Fashion has spent years turning media attention into rankings, dashboards and monetary-style impact scores. Now the same measurement machinery is moving into generative AI. Launchmetrics has introduced AI Visibility, or AIV, a new metric designed to quantify how fashion, lifestyle and beauty brands appear in answers generated by AI systems and to show which editorial sources are shaping those answers.
The launch was reported by Vogue Business on September 9, alongside New York Fashion Week. Launchmetrics is adding AIV to the brand rankings it already produces around major fashion events, placing the new score beside its established Media Impact Value, or MIV. The result is an unusually concrete sign that generative engine optimization is moving from experimental marketing language into the reporting stack used by major fashion communications teams.
The new metric comes with an important limitation. Launchmetrics describes what AIV is intended to measure and exposes several of the inputs and outputs around it, but the complete formula and weighting behind the proprietary score have not been published in enough detail for outsiders to reproduce it independently. AIV should therefore be understood as Launchmetrics' measurement framework for AI visibility, not as an industry-standard unit comparable across every GEO platform.
AI visibility is joining fashion's existing media scoreboard
Launchmetrics has long measured fashion-brand performance through Media Impact Value, a proprietary metric that assigns value to visibility generated across media, influencers, celebrities, partners and owned channels. According to the company, MIV was introduced in 2018 and became a common benchmarking tool for campaigns and major fashion-calendar moments.
AIV extends that measurement logic into AI-generated answers. Vogue Business reports that Launchmetrics' rankings for events such as New York Fashion Week will now include both a brand's MIV and AIV scores, along with a view of how the brand's measured value is distributed between traditional media impact and AI visibility.
That pairing is strategically significant. MIV attempts to quantify how loudly a brand is being discussed across media channels. AIV asks a different question: when consumers ask an AI system about the category, does the brand appear, how is it described, and what external voices are helping shape that representation?
The two scores therefore measure different layers of influence. A brand can generate enormous social and editorial attention without necessarily becoming a prominent recommendation in an AI answer. Conversely, a company with less raw media volume could potentially perform strongly in generative results if authoritative sources repeatedly associate it with the topics consumers ask about.
The dashboard examines what AI systems actually say about a brand
Vogue Business says the new Launchmetrics dashboard is designed to help brands understand how they are represented in responses from systems such as ChatGPT and Google Gemini. It can highlight brands that stand out in AI search, identify the editorial voices associated with citations and compare AI search results across cities.
Launchmetrics' current AI Visibility product page describes a broader monitoring product covering ChatGPT, Claude, Gemini and Perplexity across 10 markets on a weekly basis. The company says brands can monitor an overall AI Visibility Score, competitive ranking, narratives and the sources driving changes over time.
The difference in scope is worth noting rather than smoothing over. Vogue's launch report specifically describes AIV around platforms such as ChatGPT and Gemini, while Launchmetrics' current product materials advertise tracking across four LLM products. The safest interpretation is that AI Visibility is a broader product framework whose launch coverage focused on the systems most relevant to the initial fashion-industry rollout.
City-by-city comparisons expose the geographic instability of AI answers
One of the more interesting features is the ability to compare brand results across cities. Fashion is inherently geographic: a label's cultural position in New York can differ from its position in Paris, Milan, London or Tokyo, and editorial ecosystems vary by market.
Generative systems can reflect those differences. A question about emerging designers, luxury shopping or the most relevant brands in a category may produce different answers depending on market context, available sources and the model's interpretation of local relevance. A single global visibility percentage can conceal those variations.
For international brands, city-level comparison makes GEO measurement closer to traditional market intelligence. Instead of asking only whether a brand appears in AI, communications teams can investigate where it appears, which competitors displace it in particular markets and whether the narrative attached to the brand changes geographically.
That can be especially useful around fashion weeks, where local events generate sudden concentrations of coverage. A runway show may alter the information environment in New York immediately while taking longer to influence answers elsewhere. Measuring before and after an event can reveal whether a burst of media attention also changes the brand's representation in AI systems.
Launchmetrics is connecting AI citations back to editorial “Voices”
The second important layer is source attribution. Launchmetrics says its dashboard can identify the publications and other editorial voices associated with the sources AI systems cite when discussing a brand. That turns AI visibility from a simple mention count into a PR question.
