AI Governance Day One
When we built an AI system to support pre-authorization decisions for a healthcare provider, governance wasn't a phase two checklist. It was part of the first release.
Design for explainability
Every recommendation was tied to a set of contributing factors that clinicians could review. We built guardrails so the system would defer to human judgment when confidence was below a threshold or when the case fell outside the distribution of training data. Versioning and audit trails were in place from sprint one, which made it possible for the client to get internal sign-off and run a controlled rollout.
What we learned
- Success depended as much on process and governance as on the model itself.
- Weekly reviews with both technical and business owners kept everyone aligned.
- Documenting assumptions about data quality, labelling, and edge cases meant we could trace issues quickly when something went wrong.
- The first pilot was designed to learn: we had clear criteria for when to scale up and when to pause and adjust.
Today the system handles a substantial share of pre-auth decisions in that region. The client has replicated the approach in other lines of business. If you're considering AI for operational or clinical workflows, we'd be glad to share how we structured this engagement.
