An expert charging $475/hour learned the hard way this week what happens when “AI gave me an answer” gets confused with “the job is done.”

3M (the Post-it notes company) hired an engineering expert to investigate a very serious factory incident in Houston. Then in court, it came out that he’d used ChatGPT to help write the report, including asking it to “show how 3M is 0% at fault.”

The firm was paid around $90,000 for the analysis. Bold approach to independent expert work and even bolder considering the prompt archive is now public lol

Go snoop through his actual ChatGPT prompts

This story sent me down a rabbit hole about something much more relevant to the rest of us: what happens after AI gives you the answer? Because we finally have some decent data on this. And on roughly 1 in 6 AI-assisted work tasks, people say AI makes the task take longer.

So...where did the time go?

WINDOW INTO THE FUTURE OF WORK

AI is extremely good at finishing the output, not the job.

Earlier this month, Epoch AI and Ipsos asked 1,106 professionals about how AI is showing up in 10 normal work tasks like reading documents, analysing data, maintaining records, designing systems, etc.

The good news is when AI does most or all of the task, people are much more likely to report saving time. 53% said those tasks now take less time, compared with 37% when AI only helps with part of the work.

However, roughly 1 in 6 AI-assisted tasks now takes longer than it did before.

Obviously this doesn’t mean AI is wasting everyone’s time. It means the extra work is going somewhere. So I went looking for it.

The clearest answer I found was annoyingly boring: bad project briefs 👇

A £6,000/month boring problem

The most useful example I found all week was not another autonomous agent demo. It was a marketing intake form.

E.ON Next’s go-to-market team had a very normal problem: people across the company would submit project requests without enough information, then producers would spend ages chasing context before anyone could start the work.

According to their case study, that manual back-and-forth was costing roughly £6,000 a month in producer time. So they built AI into the bit everyone usually skips over in the demo.

Someone submits a request. The AI checks it against an example of what a good request should contain. Missing context? It goes back and asks. Still missing something? It asks again. Only then does it write the brief.

marketing team goes to the beach. They just found the part of the job that kept creating stupid back-and-forth and designed around it.

E.ON says the workflow now saves an estimated £5.5k/month. Across the wider setup, early-stage back-and-forth fell 25%. See exactly how E.ON built the workflow.

The AI output isn’t really the innovation here. The team first had to work out what information needs to exist before this job can move forward. That’s a very different question from what can we automate? And the actual process is stealable.

Steal this bit

Before looking for another task to “automate”, pick one piece of work that keeps getting stuck and ask:

Where does the back-and-forth happen?
What information is always missing?
What has to be true before the next person can use the output?

That’s basically what E.ON did. The AI came afterwards.

AI SPEED VS. COMPANY SPEED

Last week I wrote about getting one good AI result to repeat. The annoying sequel is that you can get much faster while the system around you stays exactly the same. And this is probably why this post from Zara Zhang has been bouncing around X today:

I wouldn’t take the 10x vs 20% numbers literally, but the frustration feels very real.

You can 10x the person without 10xing the workflow.

Which is why I like the E.ON example. They didn’t just give the producer a faster way to write briefs. They went after the crappy handoff creating the work in the first place.

This gets even messier when the “workflow” involves 50 or 500 people rather than one very motivated ChatGPT user. That’s a lot of what we work on with teams at 100 School: moving beyond everyone individually getting faster and figuring out what changes in the work around them.

See how 15 Days of AI for Teams works

Anyway, one final diagram of the AI productivity revolution.