Existence
“I know this company or technology exists.”
A founder is asked a simple question by a prospective buyer: what exactly is this? The answer that follows runs to five minutes. It borrows terms from three adjacent technologies, leans on a comparison to something unrelated, and ends in a product demo. By the end, the buyer is polite, interested and no closer to understanding the idea than when they asked.
In that situation, the product may not be the problem. The market may simply lack the language and context needed to place it. Buyers cannot evaluate what they cannot describe, and they rarely commit budget to something they could not explain to a colleague.
This is the problem of technology category awareness: not whether people have heard of a company, but whether they hold a working mental model of what its technology is, what problem it addresses, and where it sits among the options they already know. This article sets out a methodical way to build that understanding, and describes when the right decision is not to name a new category at all.
People make sense of new offerings by matching them to something familiar. When a technology fits an existing shelf, such as payroll software or endpoint security, marketing can concentrate on differences. When it does not, several difficulties compound.
The terminology is unfamiliar, so buyers cannot search for it or describe it to colleagues. The benefits are abstract. Category boundaries are unclear, so it is uncertain which team, budget line or analyst report the technology belongs to. The real alternative may be a spreadsheet or doing nothing. Use cases are speculative, the problem may be barely recognised, and no agreed criteria exist for comparing vendors.
Two things follow. First, “I have heard of it” and “I understand what it is and why it matters” are different states, and most awareness measurement captures only the first. Second, B2B purchases are rarely made by one person. The 2025 Edelman–LinkedIn B2B research, a survey of nearly 2,000 decision-makers and executives, reports that more than 40% of B2B deals stall because of misalignment within the buying group.Documented research A technology one champion understands and five colleagues do not is exactly the kind that stalls.
Familiarity does not convert automatically into confident use either. McKinsey’s November 2025 survey of 1,993 respondents found nearly nine in ten organisations reporting regular AI use, yet nearly two-thirds had not begun scaling it across the enterprise.Documented research The survey does not measure understanding, so the gap should not be attributed to it alone, but awareness and the ability to apply a technology are clearly separate accomplishments.
It helps to treat awareness as layered rather than binary. A buyer can know a company exists without understanding what it does, why the problem matters or who should use it. This progression is a way to locate where a market is stuck. Strategic interpretation
“I understand what it does.”
“I understand where it fits among the alternatives.”
“I have enough evidence to take it seriously.”
“I would evaluate it as a potential solution.”
This is a diagnostic, not a funnel. Real buying journeys are less linear: buyers skip layers, return to earlier ones, and different members of a buying group sit at different layers at once. Most technology marketing invests heavily at the two ends, in visibility and demonstration, while Understanding, Relevance and Context are left to the buyer to work out alone.
Consider a hypothetical company that monitors how AI systems behave after deployment. Led by technology, the explanation is “a real-time observability layer for model behaviour.” Led by the problem, it sounds different:
Illustrative example, not a real company
Teams deploy AI systems and then have no reliable way to know when they begin behaving differently. The consequence is that errors surface through customer complaints rather than through monitoring. Existing monitoring tools were built for software that behaves the same way each time, which is not how these systems behave. A different approach is needed, and that is what this technology provides.
The second version also tests whether the problem is recognised at all. If buyers cannot see it without first being taught the technology, the education has to begin earlier than the product.
If a company uses five terms for its product and buyers use five others, discovery and understanding both suffer. A shared vocabulary means agreeing on a category name (or a plain-language description where none exists), holding definitions constant across product pages, FAQs, glossary entries, educational articles and executive commentary, and acknowledging the terms buyers already use. It is not keyword repetition. It is describing the same problem and solution the same way wherever a buyer meets them.
Our digital presence and content systems keep definitions, messaging and terminology consistent across a company’s website, resources and channels.
Inconsistency costs trust. In Gartner’s 2025 survey of B2B buyers, 69% reported inconsistencies between a supplier’s website and what sellers told them.Documented research The survey concerns sales generally, not emerging technology, but an unfamiliar technology described differently in different places is harder to trust as well as harder to understand.
