The “Time-Saver vs. Work-Creator” test

Before you approve anything AI-related, ask this:

Does it reduce the number of handoffs and decisions… or increase them?

Because AI often “helps” by generating output. But output isn’t the same as value.

The hidden cost is everything that comes after the output:

• checking it
• correcting it
• getting approval for it
• explaining it
• handling the consequences when it’s wrong

That’s where the time goes.

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The two types of AI (you’ve seen both)

Type A: Time-Saver AI

This is the AI that quietly makes people better at their jobs.

It usually does one of these:

• drafts a first version (so humans aren’t starting from zero)
• summarizes long documents or calls
• finds patterns in data people already trust
• fills out repetitive forms
• speeds up triage (“Which bucket does this belong in?”)

Key trait: when it’s wrong, the cost is low.

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Type B: Work-Creator AI

This is the AI that looks great in a demo and then becomes a burden.

It often creates:

• answers that require heavy fact-checking
• outputs that don’t fit the workflow
• “almost right” content that takes longer to fix than to write
• new approval steps because leaders don’t trust it
• new risk because nobody knows what data it used

Key trait: when it’s wrong, the cost is high.

And most of the time, the work-creator AI doesn’t fail loudly. It fails slowly—through rework, confusion, and “we’ll circle back.”

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The 5 questions that reveal the truth (fast)

If you’re evaluating an AI tool or internal AI idea, ask these five questions in plain language:

1) What job does it remove?

Not “what does it generate?” What does it eliminate?

If the answer is vague, it’s probably not saving time.

2) Who owns the outcome?

If AI drafts something, who is accountable for the final result?

If ownership is fuzzy, AI becomes a hot potato.

3) What happens when it’s wrong?

Every AI is wrong sometimes. The question is:

• Does someone catch it quickly?
• Can we reverse it easily?
• Does it create real damage?

If “wrong” creates major consequences, it needs guardrails.

4) Where does the truth come from?

If the AI gives an answer, what is it based on:

• your internal systems and documents?
• or “the internet vibes”?

If you can’t explain the source, you can’t defend the output.

5) Does it fit the workflow people actually use?

If it requires people to change how they work overnight, adoption will stall. AI should slide into the flow—not demand a new one.

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The biggest trap: “AI output” ≠ “AI value”

A tool that produces a lot of words, summaries, and recommendations can feel productive.

But leaders should measure AI in one of two ways:

✅ Either it reduces time…

• shorter cycle times
• fewer handoffs
• fewer meetings
• faster decisions

✅ Or it reduces errors…

• fewer rework loops
• fewer escalations
• fewer surprises
• cleaner consistency

If it’s not doing one of those, it’s probably just noise with a UI.

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The simple way to get AI wins without drama

If you want AI to help without creating chaos, start with the “low-risk, high-repeat” areas:

• meeting summaries + action items
• drafting internal updates and status reports
• first-pass customer replies (with human review)
• internal search: “find me the right document/policy/process”
• sorting requests into categories (routing/triage)
• turning bullet notes into a clean first draft

These are assistive wins—AI helps, humans decide.

Then, once trust is built, you can move toward more automation.

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Closing thought

AI will either:

• make your team faster, calmer, and more consistent or
• create a new layer of checking, approvals, and rework

The difference isn’t the model. It’s the design: ownership, workflow fit, and what happens when it’s wrong.

Question for leaders: Where have you seen AI genuinely save time—and where has it quietly created more work?