AI Product Maturity Checklist
Assess your AI product against AIPCH01–AIPCH20. Your answers are saved locally (browser only).
Scoring uses percentage normalization since weights total 92.This AI Product Maturity Checklist may be used, shared, and adapted freely, including for commercial purposes, provided attribution is given to KaizenXOne (Founding Architect of AIPS) “AI Product Maturity Checklist — KaizenXOne, Base Product Specification (AIPS), licensed under CC BY 4.0.”
🤖 AI Product Maturity Checklist
0.0% — Maturity Level 0: AI Asset (Basic Productization)Mark each characteristic as Yes, Partial (50%), or No. Add optional evidence links/notes.
| # | AIPCH | Characteristic | Checklist Item | Description | Weight | Status | Evidence (URL or note) | Points |
|---|---|---|---|---|---|---|---|---|
| 1 | AIPCH01 | Domain-Owned | Named AI Product Owner (AIPRO) Assigned | Product is owned by a specific business domain or functional area with a named accountable AIPRO. | 6% | 0 | ||
| 2 | AIPCH02 | Deployable | End-to-End Designable, Developable, Deployable by Domain | Domain team can design, train, and deploy the AI Product independently using self-service tooling. | 6% | 0 | ||
| 3 | AIPCH03 | Declarative | Defined via Declarative AI Specification | Model architecture, configuration, policies, and interfaces are expressed through structured specs (AIPDS YAML/JSON). | 4% | 0 | ||
| 4 | AIPCH04 | Discoverable | Registered in AI Marketplace or Catalog | Product is discoverable through metadata-rich listings including capability, domain, and trust score. | 4% | 0 | ||
| 5 | AIPCH05 | Self-Describing | Rich Metadata Embedded (Model Card / Datasheet) | Includes description, version, training summary, input/output schema, and usage notes. | 4% | 0 | ||
| 6 | AIPCH06 | Trustworthy (via Trust Signals) | Emits Trust & Quality Metrics | Model publishes trust metrics such as accuracy, drift, compliance, and ethical posture. | 6% | 0 | ||
| 7 | AIPCH07 | Reusable | Reused Across Use Cases or Domains | AI Product is reused or extended across multiple workflows, applications, or teams. | 4% | 0 | ||
| 8 | AIPCH08 | SLA–SLO Backed & Observable | Performance Monitored and Measured | Latency, inference accuracy, uptime, and quality metrics are defined and continuously observable. | 6% | 0 | ||
| 9 | AIPCH09 | Compliant by Design | Policy-as-Code Enforcement for AI Ethics & Regulation | AI Product integrates regulatory and ethical compliance checks (GDPR, fairness, bias mitigation). | 6% | 0 | ||
| 10 | AIPCH10 | Addressable | Provides Well-Defined Interfaces | Exposed via APIs, SDKs, or agent interfaces with standard documentation and access control. | 4% | 0 | ||
| 11 | AIPCH11 | Semantically Aligned | Aligned to Enterprise and Domain Ontologies | AI Product uses canonical vocabularies and semantics for input/output data. | 4% | 0 | ||
| 12 | AIPCH12 | Consumption-Driven Intent | Built for Clear Business Purpose or Consumer Context | Product’s design is guided by explicit business or operational use cases. | 4% | 0 | ||
| 13 | AIPCH13 | Testable & Versioned | Includes Evaluation Dataset and Version Control | Product maintains reproducible tests, benchmark datasets, and semantic versioning. | 6% | 0 | ||
| 14 | AIPCH14 | Economically Accountable | Tracks Cost, Usage, and ROI Metrics | Inference, training, and infrastructure costs are tracked, attributed, and visible to stakeholders. | 6% | 0 | ||
| 15 | AIPCH15 | Explainable & Transparent | Model Decisions Are Interpretable and Documented | Includes explanation interfaces, LIME/SHAP-style outputs, and global feature insights. | 5% | 0 | ||
