Your Most Valuable Work Is the Worst Place to Start With AI
Hand the model your highest-stakes brief and it'll confidently invent a case that doesn't exist. Start somewhere cheaper.
How to Choose Where to Start With AI When It Can Do Almost Anything
TL;DR: The hard part isn’t the model. It’s deciding what to point it at first. Don’t start with the most impressive thing it can do. Start with the task you repeat constantly and can check in under a minute. Score your options on three things: how often you do it, how much it drains you, and how fast you can verify the result. Keep the high-stakes work for later. And when one sticks, turn it into a repeatable step so the whole team gets the gain, not just you.
A client asked me last month what the model is best at. Honest answer? I don’t fully know, and neither does anyone else.
Here’s the problem that creates. You open the box, the cursor blinks, and you can ask it to do almost anything. Write the client memo. Summarize the deposition. Build the spreadsheet. Plan the offsite. So you do one of two things. You freeze, because where do you even begin. Or you reach for the hardest, most important thing on your desk, hand it over, and watch it confidently invent a case that doesn’t exist.
Both reactions kill momentum. I’ve watched sharp partners try the model once on a brief, catch one bad citation, and write off the whole thing as hype. They started in exactly the wrong place.
Here’s the filter I actually use with firms.
Stop asking what’s most impressive
The instinct is to test AI on your most valuable work. It feels efficient. If it can draft the M&A memo, that’s where the money is.
But that’s backwards. Your most valuable work is usually your highest-stakes, hardest-to-check work, which makes it the last place you’d want to be testing whether you can trust the model yet. Your first mistakes have to land somewhere. Better they land where nobody’s watching.
The better question is quieter. What do you do over and over that you could check in twenty seconds?
Think about it this way. You want reps, low stakes, and a fast feedback loop. That points you at the boring stuff. The work that doesn’t feel impressive at all.
What makes a task a good first pick
When I sit down with a firm to pick a first use, I’m really weighing three things, and they don’t matter equally.
Start with how often the work comes up. You want something constant, a dozen times a week or more, because the saved minutes compound and you get fluent fast. Five minutes saved once is a rounding error. Five minutes saved forty times a week is most of a workday handed back.
Then there’s the drag. The work people avoid, delay, or grind through with their jaw set. Cleaning up a rough transcript. Turning six pages of email into something a partner will actually read. It’s rarely the strategic work, it’s the irritating work, and that’s the point. If a task doesn’t bug you today, handing it off won’t feel like anything.
The one that matters most, though, especially when your name is on the result, is whether you can check the answer fast. This is the test people skip and regret. If verifying the output takes as long as doing the work yourself, you’ve gained nothing. The tasks worth starting with are the ones where a wrong answer jumps out at you. You read the summary, you were on the call, you know in three seconds if it’s off. A brief full of citations is the opposite. There, checking the work is the work.
And keep an eye on what a mistake costs. Early on, you want work where being wrong is cheap. An internal summary that’s a little off costs you nothing and a minute to fix. A fabricated quote in a client deliverable costs you the client, maybe worse.
What good looks like in practice
A few tasks that fit.
Turning a messy call transcript into clean notes and action items. You were there. You can spot a wrong takeaway instantly, and you do this constantly.
Drafting the first version of a routine client email, the kind you’ve written a hundred times. You’re not shipping the model’s words. You’re skipping the blank page. Thirty seconds to read, easy to fix.
Pulling the three things that matter out of a long document so you can decide whether the whole thing is worth your time. Wrong? You’ll know the moment you open it. Right? You just saved twenty minutes.
All three look different and share the same bones. The raw material is already in front of you, the transcript, the email thread, the document. You can eyeball the output in seconds. And if it’s wrong, nobody gets hurt.
It shows up well outside the practice of law, too. A deal team buried in call notes. An executive trying to turn a dozen scattered updates into one Monday brief. Different work, same fit.
Now the one that fails. The appellate brief with case citations. High stakes, slow to verify, and the exact spot where these models still make things up. Get there eventually. Don’t start there.
How you’ll know it worked
Measuring this is simpler than people expect. Did it actually save time you can count? Rough math is fine. A task that took fifteen minutes and now takes four, including your review, is eleven minutes back. Write it down.
Quality has to hold, too. The output should be at least as good as what you shipped before, after your edit. If you’re spending more time fixing than you saved, the task isn’t ready, or the way you’re asking isn’t.
But the real test is duller than any metric. Are you still using it in week three? Most AI experiments die quietly. People try it twice, forget, slide back to the old way. If a use survives three weeks of your actual calendar, it’s real. If it doesn’t, drop it without a second thought and pick the next one.
Then make the win repeatable
Here’s the part most people skip. When a use sticks, don’t leave it in your head.
Turn it into something other people can run. A saved prompt. A one-page template. A line in the intake process that says draft the summary first, then review. That’s the difference between you saving eleven minutes and forty people saving eleven minutes. One person getting faster is a good habit. A repeatable step is how a firm actually changes.
What to do Monday morning
Write down every task you repeated last week. Circle the three you could check in under a minute.
Pick one. Run it through the model every time it comes up this week, and jot the minutes saved each time.
Friday, decide: keep it, change how you ask, or drop it and try the next one on the list.
That’s the whole thing. You’re not hunting for one perfect use. You’re running cheap experiments until a few of them stick.
The firms pulling ahead didn’t go hunting for the impressive use case. They started on the boring, repetitive work, racked up small wins, and kept going. Everyone else is still staring at the blinking cursor, waiting to figure out the perfect place to begin.
If you enjoyed this, please share it with three friends. If you want to chat further on it, reach out to me at steve@intelligencebyintent.com. If there’s a topic you’d like me to cover, let me know.