AI Product Maturity Checklist
Assess your AI product against AIPCH01–AIPCH20. Your answers are saved locally (browser only).
Scoring uses percentage normalization.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 | Satisfied Only When ... | Weight | Status | Evidence (URL or note) | Points |
|---|---|---|---|---|---|---|---|---|
| 1 | AIPCH01 | Domain-Owned | Named AI Product Owner (AIPRO) Assigned | ... the AI Product is owned by a business domain that is accountable for the behavior and outcomes of the capability, with a named AIPRO responsible for its definition, governance, and lifecycle. | 8% | 0 | ||
| 2 | AIPCH02 | Deployable | Independently Deployable AI Capability via Self-Service | ... the AI Product can be defined, composed, deployed, and managed end-to-end by the domain using self-service, with all technical complexity abstracted by the platform. | 6% | 0 | ||
| 3 | AIPCH03 | Declarative Specification | Defined via Structured Declarative AI Specification | ... the AI Product is fully represented through structured declarative artifacts (e.g., AIPDS), with no reliance on manual procedural definitions. | 5% | 0 | ||
| 4 | AIPCH04 | Declarative Intelligence Composition | AI Product Is Composed via Declarative, Intent-Driven Expression | ... the AI Product is created through declarative composition of intent, behavior, constraints, and constituent capabilities, with all implementation fully derived and compiled by the platform. | 5% | 0 | ||
| 5 | AIPCH05 | Discoverable | Registered in AI Marketplace or Catalog | ... the AI Product is published into a discovery surface where consumers can independently find, evaluate, and compare it based on declared capabilities and metadata. | 4% | 0 | ||
| 6 | AIPCH06 | Self-Describing | Rich Capability Metadata Embedded | ... the AI Product exposes sufficient metadata describing its behavior, inputs/outputs, constraints, dependencies, and usage context for independent understanding. | 4% | 0 | ||
| 7 | AIPCH07 | Trustworthy (via Trust Signals) | Emits Trust Signals | ... the AI Product continuously emits measurable trust signals covering performance, reliability, compliance, and risk posture, including composed components. | 5% | 0 | ||
| 8 | AIPCH08 | Reusable | Reusable Across Use Cases or Domains | ... the AI Product is designed for reuse and is adopted across multiple contexts without requiring bespoke customization. | 4% | 0 | ||
| 9 | AIPCH09 | SLA–SLO Backed & Observable | Observable with SLAs, SLOs, and Quality Guarantees | ... the AI Product is continuously observable with defined SLAs/SLOs and automated monitoring of performance and behavior. | 5% | 0 | ||
| 10 | AIPCH10 | Compliant by Design | Policy-as-Code and Governance Controls Enforced | ... governance policies (regulatory, ethical, risk) are bound at design time and enforced automatically at runtime via policy-as-code. | 5% | 0 | ||
| 11 | AIPCH11 | Addressable | Provides Well-Defined Output Ports | ... the AI Product exposes governed output ports (APIs, events, agent interfaces) independent of consumer experience layers. | 4% | 0 | ||
| 12 | AIPCH12 | Semantically Aligned | Aligned to Enterprise and Domain Ontologies | ... the AI Product’s inputs, outputs, and behavior align with shared enterprise vocabularies and ontologies. | 4% | 0 | ||
| 13 | AIPCH13 | Consumption-Driven Intent | Built for Clear Business or Operational Purpose | ... the AI Product is explicitly defined against a business purpose or decision context, traceable to consumer needs. | 4% | 0 | ||
| 14 | AIPCH14 | Testable & Versioned | Version-Controlled and Behavior-Tested | ... the AI Product evolves through versioned releases with reproducible tests validating behavior and outcomes. | 5% | 0 | ||
| 15 | AIPCH15 | Economically Accountable | Tracks Cost, Usage, and Value Metrics | ... cost, usage, and value contribution of the AI Product are measurable and attributable across its lifecycle. | 4% | 0 | ||
