Why AI Governance Can't Wait Until Launch

Last month, a major UK bank had to pull its AI-powered credit decision tool after discovering it systematically disadvantaged certain customer segments. The issue? They built governance processes after the model was live.

This is a pattern I see repeatedly: teams rush to deploy AI, then scramble to add compliance, monitoring, and controls later. It never goes well.

The "We'll Fix It Later" Problem

Here's the typical timeline:

1. Month 1-3: Build and train the model
2. Month 4: Deploy to production
3. Month 5: Realize you need model cards, audit logs, bias testing
4. Month 6-9: Retrofit governance while the system is live
5. Month 10: Get flagged by internal audit or regulators

By the time you're adding governance, you're operating with blind spots. You don't know what decisions the model made, why it made them, or whether they harmed anyone.

Why "Bolting On" Governance Fails

1. Missing Data

Once a model is live without proper logging, you can't retroactively capture:

• Which data points influenced each decision
• Confidence scores and edge cases
• User interactions and feedback
• Model drift over time

2. Architectural Constraints

Governance needs to be built into your system architecture:

• Real-time monitoring hooks
• Explainability layers
• Human-in-the-loop workflows
• Rollback mechanisms

Adding these after launch means re-architecting while users depend on the system.

3. Regulatory Risk

Regulators don't accept "we're working on it." Under the EU AI Act and FCA guidelines, you need governance from day one. Deploying without it puts you in violation immediately.

4. Loss of Trust

If your model makes a bad decision and you can't explain why, you've lost customer and stakeholder trust. "We're investigating" isn't good enough.

The Right Approach: Governance-First

Here's what works:

Before You Write Code

Define your use case and risks:

• What decision is the AI making?
• What could go wrong?
• Who could be harmed?

Set success criteria:

• Accuracy thresholds
• Fairness metrics
• Latency requirements

Plan your governance:

• What will you log?
• How will you monitor?
• Who oversees the model?

During Development

Build governance into your pipeline:

• Log training data provenance
• Track model versions and experiments
• Test for bias at every stage
• Document decisions and trade-offs

Create transparency artifacts:

• Model cards
• Data sheets
• User-facing explanations

Before Launch

Run a governance checklist:

• ✅ Monitoring dashboards live
• ✅ Alert thresholds configured
• ✅ Human oversight process defined
• ✅ Incident response plan documented
• ✅ User transparency mechanisms active
• ✅ Regulatory requirements met

Conduct a pre-launch review:

• Internal audit sign-off
• Legal review
• Ethics board approval (if applicable)

After Launch

Operate with discipline:

• Daily monitoring reviews
• Weekly performance reports
• Monthly governance committee meetings
• Quarterly external audits

Real-World Example: Done Right

A European insurance company came to us wanting to deploy AI for claims triage. Instead of rushing to build, we spent the first month on governance design:

• Defined risk appetite and fairness criteria
• Mapped regulatory requirements (EU AI Act, local insurance regs)
• Designed logging and monitoring infrastructure
• Built explainability into the model architecture

When we launched 12 weeks later, we had:

• Full audit trails from day one
• Real-time bias monitoring
• Automated alerts for edge cases
• Clear explanations for every claim decision

Six months in, the regulator audited them. The audit took two days instead of the usual two weeks, because everything was documented and traceable.

The Bottom Line

Governance isn't overhead—it's how you ship AI that lasts.

If you're building AI for financial services, you have two choices:

1. Build governance from the start → deploy with confidence
2. Add it later → deploy with risk and rework

The first approach is faster, cheaper, and safer.

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Next Steps

Want to build governance-first AI? Book a free consultation to discuss your project, or read our complete guide to EU AI Act compliance.

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Lewis Cross is Senior Manager, AI & Data at Amaris Consulting, where he helps financial services companies deploy AI systems with built-in governance and compliance.