The strongest category education gives buyers useful knowledge before they are prospects. It answers the questions a careful buyer would ask an independent expert:
Content that does this earns a different kind of attention. In the Edelman–LinkedIn research, 53% of decision-makers said that when a company’s insight content is strong, brand recognition matters less.Documented research These are self-reported attitudes, not proof that content overcomes a weak product, but for a lesser-known company explaining an unfamiliar technology the direction is useful: buyers appear willing to give attention to a source that helps them think.
A company explaining its own category is an interested party, and buyers know it. Understanding builds faster and holds longer when others are also discussing the category: independent media, analysts, academic researchers, conference programmes, partners, customers describing the problem in their own words, and third-party reviewers.
The measure is not the volume of coverage. It is whether the category is being discussed by credible people and organisations beyond the company itself. Public relations can help credible outsiders understand the problem and the evidence, but it cannot manufacture authority, and coverage that repeats a company’s own claims adds little.
Our PR and reputation services focus on earning credible third-party coverage, not repeating company claims.
Analysts show why this matters. The Gartner release that warned about agent washing also predicted that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing cost, unclear business value and inadequate risk controls.Documented research Independent voices can shape a vocabulary, and they can puncture one.
Founders and subject-matter experts are often best placed to translate unfamiliar technology into understandable ideas. They have watched the problem occur and know where existing tools fall short. Our earlier piece, Why Founder Branding Matters for SaaS Companies, covered the value of that visibility in its own right.
For category awareness the role is narrower. It is to reinforce one consistent explanation: the same definition of the problem, the same account of what the technology is, and the same honesty about what it is not. Founder visibility supports the company’s explanation. It does not replace product clarity or evidence.
Awareness without evidence stays superficial. Buyers who understand the idea need reasons to believe it works: customer examples, demonstrations, benchmarks, implementation accounts, measurable outcomes and independent validation.
What matters is the quality of the claim. Good evidence explains not only that a technology works, but where, for whom, under what conditions, and where it does not. A vendor willing to state when its technology is a poor fit is easier to believe when it says when it is a good one.
Gartner’s cancellation forecast is a reminder of the stakes. The poll behind it is reported to have drawn on webinar attendees rather than a representative sample, so it is directional.Reported industry observation Even so, “unclear business value” among the stated reasons suggests buyers are wary of claims that are vague about outcomes.
We have written separately about How AI Search Is Changing Technology Brand Visibility and How AI Is Changing the Way Buyers Discover B2B Software, so we will not repeat that here. The point for category work is that no single channel carries the understanding, which is why consistency across the ecosystem matters more than any one piece of content.
Our AI visibility and discoverability services help companies see how search and AI tools describe their technology.
The following questions are intended as a practical audit. Honest answers of “not yet” are more useful than reassuring ones.
If it takes a paragraph, or three comparisons, the market will struggle to repeat it.
If the problem only makes sense once the solution is explained, the education starts too late.
Check the website, sales materials, executive commentary and press descriptions against each other.
Relative to the tools, teams and budgets they already have.
People and organisations whose interests are not tied to ours.
Customer accounts, independent validation and results with stated conditions.
Drawn from sales conversations and support queries, not from what is convenient to publish.
Including the alternative of doing nothing.
Different spokespeople should be recognisably describing the same thing.
A category that claims to suit everyone is not yet understood by anyone.
Category creation is not always necessary, and is sometimes counterproductive. Making an existing category easier to understand is a different task from inventing a new one, and often the better one. A company may be better served by positioning within an established category, defining a sub-category, redefining a use case, or simply explaining an existing technology more clearly.
Unnecessary terminology has real costs. Buyers must learn a new term before they can search for it, budget for it or defend it internally. Analysts and publications may have no place to file it, and search and AI systems have little context to connect it to. A category name nobody understands creates a translation burden the company must carry alone. The mortgage-backed securities study shows innovators succeeding by adjusting how an existing category was understood.Documented research
A practical test: if the established category name plus one clarifying sentence describes the product accurately, use them. A new category is worth pursuing only when the problem genuinely falls between existing ones, and even then it depends on other people adopting the language.
