A claim about quality is only substantiated when there is a source attached to it: a score, a comparison, a document that continues to exist when someone asks further questions. "We are the best in service" is an opinion. "We score X on response time, the peer group scores Y, source: customer survey Z" is a claim that holds up in a boardroom, a tender, or a due diligence.
A manufacturer of technical components, 180 employees, used the sentence "we deliver the highest quality in the market" in every pitch. When a buyer at a major client asked what that was based on, no answer came — only "that's what our customers say". The deal went to a competitor who put three figures on the table: error rate, delivery time, warranty claims per year. Not because those figures were more favorable, but because they existed. A claim without a source is interchangeable with any other claim in the market, and that is precisely why all competitors sound the same in their promises: everyone shouts quality, no one shows it.
Substantiation starts by separating two things that are often merged: the dimension on which you claim to score, and the evidence that substantiates that score. A service provider with 90 employees claimed "personal attention" as a distinguishing capability for years. Only when that was broken down — response time to questions, number of contact moments per project, customer satisfaction on communication, each with a figure and a source (CRM data, survey, contract terms) — did it become visible that the claim held up on two of the three points and was actually weaker than the peer group on one point. That third point was exactly where the competition won. A claim you cannot break down into measurable components with a source is a claim you also cannot defend when someone tests it.
A score without a reference point says little. "95% customer satisfaction" sounds high until it turns out the entire sector is at that level. Substantiating a quality claim requires a comparison with the companies you actually compete with for the same deal — the same dimensions, the same measurement method, placed side by side. This is also where things go wrong after a merger or acquisition: two organizations that both claimed to be market leader on quality only discover, when combining figures, that their definitions of "quality" had nothing to do with each other. Anyone who needs to re-determine after an acquisition where the combined organization stands can read how that is approached in how you compare your position after an acquisition.
Not every dimension you claim is equally well substantiated, and that is usually not necessary either — until the moment a competitor wins a deal on precisely the weak dimension. A wholesaler with 220 employees claimed to lead on five dimensions: price, delivery time, assortment, service, quality. Substantiation on four of those was solid. On the fifth, no internal document, customer survey, or figure turned out to exist — the claim was floating on the management team's perception. That is the dimension on which competitors get in, because no one checks it until a deal is lost. A first impression of where that leak lies is provided by the free loss-on-price check: eight questions that indicate which dimension in your offering is most vulnerable to price pressure, without requiring a complete study for it.
A substantiated claim does not guarantee a deal. What it does is move the conversation from credibility to substance: a buyer, an investor, or a regulator who asks "what is that based on" gets a source instead of a story. That changes the nature of the discussion, not automatically its outcome. A claim that holds up is specific enough to be contradicted — and then turns out to hold up under scrutiny.
Take the quality claim that recurs most often in your own pitches, quotes, or annual report and ask: what source is behind this, and on which dimension could a competitor dispute it? If that question does not produce a clear answer, that is the starting point, not the end point. Closing a gap between claim and evidence is, after all, execution work — in processes, in people who collect and maintain data, in systems that make it measurable — and that costs time and capacity that is not distributed the same way everywhere. What that concretely costs and which part of it can be absorbed by AI is mapped out by the work scan at ftetoai.com.