwhelmingly dissatisfied with their current state.

The Numbers Behind the Paradox

High-coverage cohort profile (n=54):

Current state:

• 37.0% have ongoing governed programs (only slightly higher than overall average)
• 27.8% operate project-based
• 24.1% operate decentralized
• 11.1% don't actively manage at all

Future intent (next 12–18 months):

• 57.4% plan "significant improvement" → move to fully governed programs with approvals, lineage, and synchronized systems
• 35.2% plan "moderate improvement" → implement systems to store, standardize, and approve data
• Only 7.4% plan minimal/no change

The tension: They achieved coverage but now realize they need control. Having 75%+ rows filled in isn't the same as having a trustworthy, governable, actionable skills dataset.

Why Coverage ≠ Governance: Five Critical Gaps

High-coverage organizations signal five critical gaps that explain why they're rebuilding:

1. Process Debt: Heroic Effort Doesn't Scale

They achieved 75%+ coverage through unsustainable methods:

• Project-based sprints (one-time lifts, not repeatable)
• Decentralized teams (every function reinventing the wheel)
• Spreadsheet heroics (manual catalog maintenance)
• The data:
• 27.8% of high-coverage orgs are project-based
• 24.1% are decentralized
• 11.1% admit they don't actively manage

Translation: Coverage came first. Now they need governance before it collapses under its own weight.

2. Quality Concerns: Volume ≠ Trust

Even with high coverage, confidence lags:

• Only 61.1% of high-coverage orgs are "very confident" in their data
• 39.9% are moderately confident or less

Translation: They have the rows filled in but they know the data is fragmented, inconsistent, out-of-date, or siloed. Volume doesn't equal trust.

3. System Fragmentation: Data Exists But Isn't Synchronized

Despite high coverage, integration barriers persist:

• 18.5% cite tech/HRIS limitations as top barrier
• 18.5% cite taxonomy mismatches across systems
• 16.7% lack approvals/lineage processes

Translation: The data exists in multiple places but it's not synchronized, standardized, or trustworthy across systems. Every downstream use requires manual reconciliation.

4. Missing Control Mechanisms: No Guardrails

Even with the content, they lack governance controls:

• 33.3% still lack clear ownership
• 17.0% lack formal approval workflows
• 40.7% have no version history or lineage tracking

Translation: Anyone can change the data. No one knows who's accountable. There's no audit trail. This isn't governance, it’s chaos at scale.

5. AI as Forcing Function: New Requirements Raise the Bar

New capabilities (genAI, automation, skills-based talent strategies) require a governed and trustworthy foundation, not just a spreadsheet with 75% fill rate.

Here's where Smith's perspective gets interesting:

"Think about HR departments across all industries. You're no longer just expected to be a good recruiter. You're expected to understand how agents operate with skills data, what outputs mean, and teach that agent your industry jargon. Roles that survive are expanding in skills needed because of AI."

His perspective: AI is restructuring the workforce—displacing some roles while expanding skill requirements for those that remain. Major tech layoffs at Salesforce, Amazon, and others demonstrate AI's displacement impact. But for surviving roles, requirements are expanding. HR needs prompt engineering. Finance needs AI oversight. Every function needs to understand how to work with agents.

Translation: The bar just got raised. Ad-hoc skills data worked for manual HR processes, but it won't for AI-powered workforce planning, automated talent matching, or predictive skill gap analysis.

"You Have the Data. Now What?"

This paradox validates a critical market insight: most organizations built coverage the hard way. Now they need governance before that coverage becomes technical debt.

The path forward follows the Organize → Standardize → Strategize framework:

1. Organize (where high-coverage organizations are now): They achieved coverage: organized the skills into some inventory, whether via project, spreadsheet, or decentralized effort.
2. Standardize (where they need to go): Now they need the governance layer—clear ownership, formal approval workflows, version history/lineage, synchronized systems across HRIS, ATS, Comp, and LMS.
3. Strategize (the payoff): Only with governance can they confidently use skills data for workforce planning, enterprise transformation, internal mobility, and compensation equity.

