The “Three Fences” Model For Responsible, Scalable AI
Most teams treat AI governance like a brake pedal.
Legalese.
Red tape.
A reason to move slower.
That mindset kills momentum.
Good governance is a growth lever. It removes hesitation, reduces rework, and lets your team ship with confidence. My “Three Fences” model gives you just enough structure to move fast and stay safe.
Here’s how it works - and how to put it in place this month.
Why governance first (if you want speed)
AI fails inside organizations for three predictable reasons:
Fear (“Are we allowed to use this?”)
Rewrites (“We can’t publish this - where are the sources?”)
Bottlenecks (“Only two people are ‘approved’ to try AI.”)
A small, explicit governance layer answers those questions upfront. That clarity is what creates speed. It’s the opposite of bureaucracy. Think guardrails on a racetrack, not pylons in a parking lot.
Fence 1: Data handling
Purpose: Protect customers, protect the business, and make “what’s in/out” obvious.
Decide and document:
What’s approved: public data, anonymized internal docs, approved knowledge bases.
What’s restricted: client-confidential files, contracts, unreleased IP.
Where tools sit: vendor tiers (e.g., enterprise instances vs. public tools), retention defaults, encryption
Submission rules: redaction steps, disposal timelines, who can escalate exceptions.
Quick win: Publish a one-page “Gree /Yellow/Red Data” chart. If a file lands in Yellow, list the redaction step required to move it to Green.
Metric: Policy exceptions per month ↓; time-to-yes on typical tasks ↓.
Fence 2: Quality control
Purpose: Stop “AI slop”. Make credibility the default, not a heroic act.
Bake in by default:
Citations on: models must return sources or clearly mark speculation.
Fact-check prompts: a standard “cross-check against source list” step.
Critique before publish: a second-pass prompt that challenges bias, hallucinations, and tone.
Human in the loop: named reviewers for each asset class (e.g., legal, medical, financial, regulated claims).
Quick win: Create a 7-point AI Output Review Checklist (sources, dates, claims, bias scan, voice, accessibility, compliance notes). Attach it to every brief.
Metric: First-pass acceptance rate ↑; rewrite hours per asset ↓; flagged issues caught upstream ↑.
Fence 3: Access and Inclusion
Purpose: If only a few “AI champions” get training, you’ve created fragility. And inequity.
Operationalise access:
Who gets trained: not just leaders; give every role a relevant path.
Role-based enablement: researcher, drafter, QA, repurposer—each with examples and prompts.
Accessibility: captioning and alt text defaults; plain-language summaries of technical outputs.
Bias checks: require diverse test audiences for customer-facing content (age, language, geography, ability).
Quick win: Run a 90-minute enablement sprint for one team. Provide a role card, three prompts, one exemplar, and the review checklist. Ship something real before the session ends.
Metric: Seats trained ↑; usage spread across roles ↑; support tickets about “can I do X?” ↓.
Putting it together in a week
Day 1-2: Draft the one-pagers
“Green/Yellow/Red Data”
AI Output Review Checklist
Role cards (what “good” looks like for researcher/drafter/QA/repurposer)
Day 3: Tool tiers and defaults
Which tools for which jobs; citation and retention defaults set to “on.”
Day 4: Pilot on one real deliverable
Use your checklist. Capture edge cases. Annotate one “gold-standard” exemplar.
Day 5: Retro and publish
What slowed you down? Fix it. Pin the three one-pagers in your knowledge hub.
Now you have governance you can explain in three minutes. More importantly, you have permission to move.
Common traps (and how to avoid them)
Exec-only enablement: You get policy without practice. Fix by training the doers first.
Tool worship: Governance is vendor-agnostic. Start with decisions, not logos.
Paper policies: If your checklist isn’t attached to the brief, it won’t be used. Embed it.
What changes when the fences are up
Teams stop asking “Can I?” and start asking “How fast?”
Leaders sign off faster because the evidence is baked in.
Quality rises because critique happens before human edit, not after launch-day panic.
Inclusion improves because more people have the training—and the language—to participate.
That’s the paradox: the moment you add just-enough structure, creative speed returns.
Ready to install the three fences?
This is the governance we implement with clients before any shiny pilot.
It’s small.
It’s teachable.
It scales.
If you want scaffolding, templates, and live feedback while you deploy it, join my Marketing Power Circle (MPC) - the AI implementation mastermind for founders, consultants, and in-house leaders who want results, not rhetoric. We build the fences, design the workflows, and ship together.
