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What is an AI Product?

An AI Product is a packaged, consumable unit of AI capability that is designed, deployed, and governed for self-service use.
It transforms AI from being a raw asset (like model weights, training data, or prompts) into a product with clear purpose, interfaces, and responsibilities.

AI Asset vs AI Product​

  • AI Asset
    A component of the AI lifecycle: datasets, pretrained weights, prompts, embeddings, feature stores, or rulesets.
    Assets are building blocks but not self-sufficient. They require additional packaging, context, and governance before they can be consumed externally.

  • AI Product
    A self-contained and productized form of AI capability: a model API, an agent system, a decisioning engine, or even a future AGI service.
    Products are discoverable, governed, observable, and consumable without bespoke engineering.

Characteristics of a True AI Product​

An AI Product inherits the core characteristics of any Product (as defined in the Base Product Specification) and introduces AI-specific characteristics.

1. Inherited from Base Product​

  1. Identity & Ownership
  2. Purpose & Consumption Intent
  3. Deployable / Usable
  4. Self-Describable
  5. Governed & Trustworthy
  6. Discoverable
  7. Observable
  8. Evolvable
  9. Economic Model
  10. Composability

2. Additional AI-Specific Characteristics​

  1. Capability Type (see Glossary)
  2. Explainability & Transparency (see Glossary)
  3. Fairness & Bias Metrics
  4. Risk Classification (see Glossary)
  5. Prohibited Uses (see Glossary)
  6. Drift & Continuous Learning (see Glossary)
  7. Autonomy & Actionability (see Glossary)
  8. Energy & Carbon Footprint

Important Note on Completeness​

If these characteristics are absent or unimplemented, then the entity cannot be considered a true AI Product.
It may remain an AI Asset (a useful building block) but not a productized unit of value in the sense required by AIPS.

This distinction applies regardless of abstraction level:

  • A raw model checkpoint can become an AI Product if wrapped with the required metadata, governance, and deployment interfaces.
  • An LLM API or service may still be only an Asset if it lacks governance, risk classification, or observability.

Productness is earned, not assumed.

Formal Definition​

AI Product (AIPS):
A packaged, self-service unit of AI capability — such as a model, agent, or system — that inherits the universal characteristics of a Product (as per BPS) and extends them with AI-specific attributes such as capability type, fairness, risk classification, and explainability, to ensure responsible and trustworthy use.

Examples​

  • Today:

    • Large Language Model API (text-to-text).
    • Vision model deployed on edge hardware.
    • Multi-agent workflow coordinator.

  • Tomorrow:

    • Artificial General Intelligence (AGI) service with broad capabilities.
    • Neuro-symbolic hybrid decisioning engine.
    • Societal-scale AI systems requiring global governance.

This ensures AIPS is broad enough to cover current AI Products (models, agents, APIs) while being future-proof for emerging paradigms such as AGI or ASI.