| 16 | AIPCH16 | Bias-Controlled & Fairness-Measured | Bias Metrics Monitored and Actively Mitigated | Includes fairness evaluation, bias dashboards, and mitigation workflows. | 5% | 0 | ||
| 17 | AIPCH17 | Continually Learnable (Retraining Ready) | Supports Continuous Improvement Loops | Product supports retraining pipelines with drift detection and feedback integration. | 6% | 0 | ||
| 18 | AIPCH18 | Safe & Policy-Bound Usage | Prohibited Use Policy & Safety Controls Implemented | Includes explicit boundaries for use, misuse detection, and ethical filters. | 5% | 0 | ||
| 19 | AIPCH19 | Interoperable & Composable | Can Be Orchestrated with Other AI or Data Products | Compatible with APIs, orchestration frameworks, and hybrid agent systems. | 4% | 0 | ||
| 20 | AIPCH20 | Human-Centered Oversight | Supports Human-in-the-Loop Review and Override | Includes escalation, appeal, and override mechanisms for critical decisions. | 5% | 0 | ||
| TOTAL | 0.0 | |||||||
🎯 Prioritized Recommendations
High Priority — Not Met
- AIPCH01 (6%): Assign an accountable AIPRO and publish ownership, escalation, and lifecycle metadata in the registry.
- AIPCH02 (6%): Provide self-service pipelines for design→train→deploy; ensure environment parity and rollback paths.
- AIPCH06 (6%): Instrument accuracy/quality and compliance signals; expose trust score and evaluation history.
- AIPCH08 (6%): Define SLIs/SLOs; create dashboards; set alerts; add runbooks for incidents.
- AIPCH09 (6%): Implement privacy/residency/consent checks; fairness gates; audit logging; retention policies.
- AIPCH13 (6%): Version datasets/models; run regression/eval gates in CI; publish changelog and deprecation policy.
- AIPCH14 (6%): Integrate FinOps; report per-1k tokens/inference cost; enable showback/chargeback; budget alerts.
- AIPCH17 (6%): Implement drift detection; capture feedback loops; automate retraining/re-evaluation pipelines.
- AIPCH15 (5%): Provide local/global explanations; document decision factors; expose explanation endpoints/UI.
- AIPCH16 (5%): Run fairness audits; monitor subgroup metrics; apply mitigation; publish bias reports.
- AIPCH18 (5%): Publish prohibited-use policy; add safety filters/red-team mitigations; log and review overrides.
- AIPCH20 (5%): Provide override/escalation workflows; record interventions; include appeals process and audit trails.
- AIPCH03 (4%): Adopt AIPDS; validate spec in CI; generate infra/policies from spec; remove ad-hoc manual steps.
- AIPCH04 (4%): Onboard to registry; include task type, modalities, owners, risk class, trust score, and quickstart examples.
- AIPCH05 (4%): Publish comprehensive Model Card/Datasheets; link IO schema, training summary, and limitations.
- AIPCH07 (4%): Ship SDKs and adapters; document integration patterns; capture and showcase reuse examples.
- AIPCH10 (4%): Publish OpenAPI/gRPC; provide SDKs; document auth, quotas, and example notebooks.
- AIPCH11 (4%): Map IO schemas to enterprise/domain ontology; use persistent identifiers and lineage links.
- AIPCH12 (4%): Define primary use-cases and KPIs; capture acceptance criteria; provide task-specific examples.
- AIPCH19 (4%): Ensure composable interfaces; agent/flow orchestration compatibility; schema adapters.
ℹ️ Scoring Guidelines
- Yes = full weight
- Partial = 50% of weight
- No = 0%
≥ 80%: Maturity Level 2: Production-Grade AI Product · 50–79%: Maturity Level 1: Evolving / MVP AI Product · < 50%: Maturity Level 0: AI Asset (Basic Productization)
🔐 Governance Tips
- Link to AIPDS spec, Model Cards, eval reports, and safety/policy repos.
- Export JSON for audit trails; attach to PRs or registry entries.
- Use prioritized recommendations to plan next-quarter hardening work.