List out the issues, then let the AI get to work.
This newsletter is all about the subtle mindset shifts that are taking place as AI becomes more embedded in our daily work.
1. Longer Review Sessions and Writing Everything Down First
The instinct when working with AI is to find a problem, prompt a fix, wait, review, then move to the next one.
This is sequential. One at a time. And this is the slowest way to use AI.
What’s better?
No fixing as you go along. When you review something, document every issue, problem, bug, inconsistency, edge case in a single markdown list before touching any of them. No fixing as you go along, just capturing.
Then hand the full list to the AI in one go.
The AI will come up with a better plan as it can see which issues have the same root causes, and then fix them all at once.
Practical rule: no fixing until everything is written down. The compound benefit of the full picture is worth the longer review time.
2. Running Multiple Intelligences in Parallel
Once everything is documented, the next question changes.
Instead of “what do I fix next?” it becomes “which of these can run at the same time?”
That shift — sequential to parallel — is the single biggest productivity multiplier available with AI right now.
Anything with no dependency on another task should run simultaneously.
An orchestrator layer coordinates, individual agents execute.
This DOES NOT mean having multiple chat windows open.
What this actually means:
You start one AI session and feed it your issues document, then tell it to create a plan that handles collisions and dependencies, then to execute that plan using as many backgrounded subagents in parallel as possible.
I restructured my own workflow this way recently and estimate that clearing an issues list went from 1 week to 1 day. The bottleneck has shifted from “how fast can I prompt individual fixes” to “how fast can I review all the fixes that the AI is implementing simultaneously”.
That’s the new mindset and skill: Knowing how to instruct the AI to break work apart, plan it and manage it so it can run in parallel.
I cover further aspects of this in The Complete Claude, Claude Code & AI for Work Productivity course.
3. Verbum Verto — AI as a Universal Transformer
Before code, you couldn’t manipulate data at scale. Spreadsheets and programming gave us the ability to transform numbers, sort records, and run calculations — things that were impossibly tedious by hand.
AI does the same thing for natural language.
Paste a podcast transcript, get a bullet-point summary with action items. Paste a long email thread, get a decision log. Paste raw meeting notes, get a formatted brief for someone who wasn’t there. Paste a blog post, get social media posts in your voice.
Most people are still at the “calculator and Google” stage, asking AI to write things or answer questions. They haven’t made the leap that AI is a universal format converter for language, the same way code became a universal format converter for data.
Next time you have text in one format and need it in another, don’t rewrite it. Transform it.
4. Courses
I have a range of online courses that teach business people what they need to know about productivity and AI:
1. Next Level Productivity
A practical, straightforward course that teaches you how to achieve elite-level personal productivity in today’s constantly interrupted world.
2. The AI Agents, Automations and Agentic Workflows Guide
This non-technical course shows business people and non-coders how they can build and use AI agents in ChatGPT and Zapier.
3. The Complete Claude, Claude Code & AI for Work Productivity
A comprehensive course taking you from beginner AI concepts like prompt and context engineering, to cutting-edge AI productivity using terminal-based AI tools like Claude Code for non-coding office work. Usable with ChatGPT, Claude, Gemini and other current LLMs.
4. The AI Playbook
This is my longer premium course on how businesses can deploy AI tools and technology across their processes and teams.
5. ChatGPT for Managers
See how AI can solve complex management challenges in less than 30 seconds. Full prompt library and examples included.
That’s it for this week!
— Aaron