As Smith explains: "You have to have a governed database to develop job data points. That's step one." High-coverage organizations skipped that step, and now they're going back to build it.

 

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Section 3: The Governance Gap—Practices, Barriers, Confidence

What's Preventing Better Governance?

We asked: "What is the single biggest barrier preventing stronger governance of job/skills data in your organization?"

Top Barriers (n=227):

1. Lack of clear ownership/accountability: 19.4% (44 orgs)
2. Fragmented systems/integrations: 18.5% (42 orgs)
3. Budget or resource constraints: 18.1% (41 orgs)
4. HRIS/tech stack limitations: 15.9% (36 orgs)
5. Taxonomy mismatch across systems: 12.8% (29 orgs)
6. Missing approvals/lineage processes: 13.2% (30 orgs)
7. Lack of executive sponsorship: 1.8% (4 orgs) ← notably low
8. Other (managers gaming the system): 0.4% (1 org)

Key insights:

Ownership is the #1 Blocker

Nearly 1 in 5 cite lack of clear ownership as their biggest hurdle. In many companies, responsibility for job/skills data is diffuse, falling between HR, IT, Total Rewards, and business units with no single owner.

Impact: Without ownership, decisions about job architecture or skills taxonomy linger or get overturned. No one stewarding data quality means issues like outdated job descriptions or mis-leveled roles don't get fixed, leading to ad-hoc exceptions that are hard to defend.

Fragmentation is Nearly as Big

18.5% struggle with HR tech stacks that don't talk to each other—separate systems for recruiting, performance, compensation, and learning, each with their own job/skill data.

Impact: Even if you govern data in one system, it doesn't flow to others, resulting in inconsistent definitions. HRIS and ATS job data mismatches can cause hiring delays. It's hard to measure equity or mobility metrics enterprise-wide when data is fragmented.

Executive Support is NOT the Problem

Only 1.8% cite lack of executive sponsorship. This is encouraging: it suggests leadership buy-in is generally present. The blockers are operational (ownership, systems, budget), not strategic.

Implication: HR can likely secure support for governance initiatives. The challenge is execution, not C-suite will.

The Taxonomy Problem

Smith's practitioner insight adds critical context here:

"Many current skills databases are literally just a list of skills. Workday's skills cloud suggests skills based on job data, but has no taxonomy—it's helpful if you don't know where to start, but not valuable without hierarchical structure."

Why this matters: A skill at Manager 1 is different from the same skill at IC Level 3—seniority context affects compensation, training design and mobility decisions. Without taxonomy, skills data remains a flat list that's hard to operationalize for actual programs.

 

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The Confidence Problem: Trust Lags Coverage

We asked: "How confident are you in the accuracy and consistency of your job and skills data across systems?"

Confidence Levels (n=227):

• Fully confident (5): 9.3% (21 orgs)
• Very confident (4): 41.0% (93 orgs)
• Moderately confident (3): 28.2% (64 orgs)
• Slightly confident (2): 13.2% (30 orgs)
• Not confident (1): 1.8% (4 orgs)

The 50/50 split: Only 50.2% are highly confident (4–5 rating). The other half? They have reservations.

What this means: Even organizations that claim "typical visibility" (47.1% said they have it) don't fully trust their data. They know job titles in the ATS might not match those in the compensation system or that skills inventories are aging faster than they can update them.

Auditability risk: If asked to defend a promotion or pay decision, only half of organizations are confident their underlying job/skill data would be accurate and uniform across records. That uncertainty undermines defensibility and exposes companies in compliance audits or legal challenges.

Correlation with governance: Organizations with NO active governance averaged ~3.0 confidence (moderately confident). Those with governed programs averaged only ~3.5—not dramatically higher. This suggests that simply having a program isn't enough; the program needs to be effective (integrated data, clear ownership) to truly boost confidence.

