Technical knowledge as a competitive advantage long resided in people: the senior who knows exactly which standard applies, the engineer who can interpret a customer question within an hour, the specialist who knows the difference between what works on paper and what works in practice. A customer noticed that difference in the waiting time for an answer, in the depth of a quote, in how many questions were needed before a fitting proposal was on the table. Whoever had the best people won that comparison.
Behind technical knowledge lies mainly the unlocking and application of it: searching documentation, linking specifications to a customer question, finding earlier projects that resemble the current situation, drafting an initial concept answer. That is for a large part pattern work — retrieving and combining information according to a logic that can be repeated. Assessing an edge case, estimating risk in an unusual application, weighing in something that is not in the documentation: that is the part that relies on experience and judgment.
Those two parts are now diverging. Retrieving and combining technical information can be done by a system for a large part of the time; assessing what that produces remains human work, with oversight that approves or rejects with reason. Where this already works this way, the comparison shifts: no longer who has the most knowledge in house, but who puts a substantiated answer on the table faster and who can actually defend that answer when a customer asks follow-up questions.
That changes what a customer sees. Response time to a technical question used to be an indication of staffing and experience; if the first concept is ready within minutes, response time mainly says something about how far a company has come in setting up that process, no longer about the amount of knowledge circulating within it. Two companies with an equal number of senior engineers can therefore offer a completely different customer experience, purely because one has placed the retrieval work with a system and the other has not.
The speed at which this shifts depends on how well the underlying knowledge has been recorded. A company with clear, up-to-date documentation and structured project history can have that process supported relatively quickly; a company where knowledge mainly resides in people's heads and is written down nowhere first needs that foundation before there is anything to automate. That relates to how an organization is structured, not to the quality of the knowledge itself — a company can have excellent specialists and still be slow in this shift, simply because nothing has been recorded to build on.
This dimension also touches on other areas. Digital maturity largely determines whether that foundation is already in place; anyone wanting to know how far companies diverge in this regard can read how digital maturity is changing the competitive position. Certification also plays a role here, because part of the technical knowledge a customer seeks is actually a question about demonstrable compliance; that is addressed in what certification means now that AI is taking over assessment work. And where technical knowledge no longer makes the distinction, the comparison often shifts to price, which relates to the question worked out in why companies increasingly lose on price compared to before.
A customer notices this difference without knowing the cause. A quote that responds just a bit more specifically to their own situation, a technical question answered without detours, a proposal that refers to a comparable project from years back — that feels like knowledge, and it is knowledge, only retrieving it is no longer necessarily the work of the person who signs off on the answer. What a customer does not see is the oversight behind it: who checks the concept answers, on what grounds something is adjusted, and who remains responsible if the answer turns out to be wrong after all. That remains, rightly, human work, and for decisions about how that oversight is structured within an organization, separate legal requirements apply that are not addressed here.
Which tasks within a company's own technical services lend themselves to this shift and which do not differs per company and is mapped out per task with the work scan from FTE TO AI. This is also relevant after an acquisition, when two knowledge organizations with a different degree of recorded knowledge come together and the comparison of their position must be made anew, as described in how you compare the technical position of two merged companies.
The question on which a company believes it wins on technical knowledge can usually be formulated; whether that claim also holds up against the peer group is a different question. With the free dimension check, you name where you believe you win and see which of those claims can be defended with evidence. The full benchmark, with the peer group on all distinguishing dimensions and the evidence matrix behind it, is under construction.