Journal 024
The Report Improved Because We Listened
The biggest improvement to our latest AI Readiness report wasn't caused by changing the engine. It happened because we acted on what the previous report told us.
The Report Improved Because We Listened
When we first ran AI Readiness against Wayli, the report was encouraging.
It also highlighted gaps.
Some authority signals were missing.
Our research wasn't easy to discover.
There were opportunities to explain ourselves more clearly.
None of these were software bugs.
They were observations about how our own business appeared from the outside.
So we did something simple.
We followed the advice.
Over the following days we published more research, improved our public signals, added an llms.txt file, and made it easier for AI systems to understand what Wayli actually is.
Then we ran the assessment again.
The score improved.
More importantly, the explanations improved.
The engine recognised changes we had genuinely made.
We hadn't adjusted the scoring model.
We hadn't lowered any thresholds.
We hadn't added exceptions because the website belonged to us.
The report improved because the evidence improved.
That was one of the most reassuring moments since starting this project.
It suggested the system was doing exactly what we hoped it would do.
AI Readiness isn't trying to reward businesses for learning how to score highly.
It's trying to reward businesses that become easier to understand.
That's an important difference.
A score should never be something you optimise directly.
It should be something that changes naturally as your business becomes clearer.
That doesn't just make the report more trustworthy.
It also gives us confidence that the recommendations are worth following.
Apparently, even our own.