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Money & Vendors

Module · Demos are cheap, production is not

The Post-Pilot Scaling Check

Most AI pilots succeed and then quietly never ship. The demo works, the room applauds, and the thing dies somewhere between the slide deck and a system that runs on Monday. This module checks the five places pilots go to die: the success bar, the production owner, the integration debt, the economics at real volume, and whether your company can ever say stop.

Question 1 of 5 · Success bar was set

Did this pilot start with a number that decides go or no-go?

A pilot without a pre-agreed threshold cannot fail, which means it cannot really succeed either. 'It went well' is not a criterion; 'cut handling time 30 percent across 500 real tickets' is. Set the bar before you see the results, or the results will set it for you.

Question 2 of 5 · Production owner named

Is there a named owner who will run this in production, not just the pilot?

Pilots are owned by innovation teams; production is owned by whoever gets paged when it breaks at 2am. If that handoff has no name and no capacity attached, the pilot has nowhere to land, however well it performs.

Question 3 of 5 · Integration debt scoped

Do you know what it takes to wire this into the systems it must live in?

A demo runs on clean sample data through one friendly API. Production means legacy systems, permission models, edge cases and data pipelines that fight back. That gap is the integration debt, and it is usually where the months and the money go.

Question 4 of 5 · Unit economics hold

Do the per-transaction costs still work when volume is a hundred times the pilot?

Pilot economics flatter you: low volume, subsidised vendor pricing, and a human quietly fixing the model's mistakes off the books. At scale the token bill, the review labour and the error handling all grow together. Model it before you sign, not after.

Question 5 of 5 · Kill discipline exists

Can this pilot be killed, and has your company ever actually killed one?

Pilots that cannot die accumulate as zombie projects, consuming budget and attention nobody will admit is lost. A company that has never stopped a pilot will not stop this one either; it will scale it just to avoid the awkward conversation.

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 many AI pilots has your company run in the last two years?

None yet
One or two
Three to five
Six or more
We have lost count

How many of your AI pilots have reached durable production?

None so far
One
A few
Most of them
Too early to tell

Has your company ever deliberately stopped an AI pilot that was not working?

Never
Once
Occasionally
Routinely
No pilots 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.

Success bar was set

  1. 1No criteria
  2. 2Vague ambitions
  3. 3Set afterwards
  4. 4Number, soft
  5. 5Pre-agreed pass line

At the low end: A pilot with no success number is a demo with a budget. Write one measurable threshold this week, agreed by whoever would fund the rollout, before you read another result. What good looks like: A pre-agreed pass line is what makes a pilot a test rather than a performance. Keep it honest: if the bar is missed, say so out loud and stop.

Production owner named

  1. 1Nobody owns it
  2. 2Innovation team only
  3. 3Owner in name
  4. 4Owner, no capacity
  5. 5Owner with capacity

At the low end: Without a production owner, a successful pilot becomes an orphan. Name the operational owner now, while the pilot is still cheap to shape around what they can actually run. What good looks like: A production owner with real capacity is the single strongest predictor that a pilot ships. Protect their time; it is the asset, not the model.

Integration debt scoped

  1. 1Never assessed
  2. 2Assumed trivial
  3. 3Roughly scoped
  4. 4Mostly mapped
  5. 5Fully costed

At the low end: Unscoped integration is how a two-week win becomes a two-quarter slog. Walk the full path from real source system to real user before you commit a rollout date. What good looks like: A fully costed integration plan is rare and worth its weight. Revisit it as the surrounding systems change; integration debt accrues interest quietly.

Unit economics hold

  1. 1Never modelled
  2. 2Pilot costs only
  3. 3Rough scale guess
  4. 4Modelled, optimistic
  5. 5Modelled and stress-tested

At the low end: A pilot that ignores unit economics can scale straight into a loss. Build a simple cost-per-transaction model at target volume before the rollout conversation goes further. What good looks like: Stress-tested economics turn scaling from a leap of faith into a decision. Keep the model live against real usage; vendor pricing and volumes both move.

Kill discipline exists

  1. 1Never kill any
  2. 2Kill on paper
  3. 3Killed once, painful
  4. 4Kill when needed
  5. 5Routine, blameless kills

At the low end: If nothing ever gets killed, sunk cost is running your portfolio. Give this pilot an explicit stop condition tied to the success bar from question 1, and honour it. What good looks like: Routine, blameless kills are the mark of a company that scales the right things. That discipline is why your survivors are worth scaling.