I handed AI about half of my PM job, and there are a few things I still don't dare hand over

Let me start with something that might feel a little counterintuitive: this year I handed AI roughly half of my day-to-day PM work, and I handed it over without a second thought. But a few other things I haven’t dared hand over, and probably never will — not because AI can’t do them, but because once those things are handed off and go wrong, I can’t catch it, and by the time I do, it’s already too late.

What I’ve found separates these two piles has nothing to do with “hard vs. easy,” or “boring vs. interesting.” It comes down to a different question: if AI got this wrong, could I catch it on the spot? If yes, I’ll hand it over. If no, I keep a death grip on it.

So I’ll walk you along that line, laying out the things I “handed over” and the things I “kept” this year, one by one — and I’ll tell you about the couple of times I nearly tripped after handing something off. If you’re also stuck on which things to let AI do and which not to, maybe it’ll save you some trial and error.

First, the ones I handed over: the work “I can eyeball as right or wrong”

Number one: first drafts of all kinds of documents. PRDs, weekly reports, requirement specs, updates for the boss — I basically let AI draft all of these first. It gives me a seventy-or-eighty-out-of-a-hundred foundation, and I edit on top of it. Why do I dare hand it over? Because whether a document is right, I know the moment I read it — which sentence is fluff, which point got missed, none of it escapes me. It pulls me out of “staring at a blank page,” and I take its seventy up to the ninety I actually want. It does this faster than I would, and I verify it fast too — that’s a good trade. Honestly, the most draining part of writing a document was never the typing; it’s the stall of starting from zero. Once that stall gets taken off my plate, what I save is mental energy, not just time.

Number two: digging up material, doing the first pass of competitive research. When I want to get up to speed on a new area, or size up a few competitors, I have AI pull public information into a first version for me: how each of them does it, where their approaches differ, roughly where they’re strong and weak. It does in half an hour what would take me a day of hunting. The key here, again, is “I can verify it” — whether what it organized is right or made up, I can tell by checking it against two points I already know well; if it holds up I use it, if not I redo it. One time it invented a detail about a competitor — “they’ve launched a paid membership tier” — described in convincing, specific terms, except I happened to know that company well and there was no such thing. I casually checked it against two familiar points and it fell apart. So I never trust the material it digs up outright; I always poke at two spots I understand first, and if it cracks there, the whole version goes in the trash. What I can hand over is never “I trust it,” it’s “I can catch it any time I want.”

Number three: sorting a big pile of user feedback and finding the common threads. Hundreds or thousands of comments and pieces of feedback sitting there — reading them one by one will burn your eyes out. Now I just throw them at AI and have it categorize, tag, and pick out the complaints that keep recurring. It gives me a map of “what users are actually griping about and praising.” This used to eat most of my day; now it’s half an hour, and it’s more patient than I am — it won’t start zoning out and skipping lines by the two-hundredth comment.

Number four: meeting notes, and turning discussion into action items. After a meeting, I have AI turn the recording or transcript into notes and pull out who’s supposed to do what; I skim it, add a couple of lines, and send it out. That half hour of cleanup after every meeting — gone. And that skim isn’t for show either: now and then it’ll assign the wrong owner to something, or treat an offhand remark as a firm conclusion, but those I can spot at a glance and fix; it’s precisely because it makes those mistakes that I never skip the skim.

And building prototypes. I wrote about this one specifically last time — a one-sentence idea, and in an afternoon AI turns it into something you can click. That’s in the “handed over” pile too.

String these five together and you’ll notice they share one thing: each of them has a right-or-wrong I can check on the spot. Whether a document is good, I can read. Whether the material is real, I can cross-check. Whether the feedback is sorted right, I can spot-check. AI frees me from the tiring, time-eating grunt work — and that final “is it right” gate is always still mine to hold. That’s the entire basis for my handing it over with peace of mind.

Now, the ones I kept: the work “I can’t tell is wrong even when it is”

The more I hand over, the clearer it gets which few things I absolutely cannot. Because they all step on the same landmine: when AI gets it wrong, you can’t tell on the spot — sometimes you never tell at all.

Number one: deciding whether to do something, and what to do first. Prioritization and trade-offs, in other words. AI can help me list all the options and lay out the pros and cons of each — that I welcome. But the decision of “these three requirements, which one do we cut, which do we ship first” I never hand to it. Because prioritization has no verifiable, standard right answer — it depends on what we actually want this quarter, which direction we’re betting on, what we’re willing to give up, and all of that lives in my head and in countless bits of context never written into any document. The ranking AI gives me always looks reasonable, and “looks reasonable” is exactly the dangerous part — a wrong priority takes months, takes a whole pile of resources going down the drain, before you realize the direction was wrong from the start. That kind of error — invisible at the time, unrecoverable after the fact — I don’t dare outsource.

Number two: judging whether the stuff AI itself gives me is actually right. This one sounds circular, but it’s especially deadly. AI will very confidently present something wrong as if it were true — a number that doesn’t exist, a user conclusion it took for granted, a chain of logic that sounds airtight but doesn’t hold up. If I hand off even the “judge whether it’s right” part to AI (say, sending in another AI to check it), then no gate has a human holding it, and the wrong stuff sails all the way to production on green lights. So the more it swears something’s true, the more I make myself stop and verify it. Let go of this gate for a moment, and those five “safe to hand over” tasks instantly turn into landmines — because the precondition for handing them over was that this gate of mine was still standing.

