World Model Readiness
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Money & Vendors

Module · The costs the pitch left out

The AI Budget Reality Check

The vendor quote is the smallest number you will pay. This module checks whether your budget accounts for the work that surrounds every AI system: cleaning the data, governing it, integrating it, keeping it running, and retraining it as reality drifts. It also asks the uncomfortable questions: is there contingency, and who signs off when the real number lands?

Question 1 of 5 · Data work is budgeted

Does your budget include the cost of getting your data ready for AI?

Most AI budgets assume the data is usable. It rarely is. Cleaning, labelling, connecting, and maintaining data is often the largest line item, and it is the one vendors are happiest to leave off the quote.

Question 2 of 5 · Full TCO is known

Have you costed the whole life of the system, not just the licence and the build?

Integration, governance, monitoring, support, and retraining run for as long as the system lives. A budget that stops at go-live is a down payment mistaken for a purchase price.

Question 3 of 5 · Running costs are funded

Is there ongoing budget to maintain and retrain the system after it launches?

AI systems decay: the world drifts away from the data they were trained on. Without a standing line for monitoring and retraining, performance quietly erodes until someone notices the results stopped being useful.

Question 4 of 5 · Contingency exists

Is there a contingency reserve for the parts of this that will cost more than planned?

AI projects surprise on the downside more than the upside. A budget with no reserve is a plan that assumes the first estimate, made with the least information, was correct.

Question 5 of 5 · Sign-off is honest

Does the person who signs off the budget see the real total, not the optimistic one?

Numbers get trimmed on the way up to make the case approvable. If the decision-maker approves a figure everyone privately knows is too low, the overrun is booked before the project starts.

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 was your AI budget set?

From vendor quotes
Internal estimate
Full TCO model
No formal budget
Not sure

How much contingency does your AI budget carry?

None
Under 10 percent
10 to 25 percent
Over 25 percent
Prefer not to say

Have past AI or IT projects come in over budget?

Rarely
Sometimes
Usually
Almost always
No track record yet

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.

Data work is budgeted

  1. 1Not considered
  2. 2Vaguely aware
  3. 3Rough allowance
  4. 4Estimated properly
  5. 5Costed and reserved

At the low end: A budget that assumes clean data is a budget missing its largest line. Before approving anything, get an honest estimate of the data preparation this needs; it is usually the real cost. What good looks like: Data work costed and reserved is a mark of a serious budget. Keep the reserve ring-fenced; data preparation has a way of expanding into whatever room you leave it.

Full TCO is known

  1. 1Licence cost only
  2. 2Build and licence
  3. 3Some running costs
  4. 4Most of the life
  5. 5Full lifecycle TCO

At the low end: A licence-only figure is a fraction of what the system will cost to own. Rebuild the number to include integration, governance, monitoring, and retraining before it goes up for approval. What good looks like: A full lifecycle TCO is what separates a purchase price from a down payment. Revisit it yearly; the running costs are the ones that drift as the system grows.

Running costs are funded

  1. 1One-off budget only
  2. 2Hope it lasts
  3. 3Small maintenance line
  4. 4Funded maintenance
  5. 5Funded retraining cycle

At the low end: A one-off budget funds a system that will quietly decay. Add a standing line for monitoring and retraining now, or plan for the day the results stop being trusted. What good looks like: A funded retraining cycle is what keeps performance from eroding. Tie the spend to measured drift, so you retrain when the data says to, not on an arbitrary calendar.

Contingency exists

  1. 1No contingency
  2. 2Token buffer
  3. 3Standard 10 percent
  4. 4Sized to risk
  5. 5Reserved and governed

At the low end: No contingency assumes the least-informed estimate was correct. Add a reserve sized to the risk before approval; the first number is always the most wrong one you will ever have. What good looks like: A risk-sized, governed contingency is mature budgeting. Keep it visible and controlled, so it absorbs genuine surprises rather than quietly funding scope creep.

Sign-off is honest

  1. 1Optimistic number signed
  2. 2Gaps not disclosed
  3. 3Rough figure approved
  4. 4Realistic total signed
  5. 5Honest, contingency included

At the low end: A decision-maker who approves the optimistic number owns an overrun they never saw. Put the real total, contingency and running costs included, in front of whoever signs. What good looks like: An honest total signed with contingency included is how budgets survive contact with reality. Protect the practice: the pressure to trim the number on the way up never stops.