At first, letting AI decide felt efficient.
It suggested structures, ranked options, summarized tradeoffs, and proposed next steps. Decisions that once required effort now arrived pre-packaged. I told myself I was being pragmatic.
I wasn’t.
I was slowly removing myself from the most important part of my work.
Decisions shifted from deliberate to default
The change wasn’t obvious.
I didn’t stop thinking entirely — I just started accepting. AI recommendations became defaults instead of inputs. If something sounded reasonable, I moved on.
Over time, my role shifted from decision-maker to approver.
That’s where things began to break.
AI optimized for plausibility, not consequence
AI is good at producing answers that sound right.
What it doesn’t do is feel the weight of consequences. It doesn’t account for politics, timing, risk tolerance, or downstream impact unless those things are explicitly modeled — and even then, imperfectly.
By letting AI decide too much, I optimized for coherence instead of responsibility.
Judgment gaps widened quietly
The more decisions I deferred, the less I practiced judgment.
I stopped weighing tradeoffs deeply. I stopped defending choices internally. I stopped asking what I would do without AI’s suggestion.
When decisions finally required strong conviction, my judgment muscle hadn’t been trained.
Errors became harder to trace
When something went wrong, diagnosis got messy.
Was the decision flawed — or was the input framing weak? Did the AI misunderstand context — or did I fail to provide it? Because I hadn’t owned the decision process, accountability felt diffused.
That diffusion is dangerous in real work.
Speed replaced responsibility
Letting AI decide felt fast.
But speed without ownership creates fragility. Decisions made quickly but shallowly don’t hold up under scrutiny. When challenged, I couldn’t always explain why a path was chosen.
In professional settings, “the AI suggested it” is not an answer.
Reclaiming the decision loop
The fix wasn’t banning AI from decisions.
It was repositioning it.
AI works best as:
- A generator of options
- A stress-test for ideas
- A mirror for assumptions
Not as the final arbiter.
Once I reclaimed the decision loop — forcing myself to choose, justify, and stand behind outcomes — work stabilized again.
Why judgment must stay human
AI can support decisions.
It cannot own them.
This is why learning approaches like those emphasized by Coursiv focus on keeping judgment firmly human — training people to use AI as leverage without surrendering agency.
Because when AI decides too much, what breaks isn’t just the work.
It’s your ability to trust yourself when it matters most.
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