The situation
The board or leadership asked, in one form or another, what the company is doing about AI search. The current answer is a deck built from opinions, a few screenshots, and whatever came up in a Google search that morning.
Why it's a real problem
One follow-up question - "how do we know," "how do we compare to competitors," "what changed since last quarter" - and the whole report falls apart in front of the people who approve next year's budget. A bad AI report is worse than no report, because it spends credibility the next one will need.
What we implement
We build the report from the same instruments we run on our own estate - continuous AI answer tracking, crawler-log analysis, and AI referral segmentation - with every number labeled measured or inferred, so it holds up when someone asks how you know.
What you get
- A report where every figure traces to a method, not a vendor's marketing claim repeated as fact.
- A defensible answer to "how do we compare to competitors", backed by the same citation-tracking data behind the AI visibility audit, not a hunch.
- Reusable structure for the next board cycle, so this becomes a trend line the board can watch move, not a one-off fire drill.
Illustrative scenario
A leadership team walking into a board meeting with only a slide of screenshots might, with a tracked baseline in hand instead, field the follow-up question directly rather than change the subject. (Illustrative scenario - not a measured result.) What the tracked baseline actually shows for your category is the point of building one.
House law applies here too: the report labels every inferred number as inferred, in the deliverable itself - a board report that overstates its own certainty is exactly the failure mode it exists to avoid.