Delivery Predictability
Percentage of committed deliverables shipped on or before the originally-promised date within a measurement window (typically a quarter). Surfaces whether the engineering organization can be trusted to hit commitments the company makes externally — to customers in contracts, to the board in quarterly plans, to GTM teams sequencing launches. Common pitfall: gaming. Teams over-deliver by under-promising (predictability climbs while velocity drops) or move the goalposts (re-baseline mid-quarter so "on-time" stays high). Boards should ask for "predictability against original commitment", not "against current plan", and pair with throughput trends. — Product 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: product.delivery_predictability
Type: Percentage (%)
Domain: Product
Definition
Percentage of committed deliverables shipped on or before the originally-promised date within a measurement window (typically a quarter). Surfaces whether the engineering organization can be trusted to hit commitments the company makes externally — to customers in contracts, to the board in quarterly plans, to GTM teams sequencing launches. Common pitfall: gaming. Teams over-deliver by under-promising (predictability climbs while velocity drops) or move the goalposts (re-baseline mid-quarter so "on-time" stays high). Boards should ask for "predictability against original commitment", not "against current plan", and pair with throughput trends.
Formula
delivery_predictability_pct = (commitments_delivered_on_time / total_commitments) × 100, measured against the originally-promised date (not the most recently re-baselined date). Define "on time" explicitly — within the promised week, sprint, or quarter — and apply consistently.Why it matters
Predictability is the contract between engineering and the rest of the business. When it slips, GTM cannot sequence launches, sales cannot promise dates, and the board cannot trust the quarterly plan. Sustained low predictability is a leading indicator of either capacity mismatch, planning hygiene problems, or accumulated technical debt.
How to interpret
Industry folk-wisdom, not citation-grade: 70–85% predictability is typical for healthy growth-stage engineering organizations; 90%+ usually means sandbagging (commitments are too soft); below 60% means the planning process is broken or capacity is mismatched. Trend matters more than absolute level — a stable 75% is healthier than a 90% sliding to 70% quarter-over-quarter.
Related KPIs
product.key_initiatives_statusproduct.capacity_allocation_pctproduct.innovation_capacity_pctproduct.scalability_headroom
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.
Industry benchmark
A reference distribution sourced from imboard Editorial (2026):
| Percentile | Value |
|---|---|
| 25th | 55% |
| Median | 70% |
| 75th | 85% |
Higher is better.
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
- R&D
- Product
Machine-readable
- This KPI as JSON:
/api/ontology/product/delivery_predictability.json - All Product KPIs:
/api/ontology/product.json - Full catalog:
/api/ontology/index.json
Revenue Protection %
Percentage of the planned roadmap allocated to defensive work — platform reliability, security/compliance, scalability rearchitecture, table-stakes parity with competitors, customer-retention features. The complement of `offensive_roadmap_pct`. Common pitfall: defensive work is chronically under-funded (less visible to customers, harder to demo) until a quality-churn or scalability event forces a reactive surge. Boards should treat sustained zero or near-zero defensive allocation in a maturing product as a leading indicator of future quality issues — per the standard product-management argument (Marty Cagan and similar product-leadership writing), a healthy roadmap pays both growth and platform-health rent. — Product KPI, I'mBoard-authored (editorial tier).
Weighted Feature Adoption
Percentage of customers (weighted by ARR) actively using a defined set of strategic features within a measurement window. The "ARR-weighted" framing matters: a feature used by 30% of customers covering 70% of ARR is a different signal than 30% of customers covering 5% of ARR. Common pitfall: defining adoption as "ever used" rather than "actively using" (returning use in the measurement window) — the first metric only goes up and tells the board nothing. Boards should require an active-use definition (e.g. used in 2 of the last 4 weeks) and a per-feature breakdown for the strategic feature set. — Product KPI, I'mBoard-authored (editorial tier).