🎮 The Next Input — Issue #171

In partnership with

⚡ The Briefing — 60 sec

• Lovable launches its vibe-coding app on iOS and Android Waiting for the day Cursor drops their mobile app. Lovable on mobile means the “I had an idea in the Uber” crowd can now spin up working web apps from voice or text before the thought evaporates.

• ‘Scam Altman’: Elon Musk accusations open blockbuster AI trial Gloves are now off. Musk versus Altman has gone from founder lore to courtroom theatre, and the whole thing is basically an AI industry family feud with billions, control, and ego on the table.

• AI governance lagging Equivalent of Keenan Ivory Wayans screaming “MESSAGE”. This is my bread and butter. Governance is lacking but with Cylentis? Totally different story 😉

🛠️ The Playbook — The AI Governance Layer

Mission
Build a practical AI governance layer that lets teams move fast without turning every model, agent, and workflow into an unmanaged liability.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Faster adoption, fewer nasty surprises, and a cleaner path from AI experimentation to trusted operational systems.

0) Why This Matters

AI has officially moved past the cute demo phase.

Lovable going mobile shows how fast creation is getting compressed. Ideas become apps. Prompts become products. Voice notes become prototypes. That is incredible, but it also means more people can ship things before anyone has asked whether they should.

Meanwhile, the Musk and Altman trial is a reminder that AI governance is not just an internal policy PDF. It is ownership, mission, incentives, legal structure, control, and who gets to steer the thing when the stakes get stupidly high.

And then the government governance gap lands exactly where you would expect: adoption is running ahead of control.

That is the whole game now.

Not:

• “Should we use AI?”

• “Can we build with AI?”

• “Can agents do this?”

But:

• who owns the workflow?

• what can the AI access?

• what evidence supports the output?

• who can override it?

• what happens when it fails?

1) Architecture

2) Workflow

1. Create a register of every AI workflow, tool, agent, and experiment currently running in the business.

2. Assign an owner, data source, access level, and risk category to each one.

3. Separate low-risk assistive workflows from high-impact workflows involving money, customers, staff, legal, or public decisions.

4. Add evidence requirements for outputs that need factual grounding.

5. Add human review or override for anything with real-world consequences.

6. Review the register monthly and retire workflows that are unused, unsafe, or pure theatre.

3) Example Prompts

AI Use-Case Register Prompt

You are building an AI use-case register.

For the workflow below, identify:
- workflow name
- business owner
- AI tools involved
- data sources accessed
- actions the AI can take
- risk level: low, medium, or high
- required controls

Workflow:
[insert workflow here]

Governance Risk Classifier

You are classifying an AI workflow for governance risk.

Check whether the workflow involves:
- customer impact
- employee impact
- financial decisions
- legal or compliance exposure
- sensitive data
- autonomous action

Return:
1. risk level
2. reason
3. required controls
4. whether human approval is mandatory

Evidence Requirement Prompt

You are reviewing an AI output for evidence quality.

Check:
- which claims require sources
- which claims are unsupported
- whether the output is safe to use
- what evidence should be attached

Return:
approve, revise, or reject.

Override Design Prompt

You are designing a human override path for an AI workflow.

Identify:
- where the AI makes or recommends decisions
- who is affected
- where a human can intervene
- what evidence the reviewer needs
- what should happen when the AI is wrong

4) Guardrails

• No AI workflow without an owner.

• No high-impact output without review or override.

• No factual claims without evidence where trust matters.

• No agent access without permission boundaries.

• No production rollout without logging.

• No “AI strategy” that is just a list of tools.

• No governance theatre that blocks delivery without reducing risk.

5) Pilot Rollout — 3 hours

1. Pick one department already using AI in multiple informal ways.

2. Build a simple register of tools, workflows, owners, data access, and outputs.

3. Classify each workflow as low, medium, or high risk.

4. Add one missing control to the riskiest workflow: review, logging, evidence, or access limits.

5. Run 10 live examples through the improved workflow.

6. Turn the register into a monthly governance review instead of a one-off exercise.

6) Metrics

• Number of AI workflows registered

• Percentage of workflows with named owners

• Percentage of high-risk workflows with human review

• Number of outputs with evidence attached

• Permission creep incidents

• Override rate

• Number of AI workflows retired or redesigned

Pro Tip: Governance is not the thing that slows AI down. Bad governance slows AI down. Good governance lets you ship without pretending risk is imaginary.

🎯 The Arsenal — Tools & Platforms

• Airtable · simple AI use-case register for owners, risk levels, controls, and review status · Airtable

• Google Sheets · fast governance scorecard for tracking adoption, risk, and controls · Google Sheets

• Claude / ChatGPT · useful for classification, policy drafting, evidence checks, and workflow review · Anthropic · ChatGPT

• Lovable Mobile · proof that AI creation is moving closer to the moment of inspiration, which means governance has to keep up · TechCrunch

• Internal AI policy docs · boring until they are the reason your team can move faster than everyone still arguing about who owns the risk

Copy-paste prompt block:

You are helping me build an AI Governance Layer.

For the workflow below:
1. identify the business owner
2. identify the AI tools involved
3. identify what data the AI accesses
4. identify what actions the AI can take
5. classify the workflow as low, medium, or high risk
6. identify required controls: evidence, review, logging, access limits, or override
7. define the metrics to track

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- Owner
- Tool map
- Data access
- Risk classification
- Required controls
- Metrics

💡 Free Office Hours

If your organisation is adopting AI faster than it can govern it, I run free office hours to help map the workflows, tighten the controls, and turn governance from a blocker into a proper operating layer.

Book here: https://calendly.com

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🕹️ Game Over

Everyone wants the speed. Very few want the accountability layer. That is exactly where the edge is.

— Aaron Automating the boring. Amplifying the brilliant.

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