Product breadth was long considered a simple count: how many variants, options and applications can you offer. A broad catalogue was an investment in inventory, purchasing contracts and people who managed the assortment. Whoever could deliver more won the comparison, and that took time to build up. Catching up with a competitor on breadth meant years of expansion.
That assumption no longer holds everywhere. The work behind product breadth largely consists of combining existing components into a fitting offer: matching specifications, configuring variants, putting together quotes based on what a customer needs. That is precisely the kind of work of which AI can take over parts, with a human approving or rejecting the outcome. Where that happens, what a customer experiences as a "broad offering" shifts.
Behind every claim about product breadth lies a process: someone figuring out which combination of components answers a specific request, which variant is technically possible, and which adjustment fits within the margins. That process determines how much breadth a customer actually notices, regardless of what is in the catalogue.
In part of this work, AI can do the first round: searching specifications, proposing combinations, drafting a preliminary quote. A human then assesses whether the proposal is correct and adjusts where needed. In another part, the work remains human work, because the request is too specific or the context is missing to fill in automatically. Which part that is differs per company and per product category, and that difference determines who wins the comparison on breadth.
A customer does not compare the catalogues of two suppliers side by side. A customer asks something specific and notices how quickly and how precisely a fitting answer comes back. At a company where the combination work is largely manual, that takes longer and the answer is more often a standard variant. At a company where AI partly takes over the combining, a proposal arrives faster that lies closer to the specific request, because more combinations could be calculated in the same time.
The effect is not that the catalogue grows larger. The effect is that the effective breadth -- what a customer is actually offered within a reasonable time -- increases without the assortment itself changing. Two companies with the same catalogue can therefore have a different experienced breadth.
Whether this difference occurs depends on how the combination work is currently organised. Companies where specifications, prices and technical conditions are already structured can hand over that work to a system sooner, with human oversight. Companies where that knowledge mainly resides in employees' heads must take that step first before AI can do anything with it.
This is connected to how AI changes competition on digital maturity, because the degree to which data and processes are structured determines which work can be transferred. It is also connected to how AI changes competition on technical knowledge, because combining variants often requires the same knowledge that is also used in technical advice. Whoever has already taken steps on one of these dimensions therefore often has a head start on product breadth, without that having been the original intention.
This is not a statement about staffing levels. Whether a company can manage with fewer people in the combination work is a question with its own legal requirements regarding terms of employment and works council involvement, and that question is not answered here. The point is only that the comparison on product breadth changes when part of the combination work goes faster, regardless of what a company does with the freed-up capacity. Some companies put those hours into more specific quotes, others into different parts of the process. Both choices are independent of the question of whether the competitive position on breadth shifts.
Whether your own offering is broad on paper says little about how broad a customer experiences it. That depends on the work behind the scenes, and that work is organised differently per company. The underlying question -- which work in your company can genuinely be taken over by AI -- is answered per task by FTE TO AI's work scan, independent of how your competitors score.
Whether product breadth is the dimension on which deals are decided in your market is a different question, which is answered in how you know where you truly win. And because AI keeps shifting the work behind these dimensions, a score from a year ago is not automatically still valid; how often that needs to be reviewed is described in how often a competitive analysis needs to be repeated.
As a first step, you can take the free dimension check: you state where you believe you win, and see which of those claims can be defended with evidence. The full benchmark, with your position and that of your peer group on all dimensions, is under construction.