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Open BetaWe’re learning fast - your sessions and feedback directly shape AI CogniFit.

Measure · Track · Improve

Ship decisions faster without quality debt.

Instrument executive briefs, experiment canvases, and stakeholder narratives so every AI claim is backed by manual vs. AI evidence.

Pain points

Where time keeps slipping

  • Rework loops after exec reviews when AI-boosted claims lack proof.
  • Verification fatigue from chasing screenshots, prompt logs, and messy notes.
  • Unclear ROI when experiments mix throughput, hallucinations, and compliance risk.

How we help you ship faster

How we help

  • Baseline literacy and run the Innovation PM pack twice to see real AI lift.
  • Coach Overestimation Δ with Fair Trial guardrails before you brief leadership.
  • Use micro-TLX pulses to keep discovery, experiment, and readout weeks humane.

Performance Expectations

Understand when AI tools accelerate your work and when they might slow you down

Expect lift when...

Expect drag when...

How to measure: Lift is real when time-to-passed-review improves and TLX doesn't spike. Learn about validity

Try this first

Innovation PM Sprint Pack

Three tasks (brief → canvas → exec narrative) run twice to capture Overestimation Δ, TLX, and evidence quality before leadership reviews.

Manual vs. AI liftOverestimation ΔFair Trial checklist

Resource playlist

Apply these next

  • Guide

    Mastering Prompt Engineering: A Complete Guide

    Structure prompts with intent → context → critique loops so PM narratives stay auditable.

    Open resource
  • Playbook

    Avoiding AI Overestimation: The Reverse Dunning-Kruger Effect

    Spot when confident storytellers outrun the scored output and reset expectations early.

    Open resource
  • Checklist

    Building AI Ethics into Your Workflow

    Thread ethics checkpoints through Fair Trial templates so scaling plans stay unblocked.

    Open resource

Innovation PM FAQ

Questions teams ask first

How is our data handled? Is it private?+

Analyzer runs stay in your Supabase project space. We log timing, TLX, and Overestimation Δ—never raw prompt content. Need to purge? Admins can delete any run in seconds.

What makes the benchmarks credible?+

Every task captures manual vs. AI timing plus scored quality. We normalize deltas across cohorts and show where Cronbach’s α holds so you know when variance is acceptable.

Can I compare “with vs. without AI” for exec updates?+

Yes. Each pack forces two attempts and stores the lift, TLX, and confidence notes. Export the tiles to show execs why you’re scaling—or pausing—an initiative.

Bring proof to every stand-up and exec review.

Measure manual vs. AI runs, trend TLX, and drop Overestimation Δ tiles into your docs so the team trusts every recommendation.

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