A product page makes promises. Documentation describes. Anyone reading a manual, an API reference, or a changelog isn't reading what a competitor claims to be capable of, but what a user actually has to do to make the product work. That difference makes documentation one of the few sources that says something about the comparison on a dimension, not about the perceived maturity of a company.
The reason this is relevant now is not that documentation has changed. It's that the dimensions on which competitors beat each other are changing as soon as AI takes over part of the underlying work. Delivery time was long a matter of staffing: more people in planning, shorter turnaround time. If a planning process is largely run by a model and a human only reviews exceptions, delivery time is no longer a staffing question but a model question. Anyone who fails to grasp that is still comparing competitors on the old scale while the winning dimension has shifted.
Three types of clues recur in documentation that is worth reading:
These clues are not proof of a marketing claim. They are proof of a product decision, which makes them more valuable than a press release on the same subject.
Documentation often lags behind the product. A feature that has become AI-driven may still be described with the old, manual steps because no one has updated the text. The reverse also occurs: documentation that speaks of "intelligent" or "automatic" processing while the underlying logic is a fixed set of rules without any learning element. The word "automatic" in documentation doesn't mean AI; it only means that a human no longer clicks on it.
Another pitfall is confusing scale with presence. An API that offers a classification function says nothing about how much of the total volume actually runs through that route versus how much still goes through manual exception handling. That depends on adoption within the competitor's customer base, on the size of the dataset the model was trained on, and on how long the function has been available. Without that context, a found parameter is a clue, not a percentage.
Documentation changes slowly. For most products, a check every quarter is sufficient to signal structural shifts; reading weekly rarely yields new information because documentation releases don't happen daily. For a competitor that has just announced a major release, an immediate check is more useful than waiting for the fixed cycle, because manual steps tend to disappear or APIs tend to expand precisely around releases.
Documentation gives a technical layer, but says nothing about how customers experience that functionality, what your own sales people know about it, or how the market responds to it at the point of purchase. That same pattern of AI partly taking over work, partly leaving it under supervision, and partly leaving it with humans is visible in other places: in the requirements and scores of tenders and procurements, in what customer reviews say about the speed and consistency of delivery, and in what your own sales people have long been hearing on the phone about competitors. Anyone who, after an acquisition, has to determine which of the two organizations is ahead on which dimension, will find a comparable approach in how you line up the position of two merged parties.
The underlying question is not what a competitor publishes, but which work in your own company can genuinely be taken over by AI; that is answered per task with the work scan from FTE TO AI.
Name the two or three dimensions on which you believe you're beating the competitor whose documentation you just read. Delivery time, accuracy, response time to changes: whichever you name, check whether that claim is still on the old scale or on the new one. The free dimension check shows which of those claims can be defended with evidence and which rest on assumption. The full benchmark, with an evidence matrix per dimension, is under construction.