Policy vs Practice: Why AI Governance Fails Without Engineering Rigor
- Nikita Silaech
- Oct 6
- 3 min read
Everyone talks about AI governance. Policies, principles, rules—you see them in decks, in frameworks, everywhere. But here’s the thing: a lot of the time, these policies don’t make AI safer or fairer in the real world. They exist on paper, not in the systems we actually build.
The gap between policy and practice isn’t some abstract problem. It comes down to engineering.
Policies Are Easy. Doing Them Is Hard
Writing guidelines like “ensure fairness” or “be transparent” is easy. Following through is not. Policies alone don’t change behaviour, and they don’t prevent mistakes.
Some common problems:
Rules are vague. How do you measure “fair” or “transparent” in a real system?
Teams don’t have the tools to enforce them.
Governance is often detached from the actual work of building and running AI.
Take a real example: a company wrote a rule saying “AI should not discriminate.” Sounds good, right? But when a recruitment model started favouring male candidates, no one had set up tests to check for bias in resumes. The policy existed, but the system wasn’t enforcing it. The result? Bias slipped through unnoticed.
Engineering Makes Governance Real
If you want policies to matter, you have to treat them like engineering problems. That means building systems and processes that actually enforce them. Here’s what that looks like:
Automate checks: Don’t rely on people to spot bias or unsafe behaviour. Build tests and pipelines that catch issues before models go live.
Example: A health AI team added automated tests to flag any predictions that systematically undiagnosed certain age groups. Problems were caught before impacting patients.
Track models and data: Keep records of what data went into a model and which version is running. If something goes wrong, you can trace it back and fix it.
Example: An e-commerce company discovered a recommendation model started favouring one region’s products over another. Version tracking allowed them to quickly roll back to a fairer model.
Test continuously: AI outputs can be unpredictable. Run unit tests, scenario tests, and monitor models once they’re live.
Example: A chatbot team tested not just one-off responses but full conversations. They spotted a pattern where the bot gave inconsistent advice in multi-turn conversations, which they could then fix.
Learn and iterate: Real-world use exposes blind spots. Capture those failures, learn from them, and update both policies and engineering practices. Example: A credit scoring AI initially passed all fairness tests. But after deployment, unusual patterns in a small demographic revealed gaps in the tests. The team updated both their policies and monitoring to catch similar issues in the future.
Why Governance Often Breaks
Even with the best intentions, governance can fail. Here’s why:
Treating policies as checklists. You think you’re “doing governance,” but no one is checking whether it actually works.
Weak collaboration between ethics, legal, and engineering. Policies get written, but engineers don’t know how to apply them.
Ignoring model drift and data changes. A model that worked last month may behave differently today.
Relying too much on manual review. It’s slow, inconsistent, and impossible to scale.
Think of it like having a fire alarm but no sprinkler system. You can see the smoke, but the problem keeps spreading because the system isn’t set up to respond automatically.
Making Governance Work
The key is simple: build governance into your workflow. Don’t separate it from engineering. Make it part of how you design, test, and deploy models.
Practical steps:
Define clear rules you can measure.
Automate checks wherever possible.
Keep track of models, data, and decisions.
Monitor outputs continuously.
Use what you learn to improve both policies and engineering.
When governance is part of the system, it actually works. Policies guide you, engineering enforces the rules, and teams can be confident their AI behaves responsibly.
Policies without engineering are empty. Engineering without policies is risky. Put them together and you get AI that’s accountable, reliable, and trustworthy in practice—not just on paper.
For teams building AI, the takeaway is this: treat governance like part of your product. Plan it, test it, enforce it, and keep improving it. That’s the only way it actually works.