Why doesn't a share chart win budget?
Most AI visibility reports end with a share of voice chart. Your brand is named in 22% of answers; the leader is named in 41%. Marketing sees a gap. Finance sees a percentage with no currency attached.
The question a CFO asks is fair: if we close half that gap, what do we get? Answering it doesn't need a black box. It needs five numbers, most of which your company already has.
What does the model look like?
The value of one point of Answer Share, per year:
Value of one point = B × 1% × (S named − S not) × W × D
Where each input comes from:
- B: buyers who use AI
Your yearly count of qualified opportunities, multiplied by the share who use AI during research. Gartner puts that share at 45% of B2B buyers (May 2026).
- S named
How often a buyer shortlists you when AI named you. Ask new opportunities which tools they used, and where they first heard of you.
- S not
How often they shortlist you when AI didn't. The same question, for buyers who didn't see you in an AI answer.
- W: win rate
Your win rate once shortlisted, from your CRM.
- D: deal value
Your average deal value, from your CRM.
The gap between the two shortlist rates is the part that matters. It is the measurable difference that being named makes.
A worked example
Illustrative numbers for a B2B software company. Replace each one with your own:
| Qualified buyers a year | 2,000 |
|---|---|
| Share who use AI during research | 45%, so B = 900 |
| Shortlist rate when named | 50% |
| Shortlist rate when not named | 15% |
| Win rate once shortlisted | 25% |
| Average deal value | US$60,000 |
One point of Answer Share is worth 900 × 1% × 35% × 25% × US$60,000, which is about US$47,000 a year. Moving from 20% to 35% is worth about US$710,000 in won business. The point isn't the figure. It's that the conversation moves from "we want more visibility" to "we think each point is worth about US$47,000, and here is what we assumed". Finance can challenge an assumption. It can't challenge a chart.
Where do the hardest numbers come from?
The two shortlist rates are the inputs most companies don't have yet. Three ways to get them:
Add one question to your opportunity form or discovery call: "Did you use an AI assistant while researching, and did it mention us?"
Ask sales to log it for a quarter. Gartner found 69% of B2B buyers check AI-generated insights with a sales rep, so your team already hears this.
Compare win/loss notes for deals where the buyer mentioned AI with those where they didn't.
Until you have your own data, run the model with a cautious gap and a generous one. If the case only works with the generous gap, that tells you something too.
What can't the model tell you?
It shows association, not proof. Brands that AI names more often may also be stronger in other ways. Answer Share also moves around: we report it as a likely range, not a single number, because the same question can get different answers on different days.
And it leaves out what a wrong answer costs. If AI names you with an outdated price or a market you don't serve, the shortlist rate can fall rather than rise. That's why we check accuracy alongside share.
Frequently asked questions
Does this work for consumer brands?
Yes, with different inputs: monthly category searches instead of qualified buyers, and conversion rate instead of win rate. Adobe found AI-referred visits to US retail sites converted 31% more often than other traffic in the 2025 holiday season.
What if we can't measure the shortlist gap at all?
Use a range, say 10 to 30 points, and present both ends. A range with stated assumptions is more credible than a precise number with hidden ones.
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