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Guiding Principles for AI Products

AI Products extend the Base Product Specification (BPS) into the AI domain.
To ensure they deliver value responsibly and sustainably, AIPS is founded on a set of guiding principles.

1. Self-Service (Load-Bearing Principle)​

Every AI Product must be consumable in a self-service manner.

Self-service is the foundation of product thinking (see Glossary).

Consumers — whether humans, systems, or AI agents — must be able to discover, evaluate, and use an AI Product without bespoke arrangements or privileged knowledge.

This principle ensures AI Products are scalable, reusable, and democratized.

2. Trustworthiness​

AI Products must be designed for responsible and ethical use.

  • Transparent about capabilities and limitations.
  • Include risk classification and prohibited use declarations.
  • Provide explainability signals to foster trust.

3. Discoverability​

AI Products must be easily findable and indexable.

  • Registered in catalogs or marketplaces.
  • Searchable by purpose, capability type, and risk class.
  • Support metadata standards to ensure interoperability.

4. Observability & Evolvability​

AI Products are living systems.
They must:

  • Provide observability signals (performance, drift, bias).
  • Support continuous monitoring, retraining, and versioning.
  • Ensure consumers are notified of changes or deprecations.

5. Governance & Accountability​

AI Products must declare:

  • Ownership & stewardship — who is accountable.
  • Compliance obligations — legal, ethical, regulatory.
  • Economic model — costs, licensing, and resource footprint.

Governance ensures products remain aligned with societal expectations and enterprise policies.

6. Composability​

AI Products should be modular and interoperable, so they can be:

  • Combined with Data Products (e.g., for training, enrichment).
  • Orchestrated into multi-agent workflows.
  • Extended without breaking consumers.

7. Future-Proofing​

AI Products must be described in a way that remains valid for emerging paradigms such as:

  • Neuro-symbolic systems.
  • Hybrid AI + human workflows.
  • AGI and ASI scenarios.

This requires meta-level concepts (capability, risk, governance) that will outlast specific technologies.

Summary​

  • Self-Service is the load-bearing principle.
  • Trustworthiness, Discoverability, Observability, Governance, Composability, and Future-Proofing are essential supporting principles.

Together, they ensure AI Products are not just technically deployable but responsible, consumable, and sustainable.