 

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The Speed Problem: Slow Cycles, Missed Opportunities

We asked: "How long does it typically take to publish a revised job description (from submission to approval/publish)?"

Time to Publish (n=227):

• <3 days: 6.2% (14 orgs) — fastest/most agile
• 3–7 days: 21.1% (48 orgs)
• 8–14 days: 40.5% (92 orgs) — most common timeframe
• 15–30 days: 25.1% (57 orgs) — one-third take >2 weeks
• >30 days: 6.6% (15 orgs) — 1 in 15 takes >1 month
• Don't know: 0.4% (1 org)

Key insight: While the majority (67.8%) update jobs within 1–2 weeks, 31.7% face cycle times longer than two weeks, with some taking over a month.

Why this matters:

• Operational efficiency: If it takes a month to update a role, that delays posting critical positions or adjusting roles for new skills (like AI capabilities).
• Mobility: Long publish times can impede internal moves. Employees might seek opportunities elsewhere if it takes too long to formalize a new role.
• Agility: Slow cycle times signal bottlenecks or manual workflows that governance should streamline.

Correlation with governance: Organizations with NO active governance were far more likely to take >2 weeks (54% of that group). Governed organizations still often take 1–2 weeks (due to formal reviews) but rarely drag past a month. This suggests ungoverned settings don't update jobs regularly—and when they do, it's a big, slow effort.

 

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Section 4: How Skills Data Is (and Isn't) Used Today

We asked: "Which talent processes formally link or utilize skills data?" (multi-select)

Skills Data Application (n=227):

• Hiring and requisition requirements: 63.0% (143 orgs)
• Workforce planning: 48.5% (110 orgs)
• Internal mobility and career paths: 48.0% (109 orgs)
• Compensation and banding decisions: 48.0% (109 orgs)
• Reskilling and upskilling programs: 37.4% (85 orgs)
• Not formally linked to any processes: 8.8% (20 orgs)
• We don't use a formal job architecture: 1.8% (4 orgs)

The Uneven Integration Pattern

Strong in hiring (63.0%) but weaker in development and rewards:

• Fewer than half link skills to compensation, mobility, or workforce planning
• Only ~1 in 3 link skills to reskilling and upskilling programs

Key insights:

Hiring: The Most Mature Use Case

Nearly two-thirds formally incorporate skills into hiring (job postings, candidate assessments). This reflects the relative maturity of ATS systems and job descriptions in recruiting.

But here's the thing: Even here, 37.0% do not formally link skills, suggesting many job ads are based on outdated templates without governed skill criteria.

Compensation: The Defensibility Gap

Only 48.0% use skills in compensation or banding decisions. The other 52.0% do not formally tie skills to pay structures.

What this means: In many organizations, pay ranges aren't consistently informed by skills or proficiency, directly risking pay equity. Without skill-based rationale, pay decisions rely on legacy job grading or negotiation, which are harder to defend in audits.

Mobility: The Equity Problem

48.0% formally use skills in internal mobility decisions. This number is higher than Mercer's estimate (only 14% link skills to career paths), suggesting our HR-involved sample may be more advanced. Still, over half have a mobility equity problem.

What this means: 52% of organizations have no formal skills-based career pathing, meaning mobility often relies on job title, manager discretion, or the right network. Without transparent criteria for advancement, organizations risk promoting employees with mismatched or irrelevant skill sets.

Reskilling: The Missed Opportunity

Only 37.4% link skills data to L&D programs—the lowest among major use cases.

What this means: Many companies run learning programs without feeding skill outcomes back into a central inventory or using inventory data to target development. Training investments may not align with actual skill gaps because those gaps aren't formally tracked.

Impact on mobility: Mobility depends on upskilling. If learning systems aren't integrated with talent data, employees can't easily demonstrate newly acquired skills for internal moves.

