Every board wants generative AI on the roadmap. Every compliance team wants proof it won't hallucinate its way into a regulatory finding. Both are right — and the organizations winning with AI in banking, insurance, and healthcare are the ones that treat those two pressures as design inputs rather than a stalemate.
After shipping LLM-powered features inside regulated environments for two years, we've settled on a practical framework with four pillars.
1. Tier every use case by blast radius
Not all AI features carry the same risk. We classify each use case before writing a line of code:
- Tier 1 — internal drafts: summaries, search, and knowledge assistants whose output a professional reviews before it goes anywhere
- Tier 2 — customer-adjacent: agent-assist suggestions, document extraction, and classification feeding human workflows
- Tier 3 — customer-facing: anything a client sees directly; this tier earns the heaviest controls or gets redesigned into Tier 2
2. Keep a human in the loop where it counts
Human review is not a checkbox — it's an interface design problem. Reviewers need to see the source passages behind every generated claim, edit output in place, and reject with one click. If reviewing takes longer than doing the task manually, your AI feature is a liability with a nice demo.
3. Evaluate like you test code
We maintain evaluation suites the same way we maintain unit tests: golden datasets per use case, automated scoring on every model or prompt change, and hard release gates. A model upgrade that improves average quality but fails a compliance-critical case does not ship.
The question regulators ask is never "did you use AI?" — it's "can you show me how you control it?" An evaluation trail is the most convincing answer we've found.
4. Govern data before it reaches the model
PII redaction, data residency, and retention policies belong in the pipeline before any prompt is assembled. We standardize on a gateway pattern: one audited service through which every AI call flows, enforcing masking, logging, and rate controls centrally instead of per-team.
Start small, but start with the framework
The teams that struggle are the ones that pilot casually and then try to retrofit governance. Stand up the gateway, the tiers, and the eval harness on your very first use case — they cost little at small scale and they're what lets you say yes to the next ten ideas quickly.