| 16 | AIPCH16 | Explainable & Transparent | Provides Interpretable Behavior and Decision Transparency | ... the AI Product provides interpretable explanations of its behavior, decisions, or actions appropriate to its risk level. | 5% | 0 | ||
| 17 | AIPCH17 | Bias-Controlled & Fairness-Measured | Bias and Fairness Continuously Measured and Mitigated | ... fairness metrics are continuously evaluated and mitigation mechanisms are applied where required. | 5% | 0 | ||
| 18 | AIPCH18 | Continually Learnable (Retraining Ready) | Supports Continuous Learning and Adaptation | ... the AI Product supports continuous improvement through retraining, prompt updates, or behavioral tuning with feedback loops. | 4% | 0 | ||
| 19 | AIPCH19 | Safe & Policy-Bound Usage | Usage Boundaries and Safety Controls Enforced | ... the AI Product enforces defined usage boundaries, detects misuse, and applies safety controls automatically. | 5% | 0 | ||
| 20 | AIPCH20 | Interoperable & Composable | Supports Composition with AI Products and Data Products | ... the AI Product can be composed with other AI or Data Products via standardized ports and orchestration mechanisms. | 4% | 0 | ||
| 21 | AIPCH21 | Human-Centered Oversight | Supports Human-in-the-Loop Review and Override | ... the AI Product enables human intervention, escalation, and override for critical decisions. | 5% | 0 | ||
| TOTAL | 0.0 | |||||||
🎯 Prioritized Recommendations
High Priority — Not Met
- AIPCH01 (8%): Assign an accountable AIPRO and register ownership, domain, and lifecycle metadata in the registry.
- AIPCH02 (6%): Provide intent-driven self-service pipelines; ensure one-click publish, rollback, and environment parity.
- AIPCH03 (5%): Adopt AIPDS; validate in CI; generate infrastructure, policies, and interfaces from spec.
- AIPCH04 (5%): Enable intent-driven composition interfaces; eliminate manual pipeline/model engineering; rely on platform compilation.
- AIPCH07 (5%): Expose trust metrics (quality, drift, risk tier); publish evaluation history and trust score.
- AIPCH09 (5%): Define SLIs/SLOs; implement dashboards, alerts, and runbooks; track behavioral metrics.
- AIPCH10 (5%): Implement policy fabric (privacy, fairness, access); enforce via runtime controls and audit logs.
- AIPCH14 (5%): Implement evaluation pipelines; version artifacts; enforce regression testing and changelogs.
- AIPCH16 (5%): Expose explanation interfaces; provide reasoning traces or feature attribution where applicable.
- AIPCH17 (5%): Run fairness audits; monitor subgroup metrics; publish bias reports.
- AIPCH19 (5%): Define prohibited use; implement safety filters; log violations and overrides.
- AIPCH21 (5%): Implement review workflows; capture overrides; maintain audit trails and escalation paths.
- AIPCH05 (4%): Register in catalog; include capability type, domain, trust signals, and usage examples.
- AIPCH06 (4%): Publish capability descriptors; include IO schema, behavior summary, constraints, and dependency graph.
- AIPCH08 (4%): Design stable interfaces; document reuse patterns; provide adapters and examples.
- AIPCH11 (4%): Define standard ports; publish API contracts; separate product from UI/experience layers.
- AIPCH12 (4%): Map schemas to ontology; use canonical terms and identifiers; link to semantic registry.
- AIPCH13 (4%): Define use cases, KPIs, and acceptance criteria; document consumer scenarios.
- AIPCH15 (4%): Integrate FinOps; track cost per invocation; enable showback/chargeback models.
- AIPCH18 (4%): Implement drift detection; capture feedback; automate retraining or adaptation pipelines.
- AIPCH20 (4%): Ensure composable interfaces; support orchestration frameworks; expose dependency graph.
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.