Score your organization across eight delivery maturity dimensions — four DORA metrics and four product operations indicators. Enter scores and evidence, then copy the structured output to an AI tool for maturity analysis and recommendations.
Rate each dimension 1–5. Add evidence notes — specific metrics, examples, or observations — to enable AI-assisted analysis. Load sample data to see an example output.
Sample data shows a mid-maturity organization making progress on DORA metrics but with gaps in product ops disciplines.
Each dimension is scored 1–5 against defined behavioral criteria. Use this rubric when scoring your organization and when interpreting AI-generated analysis. Scores reflect observable practices, not aspirations.
| Dimension | 1 — Initial | 2 — Developing | 3 — Defined | 4 — Managed | 5 — Optimizing |
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The structured output from this tool is designed for use with AI analysis tools. The following framework explains how to interpret results and what kinds of questions to ask.
Scores without evidence are opinions. Scores with specific evidence — deployment frequency in the last 30 days, number of dependencies that became blockers last PI, percentage of features that achieved their stated metric — enable AI to validate scoring and identify blind spots. The more specific the evidence, the more precise the analysis.
After copying the output, paste it into Claude or another AI tool and ask: "Based on this maturity assessment, identify the two dimensions where improvement would have the highest cross-dimensional impact, explain the interdependencies between weak dimensions, and recommend a sequenced 90-day improvement plan." The structured format enables specific, actionable responses.
A narrow, irregular shape indicates high variance — some dimensions are strong, others weak — which typically signals siloed improvement efforts. A wider but uneven shape indicates systemic progress with specific gaps. A consistently wide shape approaching level 4–5 across all dimensions indicates an organization operating with intentional, measurable delivery discipline.
DORA metrics measure technical delivery capability. Product ops dimensions measure organizational effectiveness. Organizations frequently advance DORA metrics while ops dimensions remain low — this indicates engineering progress without corresponding organizational maturity, which limits the business value realized from technical improvements. Balanced advancement across both is the target.
The structured output is designed to accompany data uploads — Jira exports, deployment logs, sprint completion reports, or planning accuracy tracking. When uploading data to an AI tool alongside this output, specify which dimensions the data covers. The combination of scored evidence and raw data enables AI to validate scores, surface contradictions, and identify trends not visible in point-in-time scoring.
Over-scoring is the most common error — rating aspirational practices rather than current observed behavior. Score what is consistently happening, not what happens occasionally or what you intend to implement. If a practice exists in one team but not others, score for the median, not the best case. A conservative score with strong evidence is more useful than an optimistic score with weak evidence.