Before people are trusted with bigger decisions, they usually practise with the smaller ones: cleaning up a spreadsheet, fixing a bug, writing a first draft, responding to a customer’s support request… you get the picture.
From the outside, it looks like grunt work. Much of it is slow, repetitive and boring. But some of those tasks give beginners a chance to make a call, see what happens and revise their work while the stakes are still manageable. With useful feedback, they learn what to do differently next time.
When we delegate that work to AI, we lose those opportunities along with the tasks. The result arrives ready for approval, but someone who has never worked through a similar problem doesn’t know what good looks like. Reviewing requires experience they don’t have.
This is not a plea to keep junior drudgery, but asking people to approve work they have not learned to evaluate creates a problem education needs to take seriously. We cannot tell people that their value now lies in judgement while taking for granted how we’ve been developing it all this time.
What this means for NextWork
If fewer opportunities to develop judgement come through junior work, learning experiences need to provide more of them. But simply giving someone a project does not solve the problem. A project can make every decision for the learner just as easily as an AI tool can.
For NextWork, this means being deliberate about which parts AI handles and which decisions the learner needs to practise. Those choices should depend on what the person is learning. Something worth working through on a first project may be sensible to automate on the next, leaving them free to tackle a more demanding problem.
We do not need to preserve every task AI can now do, but we do need to preserve the opportunity to learn from decisions that matter, then move people towards work that demands greater judgement. That is what NextWork provides with hands-on projects, at every stage of a learner’s career.
All my best,
