Field notes
Things I’m trying to
understand better.
Short essays about practical AI, customer problems, reusable knowledge, and the ways I try to make work less messy.
A working principle
AI is only useful to me if it makes someone’s judgement sharper, not if it makes the decision for them.
What draws me to AI is pretty simple: it’s good at finding patterns in messy information and cutting out the busywork, fast enough that you can actually experiment with how you work.
My default is human in the loop. AI can dig up and organize the information. But someone still has to bring the context, check what actually matters, and own the call.
AI, thoughtfully used · Short essay
01AI should make people more capable, not less involved
AI can speed up research and hand you a decent first draft. But when the work touches a real customer, a person still needs to own the context and make the final call.
I don’t really care if the output sounds convincing. I care whether I can still see the sources and assumptions behind it well enough to decide for myself.
Lessons from the field · Short essay
02Why understanding the real problem matters more than answering quickly
The first symptom is rarely the full story. Ask a few more patient questions and you usually find the real workflow hiding behind the ticket. They often point toward guidance that helps more than one person.
In customer work, a request often arrives as a technical symptom. You only find the useful next step once you understand what the deadline actually is, and what this problem is stopping someone from doing.
So the real question isn’t how fast you can answer. It’s whether you understood what you were even answering.
Better ways of working · Short essay
03From support tickets to reusable answers
A resolved case becomes more valuable when the next customer or colleague can benefit from it too. Clear articles and structured knowledge turn individual problem-solving into a team capability.
A bigger archive doesn’t help anyone. What helps is enough context and reasoning left behind that the next person can act on it without having to start from scratch.
Questions I keep returning to
What makes work
genuinely useful?
- How can AI support judgement without hiding responsibility?
- How do customer questions become better workflows and reusable knowledge?
- What helps small, entrepreneurial teams move with both care and momentum?
- How can a simple tool create value without making work more complicated?
An open notebook