Can't Answer These Five? Don't Sign the AI Project
Our positionMost AI projects don't fail in delivery. They fail at the decision to start.
Five questions to settle before funding an AI project: can failure be reversed, is the data in hand, can acceptance be measured, who can call stop, and who maintains it in three months.
You are probably stuck here
- 01The budget is approved, but nobody can say which use case goes first
- 02The vendor deck looks great and the acceptance criteria still can't be written
- 03The pilot ended three months ago and nobody knows whether to continue
If one of these is yours, the rest of this page is worth your time.
After reading, you should be able to answer
- 1Whether this use case should start now
- 2Which clauses the contract has to pin down
- 3Which numbers decide success after launch
Who this is for
Executives, digital leads and procurement signing off an AI budget.
What you'll take away
- 01Ask whether failure is survivable before asking how good the upside looks.
- 02If acceptance can't be written as a number, there is no acceptance.
- 03Without a named owner, launch day starts the depreciation clock.
Method breakdown
- 01
1. Can a failure be reversed?
The risk with AI is not that it performs badly; it is that a wrong output cannot be taken back. Once outbound replies, ledger entries or generated contracts are automated, one mistake gets copied many times. List the irreversible actions in this use case first, then decide which ones keep a human gate.
What this step asks for01- List irreversible actions (send, post, sign, delete)
- Assign a human confirmation point to each one
- State the rollback window and who owns the rollback
- 02
2. Is the data actually in hand?
Most cases of "the AI is inaccurate" are cases of "the data isn't there". Before funding, you should be able to name the three data sets this use case minimally needs, which system holds each one, who can grant access, and how often it changes. If those four answers are missing, a more expensive model will not help.
What this step asks for02- List the minimum data set, not all the data
- Name the data owner and the access path
- State the refresh cadence and what happens when data goes stale
- 03
3. Can acceptance be measured?
"Improve efficiency" is not an acceptance criterion. Before signing, you should be able to write down which 30 to 50 real samples will be used, who labels them, what score passes, and what score fails. The samples must come from the business, not from the vendor's demo set.
What this step asks for03- Build the evaluation set from real business samples
- Write the pass line and the fail line as numbers
- Agree who has the authority to declare a pass
- 04
4. Who has the authority to stop it?
Every AI project needs one person who can call stop and one explicit stop date. Without both, the project extends itself one more iteration at a time and the budget is spent without a decision ever being made.
What this step asks for04- Name a business owner, not an IT owner
- Agree the stop date and the conditions that trigger it
- Decide how data, models and documents are handed over after a stop
- 05
5. Who maintains it in three months?
Launch is not the finish line. Knowledge goes stale, models change, processes get redesigned. If nobody is still watching accuracy, adoption and cost three months later, the system fails quietly and users simply call it unusable.
What this step asks for05- Name the maintainer and a monthly review cadence
- Track three long-run metrics: accuracy, adoption, cost per run
- Document how knowledge is updated and how versions are rolled back
- Back to step 01 — run it again
Three steps are a cycle, not a checklist: after one pass, what step 01 should measure has changed.
Action checklist
Tick what you already have. Whatever is left unticked is your next move.
5 still open. Do not fix them all at once; start with the one that hurts.
Take this page with you
Can't Answer These Five? Don't Sign the AI Project
- Our position
- Most AI projects don't fail in delivery. They fail at the decision to start.
- You are probably stuck here
- The budget is approved, but nobody can say which use case goes first; The vendor deck looks great and the acceptance criteria still can't be written; The pilot ended three months ago and nobody knows whether to continue
- After reading, you should be able to answer
- Whether this use case should start now; Which clauses the contract has to pin down; Which numbers decide success after launch
The copied text carries the source link — safe to drop into an internal doc, mail, or meeting note.
Use these delivery packs next
Reference cases
Freight ForwarderManufacturing CompanyTake this page to your AI
These questions were prepared by CodeLeo. Pick one, then pick an assistant — this page's title, link and summary go along with it. Copy any question to edit it first.
Three clicks, twenty seconds. The questions and the pack below adapt to your answers.
Each assistant requires signing in to that service. Answers are generated by the AI from the page content and do not represent CodeLeo's positions.
New methods & assets, straight to your inbox
Not sure which pack your company needs first?
3 questions identify your capability gaps and the asset pack to start with, based on your industry and stage.
The skeleton is yours now; the full pack ships with the consultant review.