Implement AI Governance Before Scale

Why early process design matters more than model selection

Here’s what happens when you skip governance: a team builds an AI system that works beautifully in testing. They push it to production. It makes a decision that costs the company $200K. Nobody knows how it made that decision. Nobody can explain it to the board. Nobody can prevent it from happening again.

Then the lawyers get involved.

Think of two hypothetical ways it can play out. Once with a lending system that rejected applicants based on a pattern the model learned that nobody intended. Second, with a content moderation system that over-corrected and took down legitimate business accounts. Both times, the technology was sound. The governance was nonexistent.

Here’s the thing: AI amplifies what you already do. If you have strong code review practices, AI-generated code gets better because reviewers catch more issues. If you have weak ones, everything gets worse because now you’re generating code faster than you can possibly validate it.

Most teams discover this too late - when they’re already at scale.

The fix is unglamorous. You need code review processes that work for AI-generated code. Different focus areas than traditional reviews, but mandatory. You need version control for models and prompts, not just code. You need validation frameworks that specify how decisions get approved before they affect customers. You need clear escalation paths so a questionable prediction goes to a human before it becomes a problem.

But here’s what actually matters: human-in-the-loop validation isn’t optional for production systems. It’s architectural. You design it in from the start or you’re bolting it on desperately later.

A fintech team I worked with learned this the hard way. They launched a system without proper escalation paths. Six months in, they realized they had no idea which decisions had human review and which didn’t. Audit trail was a mess. They had to pause the system for a month to retrofit governance. Could have been avoided with two weeks of thinking at the beginning.

The regulatory angle pushes this further. Financial services, healthcare, anything regulated - auditors want to see decision trails. They want to understand why the system did what it did. If you built governance in, you have logs and traces. If you didn’t, you’re explaining why you can’t explain anything.

Version everything. Trace everything. Validate automatically where you can. Escalate to humans where you can’t. This isn’t bureaucracy. This is the infrastructure that lets you ship confidently instead of nervously.

Start with this foundation and scale becomes manageable. Skip it and scale becomes a liability.