Open Positions
Count of board-approved roles that are currently posted and unfilled (requisition open, offer not yet accepted). The leading-edge indicator for upcoming hiring capacity demand. Common pitfall: "approved" drift — roles that were verbally green-lit but never went through the approval gate get counted here, inflating the number. The board number should match the approved headcount budget; everything else belongs in narrative as "pipeline ideas." — HR KPI, I'mBoard-authored (editorial tier).
I'mBoard-authored (editorial tier)
No public third-party standard anchors this KPI yet, so I'mBoard authors and maintains the definition — transparently labeled as editorial tier. See the ontology methodology for the published vs editorial tier system and the back-attribution workstream.
Rogue ID: hr.open_positions
Type: Number
Domain: HR
Definition
Count of board-approved roles that are currently posted and unfilled (requisition open, offer not yet accepted). The leading-edge indicator for upcoming hiring capacity demand. Common pitfall: "approved" drift — roles that were verbally green-lit but never went through the approval gate get counted here, inflating the number. The board number should match the approved headcount budget; everything else belongs in narrative as "pipeline ideas."
Formula
Count of requisitions where status = open AND approval = granted AND no candidate has accepted. Excludes positions filled but not yet started (those belong to `hr.new_hires` in the next period). Excludes role ideas not yet approved.Why it matters
Quantifies the hiring debt — every open role is unrealized capacity. Combined with hr.avg_days_to_fill, it projects when capacity actually arrives. A growing open-position count while time-to-fill stretches is a recruiting-capacity yellow flag.
How to interpret
Open positions ÷ approved budget gap = recruiting load. If open positions exceed 15–20% of total headcount sustained over multiple periods, it usually signals either compensation issues, recruiter capacity issues, or unrealistic role specs (industry folk-wisdom, not citation-grade). Tie to hr.key_openings for priority-weighted context.
Calculation policy
How an AI agent should compute this KPI from messy company data. Free-text rules consumed at reasoning time — not a deterministic DSL. The most common ways to get this wrong are listed under Common miscomputations.
Inclusion rules
- Count of requisitions where status = open AND approval = granted AND no candidate has yet accepted — a point-in-time snapshot of unfilled, board-approved roles as of the period-end date.
- The number should tie to the approved headcount plan: every open req traces to a budgeted, approved seat.
Exclusion rules
- Role ideas that were verbally green-lit but never cleared the approval gate — "approved drift" inflates the count; those belong in narrative as pipeline ideas.
- Positions already filled but not yet started — the candidate accepted, so the seat is no longer open; it becomes
hr.new_hiresnext period. - Backfills already covered by an accepted offer.
Required inputs
- Requisition list at the snapshot date with status, approval state, and candidate-accepted flag.
- The approved headcount plan to confirm each open req is budgeted.
- Priority / criticality tags if the board reads
hr.key_openings.
Data-source priority
- ATS (Greenhouse / Lever / Ashby) open-requisition report.
- Approved headcount budget / board resolution to validate the approval gate.
Edge cases
- Offer extended but not yet accepted: still open (candidate has not accepted) — flips out of the count only on acceptance.
- Evergreen / pipeline reqs kept open with no intent to hire imminently: exclude or flag separately so the hiring-debt read is honest.
- A single req approved for multiple identical hires: count the number of seats, not the number of postings.
Validation checks
- Open positions ÷ approved-budget gap = recruiting load; sustained above ~15–20% of total headcount signals comp, recruiter-capacity, or role-spec issues.
- Open positions closed in a period should reconcile against
hr.new_hiresstarted in the following period, allowing forhr.avg_days_to_fill.
Common miscomputations
- Counting unapproved "wish-list" roles as open positions — overstates hiring debt and breaks the tie to the approved plan.
- Leaving filled-but-not-started seats in the open count, double-reading them once here and again as next-period hires.
- Counting postings instead of approved seats when one req covers several identical hires.
Related KPIs
hr.approved_headcount_budgethr.avg_days_to_fillhr.hiring_planhr.key_openingshr.new_hires
Source
I'mBoard editorial — authored and maintained by I'mBoard, first published 2026-04-01. No third-party standard is cited for this KPI; when one emerges, the definition is back-attributed and promoted to the published tier (a minor version bump). Read the ontology methodology for the published vs editorial tier system, attribution rules, and dispute process.
Stage relevance
| Company stage | Priority |
|---|---|
| Series A | Core |
| Series B | Core |
| Series C+ | Core |
| Public | Core |
Suggested for stages: Series A, Series B, Series C+, Public.
Default owning functions
- HR
Machine-readable
- This KPI as JSON:
/api/ontology/hr/open_positions.json - All HR KPIs:
/api/ontology/hr.json - Full catalog:
/api/ontology/index.json
New Hires
Count of employees whose first day fell within the reporting period. The growth-input side of the headcount equation, paired with `hr.voluntary_exits` and `hr.terminations` on the loss side. Common pitfall: counting accepted offers vs actual start dates — these can diverge by weeks (notice period) or fall through entirely (offer rescind, candidate ghosting). The board number should be actual starts, not signed offers; pipeline movement belongs in `hr.hiring_plan` narrative. — HR KPI, I'mBoard-authored (editorial tier).
Payroll as % of Burn
Monthly fully-loaded payroll cost as a percentage of `finance.gross_burn_rate`. Tells the board what share of cash outflow funds people vs everything else (infra, GTM spend, professional services, facilities). Common pitfall: comparing this ratio across companies without normalizing for stage and capex intensity — a pure-software seed company will run very payroll-heavy; a hardware-or-bio company will not. Best practice is to read this in conjunction with the burn-rate trend, not in isolation. — HR KPI, I'mBoard-authored (editorial tier).