AI governance / vendor & model risk
The model is a dependency.
Not the operating system.
A production workflow should survive model changes, provider outages, cost changes, policy changes, and vendor failures. Bridge Road separates the business process from the model so the company keeps control of its data, rules, approvals, and fallback path.
What we evaluate before production use.
Data boundary
What information is sent, retained, logged, used for training, routed through third parties, or exposed to administrators.
Permission boundary
Whether the model can only advise, can prepare actions, or can execute writes—and which systems and records it can access.
Model change risk
Version changes, behavior drift, deprecated APIs, new limits, pricing changes, and the ability to pin or test before upgrading.
Operational resilience
Timeouts, retries, rate limits, failover, manual fallback, replay protection, queue handling, and what happens when the provider is unavailable.
Evidence and logging
Whether the workflow can retain the source, decision context, model output, approval, and final action without exposing sensitive reasoning or credentials.
Cost visibility
Model/API usage, third-party automation charges, integration overhead, monitoring cost, and a threshold for when a cheaper deterministic rule should replace AI.
Current-events implication
When AI labs debate pacing and safety, buyers should reduce provider concentration risk.
The useful enterprise response is not to stop using AI. It is to avoid designing critical operations around one opaque dependency. Keep business rules outside the model, preserve human authority, test model upgrades, and maintain a documented fallback.