World Model Readiness
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Role Checks

Module · What goes to AI, what stays human

The Manager's AI Check

A manager's core act is delegation, and AI just added a second place to delegate to. Get the split right and you free your people for work that grows them; get it wrong and you either waste them on grind or quietly hollow out the judgment they need to develop. This module checks how you decide what goes to AI versus a person, how hard you review what comes back, and whether you are protecting your team's growth while you automate the busywork.

Question 1 of 5 · You know what goes where

Do you have clear reasons for sending work to AI versus to a person?

Delegating to AI purely because it is faster or cheaper ignores what the work does for the person who would have done it. Good criteria weigh the outcome and the development, not just the speed, and they hold up when you have to explain them.

Question 2 of 5 · You review AI-assisted work

When your team ships AI-assisted work, do you review it as closely as the rest?

AI output arrives polished, which makes it easy to wave through on presentation alone. But fluent and correct are different things, and the accountability for what ships is still yours, not the tool's.

Question 3 of 5 · You protect their growth

Are you making sure your people still build judgment AI could shortcut for them?

The hard, slow tasks are often where judgment gets built, and they are exactly the ones AI offers to remove. Hand all of them to the tool and your people ship faster today while getting weaker underneath, until the day they need judgment they never grew.

Question 4 of 5 · You automate the busywork

Have you cut the meetings and reports AI can now handle for your team?

Status updates, summaries, notes and routine reporting are exactly the low-judgment load AI now absorbs well. Every hour of it you leave in place is an hour your team does not spend on the work that actually needs them.

Question 5 of 5 · Your team has norms

Does your team have shared norms for how and when to use AI?

Without shared norms, everyone invents their own line: some overuse the tools and hide it, others avoid them and fall behind. Norms make the expectations explicit, so people can use AI openly and consistently instead of guessing what is allowed.

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.

What decides whether a task goes to AI or a person on your team?

Whatever is fastest
Whatever is cheapest
Case by case gut
Explicit criteria
Not sure

How closely do you review your team's AI-assisted output?

Barely at all
A quick skim
Spot checks
Full review
They use none

Does your team have agreed rules for using AI?

None
Informal habits
Being drafted
Agreed and shared
Does not apply

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 know what goes where

  1. 1No logic
  2. 2Whatever is faster
  3. 3Rough rules
  4. 4Deliberate criteria
  5. 5Criteria your team knows

At the low end: Routing work with no logic means you are optimising nothing and can defend nothing. Write down what makes a task right for AI and what makes it right for a person, then use it. What good looks like: Deliberate, shared criteria are what make your delegation fair and legible. Keep them visible to the team so they understand why work lands where it does, and can push back.

You review AI-assisted work

  1. 1Wave it through
  2. 2Trust the tool
  3. 3Spot checks
  4. 4Review the substance
  5. 5Review and coach

At the low end: Waving AI-assisted work through because it looks clean is how confident errors reach customers. Review the substance of what ships, not the polish, exactly as you would human work. What good looks like: Reviewing substance and coaching from it turns each review into a lesson your team keeps. Keep it up; the review is where your people learn to catch what the tool gets wrong.

You protect their growth

  1. 1Never considered
  2. 2Let it slide
  3. 3Occasional stretch work
  4. 4Protect hard problems
  5. 5Grow them deliberately

At the low end: If you have never considered this, your team may be getting faster and shallower at the same time. Identify the tasks where their judgment actually develops and make sure AI does not quietly take them all. What good looks like: Deliberately growing your people while using AI is the hardest balance in the job, and you are holding it. Keep watching which tasks build judgment; the answer shifts as the tools improve.

You automate the busywork

  1. 1Everything manual
  2. 2Talk about it
  3. 3One or two tries
  4. 4Several automated
  5. 5Ruthlessly trimmed

At the low end: Keeping every report and meeting manual taxes your whole team by default. Pick one recurring status or summary this week and let AI draft it, then keep only the human parts that add judgment. What good looks like: Ruthlessly trimming the busywork is how you protect your team's attention for real work. Keep pruning; new routine reporting accumulates as fast as you remove the old.

Your team has norms

  1. 1No norms
  2. 2Everyone improvises
  3. 3Loose guidance
  4. 4Agreed norms
  5. 5Norms lived daily

At the low end: With no norms, your team's AI use is a patchwork of private guesses. Agree a short set of expectations, what to use it for, what to disclose, what to check, so nobody has to invent the rules alone. What good looks like: Norms your team actually lives by are what make AI use consistent and honest. Revisit them as the tools and the work change; norms set once and forgotten drift out of date fast.