95% of players skip the tutorial. Then blame the game.
1. The Productivity Paradox Is Back
Companies have poured $35–40 billion into AI and 95% have seen zero measurable return.
Sounds damning. But we’ve been here before.
In the 1970s–90s, computing power exploded while measured productivity flatlined for two decades. Economists called it the Solow paradox — “You can see the computer age everywhere but in the productivity statistics.” Then organisations restructured around the technology, and the gains appeared all at once.
Same thing is happening with AI. The gains are real — they’re just not showing up where executives are looking.
If you ask a skilled frontline employee whether AI has made them faster, you’ll get a different answer entirely.
AI should absolutely be speeding up your workflow right now. If it’s not, that’s a skill problem.
2. Context Engineering Beats Prompt Engineering
Most people type a random request into an AI, get a mediocre answer, and conclude that the tool isn’t that useful.
The problem isn’t the model. It’s what you’re feeding it.
Context engineering means giving AI everything it needs before you ask your question — role, constraints, tone, examples, format expectations. You build this structured context once and reuse it across every session. It’s the difference between handing a new staff member a blank piece of paper and handing them a detailed brief.
Few-shot prompting — where you include examples of what good output looks like — consistently outperforms instruction-only prompting. Not by a little, but by a lot.
This is what separates power users from casual ones. Not the model they pick, but what context they provide.
3. The XML Template You Build Once
So how do you actually do context engineering in practice?
You build a reusable prompt template. I teach an XML structure in my ChatGPT for Managers course that looks like this:
<role>You are a [specific expert]</role>
<context>[Background, constraints, current situation]</context>
<examples>[1-2 examples of ideal output]</examples>
<task>[What you actually want done]</task>
<format>[How you want the output structured]</format>Build it once. Save it somewhere accessible. Paste it at the start of every new session.
The five minutes it takes to set this up saves you hours of back-and-forth with a model that doesn’t know who you are, what you want, or what good looks like.
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