World Model Readiness
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Role Checks

Module · Does the model research, or write for you

The Analyst's AI Check

AI can draft an analysis in seconds that used to take you a day, and that is exactly the danger. The speed hides whether you still verified the sources, showed your method, and told the reader what you do not know. This module checks the five habits that separate an analyst who uses AI as a tool from one who has quietly handed over the thinking: source verification, method transparency, real synthesis, data discipline, and honest uncertainty.

Question 1 of 5 · You verify every source

When AI hands you a fact or a citation, do you check it before it goes in your work?

Models invent plausible sources and misquote real ones with total confidence. A citation you did not open is a claim you are making on trust. The reader assumes you checked; make sure they are right.

Question 2 of 5 · Your method is visible

Could a colleague see how you reached a conclusion, including where AI did the work?

Analysis the reader cannot trace is an opinion with a chart attached. If part of the reasoning happened inside a prompt you cannot reproduce, your method has a hole nobody can inspect.

Question 3 of 5 · You synthesise, not paste

Does your output add judgment the model did not, or mostly repackage what it said?

The value of an analyst is the connection nobody else made, the caveat the data hides, the recommendation with your name behind it. If the AI could have produced your deliverable alone, the reader did not need you.

Question 4 of 5 · You guard the data

Do you know which data you can paste into an AI tool, and follow that line?

Analysts handle exactly the material that must not leak: unpublished results, personal records, client figures, deal data. A public model may train on what you paste, and once it is out it does not come back.

Question 5 of 5 · You state what you do not know

Do you tell the reader how confident to be, especially where AI filled a gap?

Models write uncertainty in the same fluent tone as fact, and that fluency erases the caveats you owe the reader. An honest confidence level is worth more than a clean-looking number built on a guess.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

How much of your research and analysis now involves AI tools?

None
Occasional help
A regular part
Most of my work
I cannot work without it

Have you caught AI inventing a fact or source in your own work?

Never seen it
Once or twice
Regularly
Yes, after it shipped
I do not check

Do you have a clear rule for what data may go into AI tools?

No rule
My own judgment
A team norm
A written policy
Not sure

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.

What the five levels look like

Every dimension in this assessment is scored 1 to 5. This is what the levels mean, dimension by dimension. The graded report diagnoses where your own answers land and what to do about it.

You verify every source

  1. 1Never check
  2. 2Check if it looks odd
  3. 3Spot-check some
  4. 4Check the load-bearing ones
  5. 5Verify every cited claim

At the low end: You are signing your name to facts you have not seen. Open every source the model gives you before it reaches a deliverable; a fabricated citation in your work is your mistake, not the model's. What good looks like: Verifying every cited claim is the discipline that lets you use AI at speed without inheriting its fabrications. Keep the standard even when a deadline argues against it.

Your method is visible

  1. 1No trail at all
  2. 2In my head only
  3. 3Rough notes
  4. 4Reproducible steps
  5. 5Documented, prompts included

At the low end: Work nobody can retrace cannot be checked or defended. Start keeping the prompts and steps that produced each conclusion; it is the difference between analysis and assertion. What good looks like: A method a colleague can reproduce, prompts and all, is what makes your work auditable. This is what separates an analyst from a content generator.

You synthesise, not paste

  1. 1Lightly edited AI text
  2. 2Reworded output
  3. 3AI draft plus my edits
  4. 4My structure, AI inputs
  5. 5My judgment throughout

At the low end: Lightly edited model output is not analysis; it is transcription with a byline. Ask what you know that the model does not, and make that the spine of the deliverable. What good looks like: When your judgment runs through the whole piece, AI is an accelerator rather than a substitute. Keep asking what you are adding that a prompt could not.

You guard the data

  1. 1Paste anything
  2. 2Rarely think about it
  3. 3Avoid the obvious
  4. 4Follow a clear rule
  5. 5Rule plus safe tools

At the low end: Pasting sensitive data into a public tool is a leak you cannot reverse. Learn what your organisation classifies as confidential and treat the AI box as a public place until told otherwise. What good looks like: Knowing the line and having sanctioned tools for sensitive work is exactly right. Revisit the rule as you take on new data types; the risky category is often the new one.

You state what you do not know

  1. 1Present all as fact
  2. 2Caveat if pushed
  3. 3Occasional hedges
  4. 4Flag the weak spots
  5. 5Calibrated throughout

At the low end: Presenting estimates as certainties makes your work more dangerous the more it is trusted. Mark what is measured, what is inferred, and what the model simply asserted. What good looks like: Calibrated uncertainty is the rarest and most valuable analyst habit. It is what lets decision-makers trust your strong claims, because they can see you flag the weak ones.