A structured 90-day implementation tool for wiring responsible AI directly into your development pipeline. Covers all five PRIME dimensions, stack mapping, KPI baselines, and sprint planner.
Canonical reference: The Real Cost of AI Isn’t Ethics — It’s Rework
Responsible AI is foundational, not bolt-on. PRIME artifacts go into sprint one, not sprint twelve. A fairness evaluation is a test suite, not a six-month study. A benefit hypothesis is a paragraph, not a dissertation. The rework you avoid more than pays for the artifacts you produce.
Two KPIs, not twenty. Intelligence Velocity and Principles-to-Product Coverage are sufficient to tell your board whether trust is improving or eroding. If your responsible AI dashboard has more than five KPIs, you’re measuring effort, not outcomes. Resist metric proliferation.
Select one revenue-adjacent generative AI use case as your PRIME pilot. Focus beats breadth.
| Field | Your Response |
|---|---|
| AI Use Case Name | |
| Business Owner | |
| Revenue Adjacency Why this use case matters commercially | |
| Deployment Stage Pilot / Production / Scaling | |
| Users / Audience Who consumes the AI's outputs? | |
| Current Responsible AI Coverage What exists today? Policy doc, review board, nothing? |
For your selected use case, assess each PRIME dimension. Check what exists, identify gaps, define the artifact to produce, and name the owner.
| Artifact | Owner (Name) | Target Date |
|---|---|---|
| Benefit Hypothesis | ||
| Affected-Groups Map |
| Artifact | Owner (Name) | Target Date |
|---|---|---|
| High-Stakes Context Register | ||
| Decision Rights Document | ||
| Opt-Out / Correction UX Spec |
| Artifact | Owner (Name) | Target Date |
|---|---|---|
| Model Card (living document) | ||
| Data Sheet / Lineage Record | ||
| Explanation UX per Audience |
| Artifact | Owner (Name) | Target Date |
|---|---|---|
| Evaluation Harness (halluc., toxicity, jailbreak) | ||
| Release Gate Thresholds | ||
| Kill Switch Configuration | ||
| CI/CD Privacy & License Checks |
| Artifact | Owner (Name) | Target Date |
|---|---|---|
| Accountability Register (owner per feature) | ||
| Evidence Trail Specification | ||
| Policy-as-Code Configuration | ||
| Vendor Contract Clauses |
Map your PRIME implementation to the five operational surfaces. For each surface, document what exists today and what needs to be built.
| Operational Surface | Current State | Target State | What Gets Measured |
|---|---|---|---|
| Governance Policy-as-code, provenance tags |
P2P Coverage per PRIME dimension | ||
| Data Contracts, provenance, consent |
Data lineage completeness; leakage incident rate | ||
| Model & Agent Approved models, eval harness |
Intelligence Velocity; adversarial test pass rate | ||
| Experience Disclosures, feedback, explanation UX |
Explanation coverage; user correction rate | ||
| Operations Incident registry, weekly triage |
Incidents detected; mean time to mitigation |
The two metrics that convert responsibility into engineering discipline. Measure your starting position, then track monthly.
| Month | Intelligence Velocity | P2P Coverage (%) | Notes / Actions |
|---|---|---|---|
| Baseline (Day 0) | |||
| Month 1 | |||
| Month 2 | |||
| Month 3 | |||
| Month 6 |
Assign ownership, dates, and accountability for each month.
Which responsible AI tools are already in use? Where does PRIME fill the gaps?
| Tool / Framework | In Use? | What It Covers | PRIME Gap It Doesn't Fill |
|---|---|---|---|
| Microsoft HAX Toolkit | UX design patterns, 18 Guidelines for Human-AI Interaction | No CI/CD integration, no automated evals, no policy-as-code | |
| Google SAIF | AI security, threat taxonomy, adversarial robustness | No fairness, explainability, benefit assessment, or accountability | |
| NIST AI RMF | Comprehensive risk management framework (Govern, Map, Measure, Manage) | PRIME provides the pipeline-level implementation NIST describes at policy level | |
| Internal Tools | |||
| Other |
Rate your completion of each PRIME dimension. Circle: Not Started / In Progress / Complete.
Definitions used throughout this worksheet. These align with the canonical PRIME article.
PRIME. A five-dimension framework for wiring responsible AI directly into the development pipeline: Purposeful Impact, Respect for Humans, Interpretable by Design, Mitigated by Engineering, Enforceable Accountability. Maps OECD AI Principles to NIST AI RMF controls.
Intelligence Velocity (IV). The time it takes to resolve a high-severity AI issue (data drift, fairness regression, prompt injection, hallucination) once detected. Target: under 72 hours for the pilot use case. Lower is better.
Principles-to-Product Coverage (P2P). The percentage of shipped AI features with all five PRIME artifacts complete: benefit hypothesis, fairness evaluation, explanation UX, safety gates, and audit trail. Most organizations start at 20–25%. Quarter-one target: 80%.
PRIME pilot use case. The single revenue-adjacent generative AI use case selected for first PRIME implementation. Focus beats breadth — one use case run end-to-end is more valuable than five run partially.
Stack mapping. Translating PRIME requirements into concrete artifacts at five operational surfaces: Governance, Data, Model & Agent, Experience, Operations. Frameworks that stay conceptual don’t survive contact with engineering teams.
Rework cost. The percentage of each engineering sprint consumed by remediating responsible-AI gaps caught after deployment. Pre-PRIME baseline observed across engagements: ~30% of sprint capacity. Post-PRIME at month 3: under 5%.
EU AI Act readiness note. A one-page document produced at Month 3 stating the pilot’s risk-category hypothesis under the EU AI Act, plus the evidence already collected via PRIME artifacts. Most of the regulatory evidence requirement is met by the artifacts you already produce.
This worksheet is the operational layer of a published, sourced framework. The full argument, OECD/NIST mapping, and the financial-services case study live in the canonical article below.
Canonical article: The Real Cost of AI Isn’t Ethics — It’s Rework — the rework problem, the PRIME framework, OECD-to-NIST mapping, two-KPI rationale, and a 90-day case study (rework cut from 30% to under 5%).
Companion frameworks:
Foundational external sources: OECD AI Principles · NIST AI Risk Management Framework · NIST AI 600-1 (Generative AI Profile)