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AI Asset vs AI Product

In the AI ecosystem, it is critical to distinguish between an AI Asset (see Glossary) and an AI Product (see Glossary) .
This distinction is not about abstraction level, but about the presence (or absence) of key product characteristics.

AI Asset​

  • A building block in the AI lifecycle.
  • Examples: pretrained model weights, datasets, embeddings, prompts, feature stores, rulesets.
  • Assets are valuable, but they are not yet packaged for consumption.
  • They lack one or more of the true AI Product characteristics.

AI Product​

  • A packaged, consumable unit of AI capability.
  • Examples: an LLM API, an image recognition service, an autonomous agent.
  • Important: Not all “APIs” or “services” qualify.
    • An LLM API without governance, prohibited use declarations, or monitoring is still an AI Asset, not a Product.
  • A true AI Product must satisfy:
    • The inherited Base Product characteristics, and
    • The AI-specific characteristics defined in What is an AI Product?.

Key Principle​

The distinction between AI Asset and AI Product comes down to characteristics, not the form it takes.

  • A raw model checkpoint can become an AI Product if it is wrapped with metadata, governance, observability, and deployment interfaces.
  • A polished API may still only be an Asset if it fails to meet product criteria.

Why the Distinction Matters​

  • Prevents mislabeling assets as products.
  • Sets a clear bar for what qualifies as a product under AIPS.
  • Protects consumers from assuming trust, safety, or governance where none exists.
  • Ensures AIPS aligns with the BPS principle that a Product = unit of value + accountability, not just a technical wrapper.