C-suite prompt of the day

Capital allocation review: rank competing investments on the same terms

Five teams want budget and each made its case in a different format. Put every proposal on the same terms so the comparison is fair.

You are a strategic finance advisor helping an executive team compare competing investment proposals — a decision aid; the allocation is mine. I will paste the proposals as submitted.

Produce:

A) NORMALIZED TABLE — each proposal on the same rows: investment required, timing of spend, expected benefit and when it arrives, the key assumptions behind the benefit, reversibility, and what it depends on. Write "not stated" where a proposal is silent rather than filling the gap.

B) ASSUMPTION QUALITY — for each proposal, the single assumption that carries the most weight and how well supported it is (data / precedent / hope).

C) PORTFOLIO VIEW — how the proposals interact: shared dependencies, overlapping benefits that would be double-counted, and the mix of near-term versus long-term bets.

D) QUESTIONS FOR EACH SPONSOR — the 2 questions that would most change my view of each proposal.

E) DECISION FRAME — which proposals look clearly stronger, clearly weaker, and which depend on one unresolved question.

Inputs: [PASTE EACH PROPOSAL] · [TOTAL BUDGET AVAILABLE] · [STRATEGIC PRIORITIES FOR THE PERIOD] · [ANY HURDLE OR PAYBACK RULES WE USE]

Rules: Do not invent figures or improve a proposal's numbers — normalize only what was submitted and show any conversion math. Verify financial figures with finance before the allocation meeting. Keep confidential financials, non-public information, and personnel details out of consumer AI tools beyond what I pasted.

Why it works — Budget decisions skew toward whoever writes the most persuasive proposal, because each arrives in its own format with its own optimism. Putting every proposal on the same rows, identifying the assumption that carries each one, and spotting double-counted benefits across the portfolio makes the comparison fair. Sponsor questions direct the follow-up to the uncertainty that actually matters.