As a starting point for Ai-era search, we don’t need to guess what people are asking. We can look at the last few deals we actually won.
Ai search (and increasingly, plain old search) wants solutions. And most B2B businesses already have the best raw material for this.
It is not in our existing marketing material. It is in our recent sales.
We have a stream of real issues that real companies brought to us, plus the proof of which approach won and why. This is exactly the kind of knowledge that search systems are trying to find.
A simple exercise makes this obvious, and it does not require naming customers.
We take the last few closed sales that our colleagues can remember clearly. Then for each one, we write a short, anonymised ‘case study’ that answers three things:
- What was the problem?
- What did we do?
- Why did they believe it was the best option?
That is always useful, but here’s the important addition: for each deal, we try to name the question that was forming in the customer’s mind before they spoke to us.
Not the vendor-shaped question. The real one.
They are decision questions. And future buyers in the same situation are asking the same thing, even if they phrase it slightly differently.
Once we have those questions, we can do something practical: we can build a set of pages that each answer one question properly. Not a fluffy explainer. A page that shows we understand the situation. That is the kind of content Ai search can use, and human buyers can trust.