World Model Readiness Betting without betting the company

Module · Betting without betting the company

The Innovation Portfolio Balance Check

AI investment is a portfolio problem wearing a technology costume. Put everything into today's reliable workflows and the future arrives without you; chase every new capability and you spend the year funding demos that never ship. The job is balance: a deliberate split between explore and exploit, gates that let projects graduate or die on evidence, a cadence that reviews the whole set, learning captured from the ones you kill, and no single bet big enough to sink you. This module checks all five.

Question 1 of 5 · Explore and exploit split

Is your AI spend deliberately split between safe bets and exploration?

Exploit spending improves what already works; explore spending buys options on what might. A portfolio that is all exploit optimises its way into obsolescence, and one that is all explore never banks a return. The split should be a decision you made, not a number you can only discover by adding up the invoices.

Question 2 of 5 · Projects pass gates

Do your AI projects have to earn their next round of funding at a gate?

Without gates, projects live on momentum: they keep their budget because they had it last quarter. A stage gate forces the question at each step, has this earned more money, with criteria set in advance. It is how promising work graduates and zombie projects die on schedule instead of forever.

Question 3 of 5 · The whole set is reviewed

Do you review your AI initiatives as a portfolio, not one project at a time?

Reviewed individually, every project has a champion and a reason to continue. Reviewed together, on a cadence, you can see the duplication, the overweight bet, the gap where nobody is working. The portfolio view is the only place you can rebalance, and it only exists if someone convenes it.

Question 4 of 5 · Dead projects teach

When you kill an AI project, do you capture what it taught you?

A killed project that leaves nothing behind is pure loss; a killed project that leaves a documented lesson is tuition. Most organisations quietly bury the failures and relearn them a year later under a new name. The learning is the return on the bets that did not pay.

Question 5 of 5 · No single fatal bet

Is any single AI bet large enough to hurt you badly if it fails?

Concentration is how a portfolio stops being a portfolio. If one initiative, one vendor, one model carries most of your AI hopes and budget, its failure is your failure, and you have no diversification to absorb it. The question is whether any one bet can take the rest down with it.

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 is your AI investment split between safe bets and exploration?

Almost all safe bets
Mostly safe
Roughly balanced
Mostly exploration
We have not looked

When did you last kill an AI project that was not working?

We never kill projects
Rarely
Within the last year
Killing is routine
No projects yet

How concentrated is your AI investment in a single bet?

One dominant bet
A few large bets
Spread across many
Deliberately diversified
We do not know

Your context

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