🎮 The Next Input — Issue #164

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⚡ The Briefing — 60 sec

• OpenAI updates its Agents SDK to help enterprises build safer, more capable agents It’s the next “cool” thing until Anthropic ships Opus 4.7 like tomorrow lol. Still, sandboxing and harness support are exactly the kind of boring-serious upgrades that make agents actually usable in enterprise settings.

• AI yet to boom with boomers but young workers trust it 30 years ago these “boomers” were the hip kids telling everyone to use the Internet. Time beats to its own drum and we generally don’t go along with it. Either way, AI is a totally different beast. Get onboard or get eaten alive.

• Shares in Allbirds surge after maker of wool sneakers announces pivot to AI Umm WTF? Turning a sneaker company into “NewBird AI” and chasing GPU-as-a-service is exactly the sort of sentence that makes you check the date twice.

🛠️ The Playbook — The AI Adoption Gap Engine

Mission
Turn AI adoption from generational confusion and executive theatre into workflows that actually improve work.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Better adoption, less fake compliance, and AI usage tied to real outcomes instead of vibes.

0) Why This Matters

There are three different stories colliding right now.

One is product maturity. OpenAI’s updated Agents SDK adds sandboxing and a new harness layer so enterprises can build longer-horizon agents with more controlled access to files and approved tools. That is the “cool thing,” yes, but it is also the infrastructure layer becoming more real.

The second is adoption reality. Younger workers are generally more open to using AI at work, while older cohorts are more hesitant, which is exactly how new waves of tech often play out — except AI is not just another software layer; it changes how decisions, writing, research, and execution happen.

The third is market absurdity. Allbirds rebranding toward AI infrastructure and GPU leasing shows how quickly the label “AI” can become a strategy, a stock catalyst, or a panic pivot depending on who is using it.

So the move is not:

• tell everyone to use AI

• count prompts

• pretend every AI pivot is genius

The move is:

• define where AI genuinely helps

• train people on workflows, not slogans

• measure whether the work actually got better

1) Architecture

2) Workflow

1. Pick one real workflow where AI could reduce friction, not just look impressive in a meeting.

2. Define what success looks like before rollout: speed, clarity, error reduction, or task completion.

3. Train a small group on that exact workflow instead of giving broad “use AI more” instructions.

4. Add guardrails for anything that takes action, including sandboxing or approval steps.

5. Compare the AI-assisted workflow against the old version on actual outcomes.

6. Expand only if the work is materially better, not just more AI-shaped.

3) Example Prompts

Workflow Definition Prompt

You are reviewing a team workflow for practical AI adoption.

For the workflow below:
- identify where AI can genuinely help
- identify where AI would just add noise
- define what success looks like
- identify the top 5 adoption risks

Workflow:
[insert workflow here]

Generational Adoption Prompt

You are helping a mixed-experience team adopt AI.

For the workflow below:
- identify what may confuse hesitant users
- identify what younger users may adopt too quickly without enough judgment
- suggest a simple training plan
- explain how to keep the rollout grounded in outcomes

Workflow:
[insert workflow]

Agent Safety Prompt

You are reviewing whether an AI workflow needs sandboxing or approval.

Check:
- whether the workflow takes action
- whether the workflow touches files, code, or sensitive systems
- whether sandboxing is required
- whether human approval is required

Return:
approve / sandbox / review
With a short reason.

Executive Reality Check Prompt

You are reviewing an AI rollout plan.

Check:
- whether it measures outcomes or just usage
- whether staff are being trained or simply told to use AI
- whether leadership understands the workflow impact
- whether the rollout should proceed

Return 4 bullet points only.

4) Guardrails

• Do not force AI usage where the workflow is still unclear.

• Measure outcomes, not prompt counts.

• Train teams on specific jobs, not vague AI enthusiasm.

• Keep action-taking agents sandboxed until proven safe.

• Separate real adoption from AI-flavoured theatre.

• If the worker experience gets worse, the rollout failed.

5) Pilot Rollout — 3 hours

1. Choose one workflow where the team is either hesitant or overexcited about AI.

2. Map the old process and define exactly what should improve.

3. Build one narrow AI-assisted version with clear limits.

4. Train 3–5 people on that version only.

5. Run 10–15 live tasks and compare quality, speed, and frustration.

6. Keep the rollout only if the workflow is genuinely better.

6) Metrics

• Time saved per workflow

• Error rate before vs after AI

• Human correction rate

• Adoption rate by role or cohort

• Worker confidence in the workflow

• Number of AI steps that required rollback

• Percentage of pilots that produced real improvement

Pro Tip: The fastest way to kill AI adoption is to confuse visible usage with actual leverage.

🎯 The Arsenal — Tools & Platforms

• OpenAI Agents SDK · now adding sandbox integration and harness support so enterprises can build more controlled, long-horizon agent workflows.

• Airtable · simple workflow inventory for spotting where AI helps and where it is just decorative · Airtable

• Google Sheets · lightweight tracking for adoption, errors, and actual improvement · Google Sheets

• Claude / ChatGPT · useful model layer, but only when tied to specific workflows and real review · Anthropic · ChatGPT

• Allbirds / NewBird AI saga · probably the funniest current reminder that not every AI pivot is the same as an AI strategy.

Copy-paste prompt block:

You are helping me build an AI Adoption Gap Engine.

For the workflow below:
1. identify where AI can genuinely help
2. identify where AI would just add noise
3. define what success looks like
4. identify what training is required
5. identify whether sandboxing or approvals are needed
6. list the top 5 adoption risks
7. propose a 2-week pilot

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- AI opportunity
- Noise / bad-fit areas
- Training plan
- Control layer
- Risks
- Pilot rollout
- Metrics

💡 Free Office Hours

If your team is stuck between AI hype, AI hesitation, and AI theatre, I run free office hours to help map the workflow, train the team, and figure out where the real leverage actually is.

Book here: https://calendly.com

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🕹️ Game Over

Everybody says “adopt AI.” Very few bother to define what good adoption actually looks like.

— Aaron Automating the boring. Amplifying the brilliant.

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