The situation
A tier-1 bank was sitting on twelve production-ready models — credit risk, fraud detection, customer churn — that could not be deployed because the review process took an average of fourteen weeks per model. The bottleneck was not technical. It was a governance structure designed for one model per quarter, not twelve per year.
What we found
The existing review process required sign-off from seven separate committees, many of which had overlapping mandates and no shared documentation standard. Model cards were written differently by every team. Reviewers were re-reading context they had already approved in a prior stage.
The root cause was a process built for compliance theatre rather than risk management. Every committee added latency without adding independent scrutiny.
What we did
We ran a three-week diagnostic that mapped every approval step, identified who was adding new information versus rubber-stamping prior decisions, and quantified the delay contribution of each stage.
The outcome was a restructured governance framework with:
- Two-tier review: a technical tier (data lineage, performance benchmarks, monitoring hooks) and a business tier (use-case approval, escalation thresholds, override policy)
- Standardised model card adopted across all teams — eight fields, no more
- Pre-approval track for model updates that fall within a defined performance envelope
- Single audit log visible to all reviewers simultaneously, eliminating the sequential hand-off
The result
The average review cycle dropped from fourteen weeks to three. The twelve backlogged models deployed within sixty days of framework adoption. Risk exposure, measured by the number of model incidents requiring human escalation, held flat in the following two quarters.
The bank's head of model risk described it as "the first time governance has felt like infrastructure rather than overhead."
What this means for your organisation
If your deployment backlog is growing faster than your review capacity, the answer is rarely more reviewers. It is a process redesign. The patterns that slow down AI governance are well-understood and correctable without weakening risk controls.