WINDOW INTO THE FUTURE OF WORK

Weirdly, OpenAI seems to have run into the same problem.

They spent years making the blank box smarter but still have a blank-box problem. Akshay Nathan, who leads core product engineering at OpenAI, was recently asked which automation he wishes worked better.

His answer was basically: bring me new ideas.

The same is true of finding a useful AI task. Your calendar, emails, Slack messages, half-finished documents and that spreadsheet called FINAL_ACTUAL_FINAL_v7 all know what happened last week.

ChatGPT doesn’t. At least not until you show it. So here’s a 10-minute exercise that turns one normal day into three clearly defined tasks and shows you what’s still missing before you test anything 👇

1. Reconstruct one normal workday

Pick the most recent day that felt fairly normal, included at least three different tasks and left enough receipts to reconstruct.

2. Pull out 3 pieces of work

• Open your: Calendar, sent emails, recent files…

• Write down one thing you finished (eg: the weekly report), one thing you chased (eg: chased interview feedback from hiring managers) and one thing you left unfinished (eg: left the campaign research half-done because the information was scattered).

• Don’t judge whether they are good AI tasks yet. Write them down roughly.

💡 Keep the source material open: the spreadsheet, report, PDF or email thread will tell you more than memory will. OpenAI’s own examples start with the actual file, then ask AI to extract changes, risks, dates or owners.

And if the evidence is scattered across files and apps, that’s the kind of longer, multi-step work ChatGPT Work is designed for. Eventually but first, name the task.

3. Turn broad areas into real tasks

This is the difference between naming an area of your job and naming an actual task. It is also one of the first habits we teach inside our free 15 Days of AI challenge for professionals (the next cohort starts August 10th).

💡 And a useful clue: if the same input → work → output repeats every week, it may eventually deserve a reusable Claude Skill but only once the process is clear enough to write down.

4. Find what’s still missing

Now you can open ChatGPT or Claude and paste this:

You should now have three tasks with the work, source material and gaps visible side by side. And that’s not just prompt theatre. MIT Sloan recommends breaking jobs and workflows into individual tasks before deciding which generative AI use cases are worth testing.

And that’s where this little rewind stops. Because a clear task is not automatically a good AI task. It may require judgment you should keep. It may be easy to automate and make almost no meaningful difference. Or it may be a great fit, but only after the missing information is fixed.

That’s also what the Institute for the Future of Work found in recent case studies: outcomes depend on which tasks change, which skills develop and how work is designed, not simply whether AI is introduced. Their research is worth reading.

Still not sure which task to choose?

That exercise makes the work visible but we are also testing a more detailed version that looks across your actual role, compares the opportunities and explains why something may be a strong fit, a partial fit or not an AI fix at all.

We’re still reviewing a mix of roles, so there’s time to put your name down before testing begins during the week of August 10.

Before you go ✌️