🎮 The Next Input — Issue #159

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⚡ The Briefing — 60 sec

• Google quietly releases an offline-first AI dictation app on iOS Very very cool app. Might help AI you sound way less “sloppy”. Offline + on-device + clean dictation is one of those small things that ends up being used everywhere.

• Anthropic: all your zero-days are belong to us Mythos truly seems like a step change. But we probably aren’t seeing it soon. When models start brushing up against offensive security territory, release timelines tend to slow down real quick.

• Refusing AI at work could put your job at risk honestly? As someone who loves what AI can do? This is a HORRIBLE metric. Super easy for Execs to hide behind their desks and demand “everyone use AI” while they type with two pointer fingers into an Excel spreadsheet...

🛠️ The Playbook — The AI Adoption Reality Engine

Mission
Drive real AI adoption based on outcomes and capability — not performative usage metrics that look good in a board deck.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Higher-quality adoption, less internal resistance, and AI usage that actually improves work instead of ticking boxes.

0) Why This Matters

There are two very different versions of “AI adoption” happening right now.

Version one is real: better tools, cleaner workflows, faster execution. Things like offline dictation that actually improve day-to-day work without friction.

Version two is theatre: dashboards, mandates, and vague directives to “use more AI” with no clear definition of what good usage looks like.

That second version is where things go sideways.

Because:

• forcing usage ≠ building capability

• counting prompts ≠ improving output

• mandates ≠ understanding

The result? People either fake usage or misuse the tools — and both kill long-term value.

So the play is simple:

• measure outcomes, not activity

• build capability, not compliance

• reward good usage, not visible usage

1) Architecture

2) Workflow

1. Identify key workflows where AI could realistically improve speed or quality.

2. Define what “good usage” looks like in that workflow (not just “used AI”).

3. Train the team on how to use AI for that specific task.

4. Track outcomes: speed, quality, error rate — not usage count.

5. Review outputs to see where AI actually helped or hurt.

6. Expand usage only where it proves valuable.

3) Example Prompts

Outcome Definition Prompt

You are defining what good AI usage looks like.

For the workflow below:
- define the expected improvement (speed, quality, accuracy)
- define what success looks like
- define what bad usage looks like

Workflow:
[insert workflow here]

Capability Builder Prompt

You are helping someone improve how they use AI.

Given their workflow:
- suggest better prompts
- suggest better structure
- identify common mistakes
- provide one improved example

Workflow:
[insert workflow]

Usage Audit Prompt

You are auditing AI usage.

For the workflow below:
- identify where AI is actually helping
- identify where it is being used poorly
- identify where it shouldn’t be used at all

Return 3 bullet points.

Exec Reality Check Prompt

You are reviewing an AI adoption strategy.

Check:
- whether metrics are based on usage or outcomes
- whether teams are being forced or enabled
- whether leadership is aligned with reality

Return:
1. biggest issue
2. biggest risk
3. one fix

4) Guardrails

• Do not measure prompts, measure results.

• Avoid forcing AI into workflows where it doesn’t belong.

• Train people properly before expecting results.

• Keep leadership accountable for understanding the tools.

• Reward effective usage, not visible usage.

• Kill workflows that are AI for the sake of AI.

5) Pilot Rollout — 3 hours

1. Pick one workflow where AI could realistically help.

2. Define what success looks like before introducing AI.

3. Train a small group on how to use AI properly.

4. Run real tasks and measure outcomes.

5. Compare results against the old process.

6. Expand only if there is a clear improvement.

6) Metrics

• Time saved per task

• Output quality improvement

• Error reduction

• Human correction rate

• Adoption based on effectiveness

• Number of workflows with proven ROI

• Drop-off in ineffective usage

Pro Tip: If your AI strategy needs a dashboard to prove it’s working, it probably isn’t.

🎯 The Arsenal — Tools & Platforms

• Google AI Dictation App · clean, practical example of AI that actually improves everyday workflows · TechCrunch

• ChatGPT / Claude / Gemini · powerful tools, but only when tied to real workflows · https://chatgpt.com · https://www.anthropic.com · https://gemini.google.com

• Airtable / Sheets · track outcomes, not vanity metrics · https://www.airtable.com · https://workspace.google.com/products/sheets/

• Prompt libraries · build real capability across teams · (internal)

• Training loops · the difference between adoption and actual skill · (internal)

Copy-paste prompt block:

You are helping me build an AI Adoption Reality Engine.

For the workflow below:
1. define what good AI usage looks like
2. define expected improvements
3. identify where AI should and should not be used
4. identify risks of forced adoption
5. propose a pilot rollout
6. define success metrics
7. identify how to train the team

Workflow:
[insert workflow here]

Return the answer in markdown with sections for:
- Workflow summary
- Good usage definition
- Expected improvements
- Risks
- Training plan
- Pilot rollout
- Metrics

💡 Free Office Hours

If you’re trying to get real value out of AI without turning it into a box-ticking exercise, I run free office hours to help design workflows that actually work.

Book here: https://calendly.com

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Your media team opens Slack at 8am. There's a cross-platform brief in #growth: Google Ads spend vs. ROAS, Meta CPA by campaign, Stripe revenue by channel. Viktor posted it at 6am. Nobody asked for it.

Last week, one team's Viktor caught a spend spike at 2am on a broad match campaign and flagged it in Slack: "CPA up 340%. Recommend pausing and shifting budget to the top two performers." That would have burned $3K by morning. The media buyer woke up to a problem already handled.

Your strategist reviews spend trends. Your account manager checks revenue attribution. Same Slack channel, same colleague, before anyone's first coffee.

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🕹️ Game Over

Using AI isn’t the goal. Using it well is.

— Aaron Automating the boring. Amplifying the brilliant.

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