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Overestimation Bias in AI Productivity Claims

Research on how knowledge workers systematically overestimate AI productivity gains due to cognitive biases.

Studies show that high-literacy users exhibit reverse Dunning-Kruger effects with AI tools...

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Practice prompt

Re-write your Overestimation Bias in AI Productivity Claims prompt with explicit success criteria and critique instructions.

Try this now

Run the Analyzer pack twice (manual vs. AI) and compare the Overestimation Δ.

Common pitfall

Skipping reviewer verification time hides the real cost of rework and hallucinations.

Key takeaways

  • Run a manual vs. AI comparison to see actual lift.
  • Capture Overestimation Index and micro-TLX together.
  • Document what “good” looks like so teams can replicate it.

See it in action

Drop this into a measured run—demo it, then tie it back to your methodology.

Next Steps

Ready to measure your AI impact? Start with a quick demo to see your Overestimation Δ and cognitive load metrics.

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