🎮 The Next Input — Issue #121

In partnership with

⚡ The Briefing — 60 sec

• OpenAI launches Prism, a new AI workspace for scientists
  Only AI could make a shape sound sexy. Jokes aside—this is OpenAI going hard at serious, technical users.

• Talking AI vegetables are teaching adults kitchen hacks they’re scared to ask
  I don’t think I’ll be learning my 日本語 from a frost-bitten cherry tomato anytime soon—but hey, whatever lowers the barrier.

• Australia’s AI boom is outpacing data skills and governance
  This is exactly the gap Cylentis AI exists to close. One shameless mention in 121 issues? I’ll allow it 😁

🛠️ The Playbook — The Applied AI Competency Stack

Mission Turn AI enthusiasm into real, governed capability by pairing tools with skills, ownership, and standards.
Difficulty Advanced
Build time 3–4 hours
ROI Moves teams from “AI experiments” to production-grade outcomes without governance debt.

0) Why This Matters

Prism shows where AI is headed: deep, technical, and domain-specific.
Meanwhile, viral veggie bots prove the opposite—AI everywhere, often without substance.

Australia’s problem isn’t access to AI. It’s competency: knowing what to build, how to run it, and who owns the risk.
This stack closes that gap.

1) Architecture

2) Workflow

1. Intake: Teams submit AI use cases with expected outcome and data sources.

2. Capability check: System matches the use case against required skills (data quality, domain knowledge, risk).

3. Build: Approved use cases move into a controlled workspace (e.g., Prism, notebooks, IDEs).

4. Review gate:

   • If exploratory → insights only.

   • If operational → require governance sign-off.

5. Deployment: Only governed workflows move to production tools.

6. Feedback loop: Outcomes logged—what worked, what broke, what to retire.

3) Example Prompts

Use-case Validator (GPT-5-mini)

Evaluate this AI use case.
Return:
- business value (low/medium/high)
- data readiness (poor/ok/strong)
- governance risk (low/medium/high)
Recommend: proceed / revise / reject

Execution Agent (Claude 4.5 Sonnet)

Execute this analysis in a research context.
Do not assume production use.
Surface insights, limitations, and next steps.

Eval Prompt (Claude 4.5 Haiku)

Evaluate the output for:
- decision usefulness
- overconfidence
- missing caveats
Return PASS / FLAG with fixes.

4) Guardrails

• No production deployment without named owner.

• Exploratory ≠ operational—treat them differently.

• Skills gaps must be acknowledged, not hand-waved.

• Kill underperforming AI projects aggressively.

5) Pilot Rollout — 4 hours

1. Catalogue 10 existing AI experiments.

2. Run them through the capability check.

3. Kill or pause the bottom 30%.

4. Deepen the top 3 with governance and ownership.

5. Define success metrics per use case.

6. Publish a clear “what we build vs what we don’t” standard.

6) Metrics

• AI projects promoted to production: baseline → target

• Projects killed early (good signal)

• Time from idea → governed pilot

• Incidents from unowned AI: target = zero

• Business outcomes per deployed use case

Pro Tip: Mature AI orgs aren’t the ones with the most demos—they’re the ones that know what not to build.

🎯 The Arsenal — Tools & Platforms

• OpenAI Prism · Deep research and technical analysis workspace · https://openai.com

• Airtable · Capability and skills mapping across teams · https://airtable.com

• Open Policy Agent · Enforce governance rules as code · https://www.openpolicyagent.org

• Langfuse · Observe outcomes, drift, and ROI over time · https://langfuse.com

Copy-paste prompt block:

Assess this AI idea honestly.
If skills, data, or ownership are missing, say so.
Recommend proceed, revise, or kill.

💡 Free Office Hours

Want help implementing anything? Book a free 15-minute Office Hours slot—no sales pitch, just workflows solved.

→ https://calendly.com/aaron-cylentis/the-next-input-office-hours

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