The "Not Linked" Red Flags

• 8.8% selected "not formally linked to any process"
• 1.8% said "we don't use a formal job architecture"

These ~10% essentially operate without a structured skills framework. In such environments, everything from hiring requirements to pay scales is handled ad-hoc, case-by-case. Doing so creates inconsistent standards, duplicate roles, unequal pay for similar skills, and lack of career progression paths.

Business implication: Fragmentation in applying skills data undermines both equity and defensibility. If skills aren't embedded in compensation, pay equity reviews miss skill-based disparities. If skills aren't part of mobility criteria, internal moves favor tenure and connections over skill-fit. If workforce planning ignores skill inventories, organizations under-utilize internal talent or over-invest in external hires.

 

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Section 5: AI as Forcing Function

We asked: "How is AI (e.g., generative AI, automation) affecting your organization's approach to workforce skills?"

AI Impact (n=227):

• Accelerating upskilling/reskilling: 23.8% (54 orgs) — top response
• Driving new skill requirements (prompt engineering, AI oversight): 20.3% (46 orgs)
• Forcing re-evaluation of job architecture/governance: 16.7% (38 orgs)
• Leading to potential role reductions or redeployments: 15.4% (33 orgs)
• Minimal impact so far: 22.9% (52 orgs)

Key insight: ~77% of organizations see AI driving some change in their skills strategy. Only ~23% see minimal impact, though given AI's pace, this "lag" group may shrink soon.

Combined urgency: About 45% are either accelerating upskilling or rethinking governance due to AI. That's a strong signal that governance improvements are imminent.

Paul Smith's Perspective: AI's Dual Impact on the Workforce

Smith offers critical context on AI's complex impact:

"AI is fundamentally restructuring work. Some roles are being displaced—we're seeing that with major layoffs across tech. But roles that remain are becoming more complex. Think about HR; you're no longer just expected to be a good recruiter. You're expected to understand how agents operate with skills data, what outputs mean, and teach that agent your industry jargon. Surviving roles are expanding dramatically in skills needed."

He adds: "If you're not a good prompt engineer, it doesn't matter what your role is. You're going to be antiquated within the next three or four years, almost guaranteed."

What this means for skills governance:

• AI is both eliminating roles (as seen at Salesforce, Amazon, and others) and adding new skills to remaining positions
• Organizations need visibility into existing skills to manage both workforce reductions and redeployment/reskilling efforts
• HR needs prompt engineering, agent interaction understanding, and AI context-setting
• Finance needs AI oversight and audit trail interpretation
• Every function needs to teach AI systems their industry jargon and processes

The governance implication: Managing AI's workforce impact—whether displacement, redeployment, or upskilling—requires real-time, governed skills data. You can't redeploy talent you can't see, and you can't upskill effectively without knowing what skills already exist. Ad-hoc spreadsheets and project-based inventories can't keep pace. High-coverage organizations realize this, which is why 57.4% of that group plan to implement fully governed programs in the next 12–18 months.

 

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Section 6: Outcomes & What's Measured (or Not)

The Mobility Bright Spot

We asked about year-over-year change in internal mobility rate:

Internal Mobility Trends (n=227):

• Up 11+ percentage points: 5.7% (13 orgs)
• Up 6–10 percentage points: 34.4% (78 orgs)
• Up 1–5 percentage points: 38.3% (87 orgs)
• No material change: 13.2% (30 orgs)
• Down: 1.3% (3 orgs)
• Don't track/not sure: 0.4% (1 orgs)

The encouraging trend: 78.4% of organizations reported an increase in internal mobility over the prior year. Only 1.4% saw declines.

Correlation with governance: Organizations with ongoing governed programs were somewhat more likely to report above-average mobility increases—qualitatively, a higher proportion of the 6–10 point improvements came from the governed group. The most significant jumps (>10 points) were exclusively in companies with at least some formal governance.

What this suggests: When job and skills data are structured and visible, it's easier to match people to opportunities, resulting in more internal hires and transfers. Without structured data, managers default to external hiring or miss internal candidates.

