Every enterprise AI discussion eventually arrives at governance. How do we ensure AI is used responsibly? How do we manage risk? How do we maintain oversight as AI proliferates?

Then the conversation usually stalls. Governance sounds important but feels bureaucratic. Nobody wants to create a compliance monster that slows everything down.

Here’s how to build governance that actually works.

Why Governance Matters Now

A few years ago, AI was contained in data science teams doing isolated projects. Governance could be informal.

That world is gone. AI is now:

• Embedded in productivity tools everyone uses
• Making recommendations that affect customers
• Generating content that represents your brand
• Processing data subject to privacy regulations
• Making decisions that require explanation

Without governance, you’re accumulating risk invisibly. The question isn’t whether you need governance but how to do it without creating bureaucratic paralysis.

The Principles-Based Approach

Detailed rules don’t work for fast-moving technology. By the time you write rules for specific AI capabilities, those capabilities have evolved.

Instead, start with principles that guide decision-making:

Example principles:

1. Human oversight: AI assists decisions but doesn’t make consequential decisions autonomously.

2. Transparency: We can explain how AI-generated outputs were produced.

3. Data minimisation: We use only the data necessary for the specific AI application.

4. Bias awareness: We assess AI outputs for unfair bias before deployment.

5. Continuous monitoring: We track AI performance and address degradation or drift.

Principles like these provide guidance without prescribing specific implementations. Teams can apply them to their specific contexts.

The Governance Structure

Someone needs to own governance. Options:

Centralised AI governance team: A dedicated team that reviews all AI initiatives.

• Pros: Consistency, expertise accumulation
• Cons: Bottleneck, disconnected from business context

Distributed ownership with coordination: Business units govern their own AI with central standards.

• Pros: Speed, business relevance
• Cons: Inconsistency, expertise dilution

Hybrid model: Tiered approach where high-risk AI requires central review, lower-risk can proceed with self-certification.

• Pros: Balances speed with control
• Cons: Requires clear risk criteria

Most mid-large enterprises land on the hybrid model. It’s not perfect, but it’s practical.

Risk Tiering

Not all AI needs the same oversight. A risk-based approach:

High risk (full review required):

• AI making decisions about people (hiring, lending, pricing)
• AI in regulated processes
• AI handling sensitive personal data
• Customer-facing AI that could cause harm

Medium risk (self-certification with spot checks):

• Internal productivity AI
• AI-assisted (not autonomous) decisions
• Content generation for internal use

Low risk (proceed with documentation):

• AI tools used individually
• Development/testing environments
• Non-production experiments

Define these tiers clearly. Provide examples. Make it easy for teams to self-assess.

The Governance Process

For high-risk AI, a review process might look like:

1. Application submission: Project team documents the AI use case, data sources, intended use, and risk assessment.

2. Initial triage: Governance team confirms risk tier and required reviews (privacy, legal, technical, business).

3. Specialist reviews: As needed based on risk factors.

4. Approval or conditions: Go-ahead, go-ahead with conditions, or rejection with rationale.

5. Post-deployment monitoring: Ongoing checks that approved AI operates as intended.

The process should be fast for genuinely low-risk applications. Days, not months.

Documentation Requirements

Governance requires documentation, but documentation shouldn’t be a burden. Minimum viable documentation:

• What the AI does: Plain language description
• What data it uses: Sources, types, consent basis
• Who’s responsible: Business owner and technical owner
• How it’s monitored: Metrics tracked, alert thresholds
• What could go wrong: Known risks and mitigations

This should fit on two pages. If you’re requiring 50-page documents, you’re over-engineering.

Common Pitfalls

Making governance optional: If people can skip it, they will. Build governance into project processes so it’s the path of least resistance.

Reviewing everything: You don’t have capacity to review every AI use. Tier risks and focus attention where it matters.

Being the department of “no”: Governance that only blocks things gets routed around. Provide constructive guidance, not just rejection.

Ignoring embedded AI: Microsoft Copilot, Salesforce Einstein, AI consultants Sydney at Team400, and similar embedded AI needs governance too. It’s easy to overlook because you didn’t “build” it.

Treating governance as one-time: AI systems change. Data patterns shift. Governance is ongoing, not a gate you pass once.

Not measuring: Track metrics like time to approval, number of projects reviewed, issues caught, and issues missed. Without measurement, you can’t improve.

Making It Work Culturally

Governance only works if people actually follow it. Cultural factors that help:

Leadership modelling: When senior leaders submit their AI initiatives for review, it signals governance isn’t optional.

Fast feedback: If governance takes weeks, people avoid it. Aim for days.

Useful output: Governance reviews should add value, not just approve or deny. Teams should leave with better approaches.

Continuous improvement: Regularly update governance based on what’s working and what’s not. Involve practitioners in updates.

Communication: People need to understand why governance exists and what it prevents. Share (anonymised) examples of risks caught.

Starting Point

If you’re building governance from scratch:

1. Define 3-5 principles – get executive endorsement
2. Create risk tiers – with clear criteria and examples
3. Document a simple process – application, review, approval
4. Assign ownership – someone needs to be accountable
5. Pilot with willing teams – learn and adjust before mandating
6. Roll out and iterate – governance evolves with the technology

You don’t need perfect governance to start. You need good-enough governance that you improve over time.

Final Thought

AI governance is a balancing act. Too little, and you accumulate risk. Too much, and you create bureaucracy that people circumvent.

The goal is governance that enables responsible AI adoption, not governance that prevents AI adoption. It should feel like a reasonable checkpoint, not an obstacle course.

Get the balance right and governance becomes an enabler. Get it wrong and it becomes either theatre or blockage.

Neither extreme serves the organisation.