A customer weighing up two suppliers eventually asks a specific question. Does this work with that other application? What happens under this load, this protocol, this exception? The answer given at that moment often decides more than the quotation already on the table. In most b2b markets, technical knowledge is not the knowledge itself, but the speed and precision with which that knowledge reaches the right person at the right moment. A supplier who gives a well-founded answer within an hour beats a supplier who needs three days, even if the underlying knowledge is equal on both sides.
That is the reason technical knowledge is a dimension in this benchmark: not because the sharpest mind wins, but because the turnaround time from knowledge to answer wins.
Behind every technical answer sits a chain. Someone recognises the question, looks up the relevant specification or experience, translates that to the customer's situation, and checks whether the answer is correct before it goes out. At many companies, that chain runs through a small number of people who carry the knowledge in their heads and are not always reachable. At other companies, that chain is captured in documentation, case history and decision rules that someone else can also consult.
That difference determines how much of the work can be taken over. What exists as recorded text, a system can search and summarise. What only lives in the head of a senior engineer, it cannot, until someone writes it down.
The three categories that recur everywhere apply here without exception. Looking up and summarising existing specifications, manuals and earlier answers is work a system can take over, provided the underlying documentation exists and is kept current. Translating a generic answer to a customer's specific situation, including the judgement of whether an exception does or does not apply, is work where a system can make a proposal but an expert approves or rejects it with reason. Assessing a situation for which no precedent exists remains human work.
The shift is not in the knowledge itself, but in who already handles the first two categories today with system support and who still does that entirely by hand. A company where a customer's first question receives a draft answer within a quarter of an hour that an engineer only needs to check, competes at a different speed than a company where that same question ends up in the queue of a single specialist. That is not a difference in knowledge. It is a difference in how much of the preparatory work has already been taken over.
The difference rarely lies in the willingness to change and more often in the state of the underlying information. A system can only answer quickly and reliably if there is something to look up: structured documentation, a consistent history of previous questions and answers, clear decision rules for the edge cases. Companies that have maintained this information for years because it was needed internally for onboarding or quality assurance happen to already have the foundation in place for acceleration with AI. Companies where that knowledge is mainly passed on verbally first need a recording effort before a system can do anything with it.
This has nothing to do with the size of the company or the number of specialists employed. A small team with well-documented knowledge can respond faster than a large department where the knowledge is fragmented across heads. That also makes it a dimension on which the pecking order in the market can shift, regardless of who historically had the biggest technical name.
The customer does not see who uses AI and who does not. The customer only notices the result: how quickly a technical question gets a concrete, correct answer, and how consistent that answer is when the question is asked a second time to a different contact person. A company that relies on an individual specialist scores inconsistently, depending on who is available that day. A company where the answer comes from a shared, searchable knowledge base scores more consistently, with or without the intervention of a system.
This consistency can be measured alongside other aspects of the comparison, such as response time to questions as a competitive advantage, the extent to which digital processes are already aligned with each other, and what a competitor's job postings reveal about where they are focusing internally. Technical knowledge rarely stands on its own; it wins or loses in combination with the rest of the playing field.
Whether AI at a company already partly takes over question answering says little on its own about the quality of the underlying workforce and nothing about what an employer should do with that regarding individual positions; separate legal requirements apply there that are not addressed here. What can be answered is: which part of the technical question traffic at a specific company, given the state of the documentation, can be accelerated. That question is answered per task by the work scan from FTE TO AI.
Before the full benchmark with peer scores becomes available, you can check whether the claim you are already making about technical knowledge holds up. Anyone using precision or speed as a distinguishing capability can test that claim for whether the distinction is also demonstrable and place it alongside other hard factors such as certifications as demonstrable proof of quality. The free dimension check lets you name where you believe you are winning, and shows which of those claims can be defended with evidence. The full benchmark is under construction.