Central Platform, Distributed Product Teams

Large organizations benefit from a shared AI platform layer with reusable controls, while product teams own domain-specific prompts and workflows. Without this split you get 14 teams solving IAM, guardrails, and observability 14 different ways - or one bottleneck team approving every prompt edit.

The Responsibility Split

The Paved Road, Concretely

# what a product team writes with a good platform SDK
from ai_platform import invoke

result = invoke(
    task="ticket-triage",        # routing matrix picks the model tier
    prompt_version="3.2",         # from Prompt Management
    context={"ticket": text},
)   # tracing, guardrails, token metering, retries: all inherited

Adoption comes from the paved road being genuinely easier than DIY - mandates without convenience breed shadow AI on personal API keys, outside every control you built.

Governance Without Bottlenecks

• Tiered review: low-risk changes (patch prompts on internal tools) auto-approve via CI gates; high-risk (new external-facing capability, regulated data) get human architecture/safety review.
• Common risk framework: one classification rubric decides which tier a change lands in - argued once, applied everywhere.
• Platform SLAs: the platform team commits to SDK support and gate latency, so product teams treat the paved road as reliable infrastructure, not an obstacle course.