The gap: While 78.4% see mobility increasing, only 38.3% measure it regularly (see below). Many are experiencing gains but not quantifying them, making it hard to tie improvements to skills initiatives.

 

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The Measurement Gap: What's Tracked vs. What Matters

We asked: "Which outcomes and metrics are measured at least quarterly for job/skills programs?" (multi-select)

Metrics Measured (n=227):

• Cycle times (JD → Req → Hire; comp reviews): 47.1% (107 orgs)
• Employee experience outcomes (clarity and engagement): 42.7% (97 orgs)
• Time-to-publish roles/job content: 38.8% (88 orgs)
• Exception rates and MTTR: 38.8% (88 orgs)
• Internal mobility rate: 38.3% (87 orgs)
• Pay equity variance: 22.5% (51 orgs) ← equity gap
• First-year turnover rate: 22.0% (50 orgs)
• None of the above: 5.7% (13 orgs)

The pattern: Operational metrics (cycle time, exceptions) are tracked more than strategic outcomes (equity, mobility).

Key gaps:

Pay Equity: The Silent Risk

Only 22.5% measure pay equity outcomes quarterly. Despite equity being a major HR concern (and often a legal compliance area), fewer than 1 in 4 have a regular metric around it.

Why this matters: If ungoverned skills data contributes to pay discrepancies (e.g., similar roles with different skill-based pay without clear rationale), most companies aren't rigorously tracking that on an ongoing basis. Equity problems can persist unnoticed.

Mobility: The Blind Spot

Only 38.8% measure internal mobility regularly, indicating that most HR teams can't show whether their skills investments are actually driving mobility or which programs deliver results.

Why this matters: Without clear mobility metrics, organizations are essentially guessing where to invest in reskilling and career pathways. HR is left with costly programs that don’t boost ROI and employees who look elsewhere for growth.

Exceptions: The Defensibility Signal

38.8% measure exception rates, suggesting a meaningful chunk of companies are quantifying how often standard processes are bypassed (e.g., special salary approvals, off-cycle adjustments).

What this tells us: The high occurrence of exceptions indicates weak governance. Without synchronized systems and formal approvals, pay decisions bypass standards, generating exceptions. Those measuring exceptions are likely better at controlling them (you can't improve what you don't measure).

Governance correlation: Organizations with clear ownership are more likely to track exception rates (perhaps because they're actively managing them), while those lacking ownership often cited fragmentation and had slower cycles (implying more exception handling burden).

 

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Section 7: The Path Forward—From Coverage to Control

The Momentum is Real: ~90% Plan Governance Improvements

We asked: "How do you expect your job/skills data governance to change in the next 12–18 months?"

Future Intentions (n=227):

• Significant improvement (move to fully governed program with approvals, lineage, synchronized systems): 37.4% (85 orgs)
• Moderate improvement (implement system to store, standardize, approve): 52.4% (119 orgs)
• No change (remain ad hoc/project-based): 10.1% (23 orgs)

The mandate: 89.9% plan to advance their governance maturity within 1–1.5 years. Only 10.1% expect the status quo to hold.

What's driving this:

• AI urgency: 16.7% already say AI is forcing governance reevaluation
• High-Coverage Paradox: 92.6% of those with best coverage know they need better governance
• Baseline dissatisfaction: Only 50.2% are confident in their data—despite having inventories

Market signal: Skills data governance is poised to be a major HR focus area in 2026–2027. If executed, we should see higher governance adoption and corresponding gains in efficiency, equity, and mobility metrics in future surveys.

 

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Best Practices from High Performers

In analyzing the data, we identified patterns among "high performers"—organizations with desirable outcomes:

1. Clear Ownership + Formal Processes = Faster Cycles

Organizations that selected both "clear ownership" and "formal approval workflows" were more likely to update jobs within 1 week than average.