I got burned on this once. One time it helped me analyze a batch of data; the conclusion was pretty, the logic flowed, and because it read so smoothly I didn’t dig in — I wrote it straight into a report. Only later did I find out it had mixed up the definitions of two metrics, and the whole conclusion was backwards. After that I set myself an iron rule: the smoother the conclusion, the more I stop; the more confident it is, the less I let myself get lazy.

Number three: the things between real people that require reading the room. Soothing a colleague who’s fuming because their requirement got cut, persuading a boss who flatly disagrees, finding balance between two teams that have started fighting — these I’ve never thought about handing to AI. AI can help me soften the wording of an email, but it can’t read whether the person across from me is genuinely angry right now or just wants an out, whether I can crack a joke or have to stay serious. These things — hidden in tone, in pauses, in the history between the two of you — are the hardest part of this job, and the part you least can outsource. A polished, perfectly even-keeled email written by AI can, sometimes, do far more damage than your own clumsy but sincere two sentences.

Number four, and the most fundamental — the taste for “what is good,” and the responsibility of being the one who carries it when things go wrong. Two prototypes you can both click, and which one “feels right” and which is off — that line is something I’ve built up over the years; I can’t spell it out but I can recognize it at a glance. And once this product runs into trouble, the person who stands up and owns it is me, not AI. You can’t make a model bear responsibility — it won’t lose sleep over a wrong decision, it won’t take a hit to its performance review, it won’t flush red at the post-mortem. And the PM role, in a sense, is selling exactly this: that there’s a specific person, on the hook for these judgments. That’s something I can’t hand over, and shouldn’t.

When a new task lands, how I decide on the spot whether to hand it over

The two piles above are what a year of doing this added up to, but when a task I’ve never done before actually lands in my hands, I don’t go flipping through this list. I just ask myself three questions, and it settles fast.

First: if it got this wrong, could I catch it on the spot? If I can catch it, lean toward handing it over; if I can’t, or it only surfaces much later, keep it. If it botches a document, I know the moment I read it, so I dare hand it over; if it botches prioritization, I won’t find out for months, so I don’t dare.

Second: does this thing have a “standard answer” I can check against, or does it come down entirely to this moment’s trade-offs, relationships, and taste? If there’s an objective right and wrong (is the material real, is the sorting accurate), hand it over; if the answer is buried in “what we actually want this quarter” or “what mood this person is in right now,” keep it.

Third: if this goes sideways, does a specific person need to stand up and carry it? If someone needs to be accountable, I don’t outsource it — because responsibility simply can’t be handed to a model that won’t lose sleep and can’t be held to account.

Here’s an example so you can see how to use it. A while back the question was whether to add an “AI smart reply” to a feature, and I split it into these three. Whether it works well, I’ll know once I ship it and see if users click it (question one: catchable, verifiable). But “should we spend these resources on this feature, is it worth betting on this direction” — no standard answer, entirely down to our wager (question two: rides on judgment, keep), and if it really flops, I’m the one taking the fall (question three: someone’s accountable, keep). So the conclusion was clear: let AI build the prototype of that reply feature, but whether to do it, whether to place that bet — I call that myself. Run the three questions once, and the line between hand-over and keep surfaces on its own.

That line, it turns out, keeps sliding in one direction

Lay out the two piles and you’ll notice: not one of the things I kept is held there because “AI isn’t smart enough yet.” Quite the opposite — at writing a polished email, at laying out priorities cleanly, it may be better than I am.

I kept them because their value lies precisely in being done by a person: a decision someone has to be responsible for, a judgment someone has to be accountable for, a relationship someone has to genuinely reach out and catch. The scarcity of these things doesn’t come from AI being unable to do them; it comes from “there must be a specific person here.” That’s also why I’m not worried this line will suddenly collapse one day — it isn’t drawn along “AI’s capability boundary,” it’s drawn along “who’s responsible, who has the taste,” and the latter, for the near future, can still only be a human.

As for the “handed over” pile, I’m well aware it’s only going to grow. Today AI helps me write first drafts and dig up material; tomorrow it might get me to eighty out of a hundred on the initial solution design too. What I need to do isn’t cling to a few tasks and refuse to let go — it’s keep a firm hold on that “is it right” gate, while constantly asking myself: which old task is it time to hand over now?

That said, honestly, the handing-over itself has a cost too, and this is a part I still haven’t fully figured out. If AI writes all my first drafts, will I slowly forget, after long enough, how to think a PRD clear from scratch? If it digs up all the material, will I lose the ability to plunge in myself and dig out a feel for it? There are some tasks I still insist on doing from scratch myself once in a while — not entirely because AI does them badly, but because I’m afraid that muscle will atrophy. When the moment comes that I need to judge whether it did something right, I’m afraid I’ll have lost the feel for it. Beyond hand-over and keep, there seems to be another question — how much to hand over, and how much to keep for my own practice — and that one I’m still figuring out. I’d like to hear how you weigh it, too.