Why Most Companies Struggle With AI Governance (And How Two ISO Standards Solve It)

Companies use it to screen resumes, detect fraud, recommend products, answer customer questions, and even assist doctors. But while AI is becoming smarter, one question keeps bothering organizations:

"How do we make sure our AI is safe, responsible, and trustworthy?"

This is exactly why ISO/IEC 42001, the world's first AI Management System standard, was introduced. But here's something many people don't realize.

ISO 42001 tells organizations what controls they should have, but it doesn't clearly explain when each control should be applied during an AI project's journey.

That's where another standard, ISO/IEC 5338, comes in.

When you combine these two standards, you get a practical roadmap for governing AI from the moment an idea is born until the system is retired.

Think of Building AI Like Building a House

Imagine you're building a house.

You wouldn't wait until the house is finished before checking whether the foundation is strong.

You'd inspect every stage:

• Planning
• Designing
• Construction
• Inspection
• Living in it
• Maintenance
• Demolition

AI works the same way.

Instead of checking everything at the end, governance should exist throughout the entire lifecycle.

ISO 5338 divides this journey into seven stages, while ISO 42001 explains which governance controls belong at each stage.

Stage 1: Before You Build Anything

Every AI project starts with an idea.

Before collecting data or training a model, organizations should ask questions like:

• Why are we building this?
• Who will use it?
• Could it negatively affect someone?
• Are we using trustworthy data and vendors?

This early planning stage prevents many problems before they become expensive mistakes.

Stage 2: Designing the AI

Now the real work begins. Developers collect data, choose algorithms, and train models. But good AI isn't just about writing code. Organizations should also document:

• Where the data came from
• Whether the data is biased
• Who is responsible for the system
• Which resources are being used

If the data is poor, even the smartest AI will make poor decisions.

Stage 3: Testing Before Launch

Would you buy a car that was never tested? Probably not.

The same applies to AI. Before releasing an AI system, organizations should verify:

• Does it work correctly?
• Is it accurate?
• Can humans override it if necessary?
• Can we explain its decisions?

Testing isn't just about finding bugs. It's about building trust.

Stage 4: Going Live

Deploying an AI system isn't the finish line.It's just the beginning.

At this stage, organizations should:

• Keep audit logs
• Tell users they're interacting with AI
• Clearly explain what the system can and cannot do

Transparency helps build user confidence and makes future investigations much easier.

Stage 5: Watching the AI

AI doesn't stop working after deployment. It continues making decisions every day. That's why organizations should continuously monitor:

• Performance
• Errors
• Unexpected behavior
• Misuse

Think of it like regularly servicing a car instead of waiting for it to break down.

Stage 6: Checking That It Still Works

The world changes. Customer behavior changes. Data changes.

Eventually, AI models may start making worse decisions than they did when they were first deployed.

This is called model drift.

Organizations should regularly review their AI systems to ensure they remain accurate, fair, and aligned with business goals.

Stage 7: Saying Goodbye

Every AI system has an end. When that happens, organizations shouldn't simply switch it off.

They should:

• Safely delete data
• Inform affected users
• Update documentation
• Record lessons learned

Even retirement is part of responsible AI governance.

The Big Lesson

One thing stood out to me while studying these standards. AI governance isn't something you add after building AI.

It should be built into every step of the AI lifecycle.

That's exactly what happens when ISO 42001 and ISO 5338 are used together. One standard explains what needs to be governed. The other explains when those governance activities should happen.

Together, they help organizations build AI that isn't just intelligent — but also responsible, transparent, and trustworthy.