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Architecture Alignment

AI Products do not exist in isolation.
They must align with enterprise, platform, and ecosystem architectures, so they can be adopted, governed, and integrated at scale.

Relationship to the Base Product Specification (BPS)​

  • BPS defines the meta-characteristics of any Product (identity, purpose, deployability, governance, discoverability, etc.).
  • AIPS specializes these principles for AI-specific needs (capability type, risk classification, drift monitoring, explainability).
  • This ensures AI Products remain consistent with other product types (e.g., Data Products, API Products, Physical Products).

Relationship to Data Products​

  • Complementary, not competing:
    • Data Products are often inputs (training sets, feature stores).
    • Data Products can also be outputs (embeddings, generated datasets).
  • AI Products require stronger lifecycle controls (drift, retraining, bias monitoring).
  • In enterprise catalogs, AI Products and Data Products co-exist under a common product framework but with distinct attributes.

Role in Enterprise Architecture​

  • AI Products must be modeled as first-class citizens in enterprise architecture repositories.
  • They integrate with:
    • Data Platforms → as consumers (for training) and producers (for embeddings).
    • Application Platforms → as capabilities exposed via APIs or services.
    • Governance Frameworks → for risk, compliance, and prohibited use enforcement.
    • Observability Platforms → for monitoring health, drift, and fairness.

Alignment with Platform Architectures​

  • AI Products in Cloud-Native Environments

    • Deployable as microservices, APIs, or containers.
    • Observable through standard telemetry (metrics, logs, traces) plus AI-specific signals.

  • AI Products in On-Prem / Hybrid Environments

    • Must declare deployment targets and resource requirements (GPU, TPU, edge device).
    • Align with existing IT/ops governance.

  • AI Products in Ecosystem / Marketplaces

    • Discoverable in registries and catalogs.
    • Carry standard metadata for interoperability.
    • Governed by shared compliance frameworks.

Alignment Across Time Horizons​

  • Today: Narrow AI Products (LLMs, vision models, task-specific agents) aligning with enterprise platforms.
  • Near Future: Multi-agent systems and hybrid AI, requiring new governance and integration patterns.
  • Long Term: AGI/ASI, requiring global governance architectures and integration with societal-scale platforms.

Summary​

AI Products must align with architecture at three levels:

  1. BPS Meta-Framework → inherit universal product characteristics.
  2. Enterprise & Platform Architecture → integrate with data, app, and governance platforms.
  3. Future Ecosystem Architecture → anticipate AGI/ASI governance and global coordination.

This ensures AI Products are not isolated experiments, but trusted, interoperable building blocks of the enterprise and society.