PromptSharp › Daily briefs › Sales › September 17, 2026
PromptSharp Sales Brief · free web issueSales prompt of the day
September 17, 2026 · for AEs, SDRs, sales leaders, RevOps. One sharp, copy-paste prompt — free, every weekday.
Business case the buyer can edit — assumptions on the outside
Your ROI slide is a black box, so the buyer's finance team discounts all of it.
You are building a customer-facing business case that the buyer is meant to challenge and edit. I will paste the value drivers and their numbers. Produce: A) AN ASSUMPTION BLOCK first, before any result — every input, its value, and its SOURCE marked as one of: customer-provided, industry benchmark, or our estimate. Nothing may be used that is not listed here. B) THE CALCULATION shown step by step in plain arithmetic a non-analyst can follow, with the total benefit, the cost, the net, and the payback period. C) A SENSITIVITY TABLE — the result if the two most important assumptions are 25 percent worse, plus the break-even value of each, so the buyer can see how much has to go wrong before this stops working. D) A CONSERVATIVE CASE stated as the headline, with the optimistic case shown separately and labelled as such. Inputs: [PASTE VALUE DRIVERS, CUSTOMER-PROVIDED NUMBERS, PRICING, IMPLEMENTATION COST AND TIME] Rules: Use only what I give you — do not invent a company fact, a metric, a quote or a name. If something is missing, write "not provided" and ask for it. Mark every claim I would repeat to a customer as needing verification before it leaves my outbox. Never include another customer's confidential information. Never present our estimate as a customer number; if a driver has no customer input, say so in the assumption block and leave it out of the conservative case. Verify every driver and number against the customer's own data before this business case is presented as theirs.
Why it works — Buyers discount ROI models because the assumptions are hidden. Putting them first, labelling their source, and leading with the conservative case inverts the dynamic — the model becomes something finance edits with you rather than something they dismiss.
What changed for Sales
A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning
Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it. We reconcile these observations with a…
arxiv
Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes
Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related risks to health. We present a novel approach…
arxiv
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