Why: When roles are clear and processes are in place, there's less back-and-forth about who approves what. Ownership and approvals reduce bureaucracy by making accountability explicit rather than creating hidden bottlenecks.

2. Synchronized Systems = Higher Confidence

Organizations with fully synchronized systems (HRIS ↔ ATS ↔ Comp ↔ LMS) nearly all also had:

• Clear ownership
• Higher likelihood of measuring exception rates and time-to-publish
• Culture of continuous improvement

Why: A single source of truth across systems builds trust in data quality, enabling better decision-making.

3. Broad Skills Integration = Better Mobility

Organizations that embedded skills across multiple talent processes (hiring, compensation, mobility, planning)—roughly 30% did 3+ areas—reported higher mobility increases.

Why: A consistent skills framework across hire → develop → promote → pay creates a more fluid talent pipeline. Employees can see clear skill-based paths for advancement rather than relying on opaque title progressions.

4. Measurement Culture = Proactive Problem-Solving

Organizations measuring a broad array of metrics (mobility, equity, exceptions, time-to-publish, experience) likely caught issues sooner and iterated on governance faster.

Emerging best practice: Track relevant KPIs and use them to improve governance iteratively. Given how few track equity (only ~22%), those that do are ahead of the curve in ensuring governance drives fairness.

 

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The Organize → Standardize → Strategize Framework

Organize (baseline): Most organizations are here or recently passed through. They've organized skills into some inventory, whether via project, consultant, spreadsheet, or decentralized effort. Coverage achieved.

Standardize (the governance layer): This is where high-coverage organizations realize they need to go—implement the operating model that keeps skills data clean, consistent, and usable:

• Clear ownership and accountability
• Formal approval workflows (governance guardrails, not bureaucracy)
• Version history and lineage (audit trails for defensibility)
• Synchronized systems (HRIS ↔ ATS ↔ Comp ↔ LMS in lockstep)
• Operational telemetry (SLAs, MTTR, exception rates tracked out-of-the-box)

Strategize (the ROI): Only with governance can you confidently use skills data for:

• Workforce planning and skill gap analysis
• Internal mobility and talent marketplaces
• Compensation equity and defensible bands
• Upskilling and reskilling program targeting
• AI enablement and role transformation

Coverage gets you the data. Governance gets you the ROI.

 

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Section 8: Business Implications—The Insight-to-Implication Matrix

 

 

 

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Conclusion: From Coverage to Control

This research confirms a fundamental market reality: enterprises have achieved coverage but lack control.

The High-Coverage Paradox—92.6% of high-coverage organizations planning governance overhauls—reveals that having an inventory of skills is not the same as having a governed system for managing those skills over time.

The Mandate for Governance

Bottom-line: without governance, skills data becomes a liability.

• Equity at risk: >50% don't link skills to comp or mobility → inconsistent decisions
• Defensibility gaps: Only 56.1% have approvals; 46.7% lack lineage → no audit trail
• Operational drag: 61.3% have fragmented systems; 31.6% take >2 weeks to publish → manual rework
• AI unreadiness: Ad-hoc data won't support automated workforce decisions → garbage in, garbage out

The Path Forward: Organize → Standardize → Strategize

Organizations that move fastest will:

1. Understand coverage (audit current practices for starting point)
2. Assign clear ownership (19.3% cite this as top barrier—fix it first)
3. Implement approvals and lineage (governance guardrails, not bureaucracy)
4. Synchronize systems (HRIS ↔ ATS ↔ Comp ↔ LMS in lockstep)
5. Measure outcomes (mobility, equity variance, exceptions, SLAs)

The JDX Difference: Governance, Not Just Coverage

Most vendors solve for coverage—taxonomy tools, skills ontologies, AI inferencing engines. They help you build the list.

JDX solves for governance—the operating model and platform that keeps skills data clean, consistent, and usable across its lifecycle:

Coverage gets you the data. Governance gets you the ROI.

