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Which repeated tasks are actually worth automating with AI · MBA Training AI

• 0:00
  Welcome to the Leaders Insights podcast.
• 0:07
  Today's episode, which repeated tasks are actually worth automating with AI.
• 0:12
  Everyone's obsessed with automating tasks with AI right now.
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  But you're about to tell me most of that effort is wasted.
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  Go on, ruin the party.
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  Happily.
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  The dirty secret is that a huge share of the tasks people rush to automate cost them
• 0:27
  more time than they ever save.
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  They build a clever prompt workflow, babysit it, fix its mistakes, and end up worse off
• 0:35
  than if they'd just done the thing themselves.
• 0:37
  That sounds like an excuse from someone who couldn't get their workflow to work.
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  No, it's arithmetic.
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  There's an old rule from the automation world.
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  The break-even test.
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  If you spend three hours building something to save two minutes a week, you need years
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  to come out ahead.
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  Randall Monroe from the webcomic XKCD made a famous chart about exactly this in 2013,
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  and it still holds.
• 1:00
  AI didn't repeal math.
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  So what actually separates a task worth automating from one that isn't?
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  Three filters, and a task has to pass all of them.
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  One, is it genuinely repeated, same shape every time?
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  Two, is the input predictable?
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  Does it arrive in a format that doesn't surprise you?
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  Three, can you tolerate the AI being wrong sometimes without it costing you a fortune
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  to check?
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  That third one feels like the killer, because you always have to check.
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  That's the trap nobody talks about.
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  The verification tax.
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  The time you spend confirming the AI got it right.
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  If checking the output takes as long as producing it, you've automated nothing.
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  You've just moved the labor and added anxiety.
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  Give me a concrete case where people get this wrong.
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  Classic one, summarizing every email into a daily digest.
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  Sounds productive.
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  But email is unpredictable.
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  Not right.
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  A legal notice and a lunch invite need totally different handling.
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  So the AI mangles the important ones.
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  And now you're reading both the summary and the original to make sure it didn't
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  drop something that matters.
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  Negative return.
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  And the flip side, where it genuinely pays?
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  Converting messy meeting notes into a structured action list.
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  Same shape every time.
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  The input is text you already have.
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  And if it misfiles one item, you spot it in five seconds.
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  That passes all three filters.
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  Companies like Otter and Fireflies built entire businesses on that narrow, boring, verifiable
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  task.
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  And their own numbers.
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  Vendor numbers.
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  So take them with salt.
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  Claim users reclaim an hour or two a week.
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  An hour or two?
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  That's not the revolution we were promised in 2024.
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  Right.
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  And that's the honest bit.
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  The best operators I talked to in 2026 don't chase the revolution.
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  They stack small, reliable wins.
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  Ten tasks that each save six minutes a day beats one glamorous workflow that saves an
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  hour but breaks every Tuesday and needs a babysitter.
• 3:01
  But surely the models are good enough now that reliability isn't the issue it was.
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  The models are better, sure.
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  The problem was never only capability.
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  It's variance.
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  Building a strong model that's right 95% of the time means one in 20 outputs is wrong,
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  and you don't know which one.
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  For anything where a silent error is expensive, invoices, client-facing copy, medical anything,
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  that 5% is the whole game.
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  So how does a normal person actually apply this without building a spreadsheet about
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  their own life?
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  Cheap test.
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  For a week, jot down the repetitive tasks that annoy you.
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  Then for each one, ask a single question.
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  If the AI got this wrong and I didn't notice, what's the damage?
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  If the answer is trivial, I'd catch it instantly.
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  Automate it.
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  If the answer is expensive, and I'd have to check every time anyway, leave it alone.
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  So the filter isn't can AI do this, it's what happens when it fails.
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  Exactly.
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  Capability is table stakes.
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  Consequence of error is the real sorting hat.
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  Give me the one thing to do tomorrow.
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  Pick the single task you already do daily that would be harmless if the AI botched it.
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  Re-formatting, tagging, first draft rewrites, and automate only that.
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  Prove the time-saving is real before you touch anything with teeth.
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  Done.
• 4:22
  Want to know where you actually stand?
• 4:24
  Take the AI self-assessment at MBA-training.com.