🎮 The Next Input — Issue #204

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⚡ The Briefing — 60 sec

• Influencers draw backlash for attending OpenAI’s first luxury trip Call me what you want. If they bring it to Sydney? C A L L M E! I’ll discuss the ethics of frontier-lab influencer junkets from the complimentary suite.

• LinkedIn now allows users to flag AI slop It’s either your secret victory or your secret doom. Me? I’m going HAM on that button. The era of “Delighted to share five leadership lessons from my breakfast” may finally face consequences.

• AI transformation fails when imposed from the top In a generic enterprise sense, fair enough. For SMBs though? I’ve got to disagree. Sometimes the owner deciding “we’re fixing this broken process now” is exactly how anything gets done. Not every article I post needs my unquestioning applause.

🛠️ The Playbook — AI Adoption Flywheel

Mission
Turn leadership-backed AI adoption into useful frontline workflows without creating resentment, slop or another abandoned transformation programme.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Accelerates adoption while ensuring AI usage produces measurable work instead of performative activity.

0) Why This Matters

AI adoption can fail from either direction.

Leadership can impose tools without understanding how work actually happens.

Employees can experiment endlessly without connecting usage to business outcomes.

And somewhere in the middle, marketing starts publishing beige AI sludge because everyone has been told to “use more AI.”

The practical answer is not purely top-down or bottom-up.

Leadership should choose the priorities, define the boundaries and fund the work. Frontline users should shape the workflows, test the system and expose where the process breaks.

Direction from the top. Design from the work itself.

1) Architecture

2) Workflow

1. Leadership selects one operational priority with a measurable business outcome.

2. Frontline users map the current workflow and identify where time, quality or revenue is being lost.

3. The team builds the smallest AI-assisted version that removes one meaningful bottleneck.

4. Users test the workflow in real work and record failures, overrides and missing context.

5. Leadership reviews outcomes rather than raw usage and removes blockers to adoption.

6. Successful workflows are standardised, governed and expanded into adjacent processes.

3) Example Prompts

Frontline Workflow Discovery

You are an AI workflow analyst.

Interview me about the following business process:

[PROCESS]

Your goal is to identify:
- repetitive work
- unnecessary handoffs
- duplicate data entry
- approval delays
- missing information
- tasks requiring human judgement
- workarounds employees currently use

Ask one focused question at a time.

After gathering enough detail, return:
1. current-state workflow
2. primary bottlenecks
3. automation candidates
4. tasks that should remain human-owned
5. recommended pilot

Adoption Friction Review

Review the following AI workflow and user feedback.

Identify:
- reasons employees are avoiding it
- steps that add work instead of removing it
- unclear instructions
- trust or quality concerns
- missing integrations
- training gaps
- leadership assumptions contradicted by frontline reality

Return improvements ranked by expected adoption impact.

AI Slop Filter

Review the following AI-assisted content.

Flag:
- generic claims
- unsupported expertise
- repetitive phrasing
- empty leadership language
- fake personal anecdotes
- excessive formatting
- statements that could apply to any company
- sections that sound generated rather than genuinely observed

Rewrite only the flagged sections using specific facts, clear opinions and natural language.

4) Guardrails

• Do not confuse tool usage with operational adoption.

• Give frontline users influence over workflow design.

• Require leadership ownership of priorities and resources.

• Automate painful work before creating new AI-facing tasks.

• Measure quality, time saved and business outcomes—not prompt counts.

• Keep human accountability for consequential decisions.

• Retire workflows that employees repeatedly bypass.

• Never publish AI-generated content merely to satisfy an adoption target.

5) Pilot Rollout — 3 hours

1. Choose one recurring workflow leadership wants improved.

2. Invite two frontline users to map how the work actually happens.

3. Identify one bottleneck that can be removed without redesigning the entire department.

4. Build a lightweight AI-assisted workflow inside an existing tool.

5. Run five real cases and record time saved, errors and user overrides.

6. Decide whether to scale, revise or kill the workflow based on evidence.

6) Metrics

• Weekly active workflow users

• Percentage of eligible work completed through the system

• Time saved per completed workflow

• Human override rate

• Output acceptance rate

• User satisfaction

• Error and rework frequency

• Time from idea to pilot

• Business value per active workflow

• Percentage of AI content flagged as low quality

Pro Tip: Leadership should mandate the outcome, not pretend it already knows the perfect workflow.

🎯 The Arsenal — Tools & Platforms

• Microsoft Teams · delivers AI workflows inside existing daily work · Link

• Airtable · captures workflow opportunities, feedback and pilot status · Link

• Miro · maps frontline processes before automation begins · Link

• LangGraph · orchestrates governed, multi-step AI workflows · Link

• Power BI · measures adoption against operational outcomes · Link

Copy-paste prompt block:

You are an AI adoption and workflow transformation architect.

Design a practical adoption programme for my organisation.

Organisation:
[DESCRIPTION]

Leadership priorities:
[LIST]

Teams involved:
[LIST]

Current AI tools:
[LIST]

Painful workflows:
[LIST]

Governance requirements:
[LIST]

The programme must:
- provide clear leadership direction
- include frontline users in workflow design
- prioritise measurable operational pain
- deploy inside existing work environments
- prevent low-quality AI output and performative adoption
- track business outcomes rather than raw usage
- support rapid pilot, revision and shutdown decisions

Return:
1. operating model
2. opportunity intake process
3. workflow design method
4. pilot selection criteria
5. leadership and frontline responsibilities
6. adoption dashboard
7. governance controls
8. 30-day rollout plan
9. success metrics

💡 Free Office Hours

AI transformation works best when leaders create urgency without pretending they understand every operational detail. The goal is not consensus theatre—it is combining executive authority with the knowledge of the people actually doing the work.

Book here: https://calendly.com

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🕹️ Game Over

Invite me to the luxury retreat.

Flag the LinkedIn slop.

Then let the people doing the work help design the AI meant to improve it.

— Aaron Automating the boring. Amplifying the brilliant.

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