JDX's Roadmap for Governed Job & Skills Information

Organizations that move fastest will follow JDX's proven roadmap: Architect, Create, Manage, Analyze, Integrate:

1. Architect — Build the Foundation

Establish clear job architecture and ownership structure. 19.3% cite lack of ownership as their top barrier. Fix this first. Define job families, levels, and who's accountable for content integrity.

2. Create — Standardize Content with Controls

Implement approval workflows and version control (governance guardrails, not bureaucracy). Ensure every job description has a clear lineage: who changed what, when, and why. Audit-ready from day one.

3. Manage — Operate with Visibility

Track operational telemetry: cycle times, exception rates, time-to-publish, and MTTR. Measure what matters, such as mobility rates, pay equity variance, and employee experience outcomes. You can't improve what you don't measure.

4. Analyze — Turn Data into Insights

Use governed job and skills data to identify gaps, predict needs, and inform workforce planning. Only 22.2% measure pay equity quarterly. Governed analytics close this blind spot.

5. Integrate — Synchronize Across Systems

Connect HRIS ↔ ATS ↔ Comp ↔ LMS in lockstep (no manual CSV exports). 61.3% have fragmented systems today. Integration is where governance delivers ROI at scale. Clean, governed data becomes the foundation for AI-powered talent strategies.

The outcome: Organizations that follow this roadmap transform job information from operational liability into strategic assets, achieving faster hiring, equitable pay, transparent career paths, and AI readiness.

 

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Methodology & Respondent Profile

Survey Details

• n = 227 complete responses (374 total; 227 "Full" responses analyzed)
• Fielding period: October 2025
• Panel: HR Leaders
• Attention check: 100% pass rate (data reliability confirmed)

Respondent Profile

• Company size: 1,000–10,000 employees (75% from 5,001–10,000; 25% from 1,001–5,000)
• Geography: U.S. organizations
• Roles: HR professionals involved in job/skills data (HRIS, compensation, and talent management)
• Seniority: 76% senior management or executives (ensuring executive-level perspective)

Survey validated by: Paul Smith, Solutions Consultant, JDX (industry practitioner with deep skills architecture expertise)

 

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Next Steps for HR Leaders

Use these findings as a benchmark for governance readiness. Key immediate actions:

1. Assign Clear Ownership (19.4% cite as top barrier)

• Designate a single owner or governance council for job/skills data
• Define RACI (Responsible, Accountable, Consulted, Informed) for changes
• Don't let ownership fall between HR, IT, Total Rewards; make it explicit

2. Audit Current State

• Assess coverage: what % of roles have up-to-date skills? (Most are 50–74%)
• Identify governance gaps: do you have ownership, approvals, lineage, sync?
• Measure confidence: survey stakeholders on data trust (50% aren't highly confident)

3. Implement Quick Wins

• Easy win: Establish approval workflow (even manual/email-based to start)
• Medium lift: Synchronize 2 critical systems (e.g., HRIS ↔ Compensation)
• Foundation: Document version history/lineage for recent changes (audit trail)

4. Set Metrics & Track

• Start measuring: internal mobility, time-to-publish, exception rates, pay equity variance
• Establish baseline: what's your current state? (Only 38% track mobility; 23% track equity)
• Create dashboard: report quarterly to prove ROI of governance investments

5. Plan for AI

• Inventory current skill requirements vs. AI-expanded needs (prompt engineering, agent interaction, etc.)
• Recognize: ad-hoc data won't support AI-powered talent decisions
• Build governed foundation before implementing AI-driven talent tools

6. Benchmark Against the Paradox

• If you're at 75%+ coverage: are you planning an overhaul? (92.6% of peers are)
• If yes: use this report to build the business case for governance investment
• If no: assess whether you have the controls (ownership, approvals, lineage, sync) that others lack
  
  

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References

1. World Economic Forum — Future of Jobs 2025
2. Mercer — Job Architecture & Skills Linkage Survey
   
   

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Coverage gets you the data. Governance gets you the ROI.