If a brand appears prominently because several respected publications consistently connect it with craftsmanship, sustainability or a particular cultural movement, communications teams can see which narratives are being reinforced. If outdated criticism or an unwanted association repeatedly appears in AI answers, teams can investigate the source ecosystem feeding that representation.
Launchmetrics' terminology is important here. The company has historically used “Voices” to categorize the people and channels contributing to brand performance, including traditional media, influencers, celebrities, partners and owned media. AIV adapts that concept to the sources informing generative answers.
This does not mean a PR team can directly control what ChatGPT or Gemini says. Models choose, retrieve and synthesize information according to systems the brand does not control. But identifying the external sources that repeatedly appear in citations gives marketers a more concrete view of the information environment than simply recording whether the brand was mentioned.
Fashion's GEO conversation is moving toward earned media
Vogue Business reports that agencies are receiving more requests from major fashion houses to incorporate generative engine optimization into communications planning. Rachna Shah, CEO of KCD, told the publication that larger global brands have increasingly asked how the agency is using AI insights and analyzing GEO.
Launchmetrics argues that earned media is particularly important because AI answers frequently rely on external editorial sources to establish what is credible or current about a brand. Its product page currently claims that 89% of what AI says about a brand comes from earned media, while the Vogue report cites broader industry research putting the share above 80%. These figures are vendor and industry claims rather than universal constants, and they should not be generalized to every model or query without methodology.
The underlying strategic point is stronger than any single percentage. AI systems answering questions about fashion need descriptive, comparative and timely information. Deep editorial features, reviews, interviews, podcasts, newsletters and other independent coverage can contain richer context than a brand's product page alone. That makes public relations relevant to AI discoverability even when the resulting interaction never resembles a traditional search click.
“PR is the new SEO” is memorable, but too simple
Launchmetrics CMO Alison Bringé told Vogue Business that “PR is definitely the new SEO,” reflecting the growing belief that earned media shapes generative answers. The phrase captures a budgetary tension, but brands should resist interpreting it literally.
SEO and PR solve overlapping but different problems. A strong owned website remains the canonical source for product information, store locations, policies, corporate facts and structured brand content. Technical accessibility still affects whether systems can retrieve that information. Search visibility still matters because traditional search continues to drive discovery and because search indexes can intersect with AI retrieval.
PR adds something owned content cannot manufacture on its own: independent corroboration and contextual authority. The more useful model is therefore not PR replacing SEO, but communications, editorial strategy and technical discoverability converging around the same machine-readable reputation.
Fashion brands may eventually move budget between those disciplines, as Launchmetrics executives predict, but a durable GEO program is likely to require both. A brand needs a technically coherent owned presence and an external evidence graph strong enough for AI systems to understand how the market talks about it.
AIV is useful precisely because AI visibility is difficult to compress into one number
The appeal of an AI Visibility Score is obvious. Executives want a benchmark that can be compared across brands, tracked over time and placed on the same dashboard as existing performance indicators. GEO vendors across industries are racing to provide exactly that.
The difficulty is that AI visibility is multidimensional. A brand can be mentioned frequently but described negatively. It can rank highly in New York and barely appear in Milan. It can receive strong visibility for a broad category while disappearing from high-intent product recommendations. One model may cite the brand consistently while another rarely does.
A single score necessarily compresses those dimensions through choices about prompts, models, markets, frequency, citation weighting and other variables. Until those choices are fully documented, the score is most useful as an internal comparative indicator within Launchmetrics' own methodology rather than as an absolute measure of how visible a brand is to “AI” in general.
This is not unique to Launchmetrics. Every GEO platform must decide which questions to test, how often to query models, how to normalize volatile answers and how to combine mention, rank, citation and sentiment into metrics. The key analytical question is whether the methodology remains consistent enough that changes in the score reflect meaningful changes in brand visibility rather than changes in the measurement system itself.
The published methodology is not yet enough to reproduce the score
Launchmetrics publicly explains several important elements of the product. It says the tool uses industry-specific prompts around fashion, lifestyle and beauty topics, tracks multiple LLMs across markets, benchmarks competitors weekly and links changes to sources, narratives and events. That gives customers useful context about what the dashboard is designed to observe.
What is not fully disclosed publicly is the complete calculation behind AIV: how individual prompts are weighted, how model differences are normalized, how citation position or recommendation prominence contributes to the score, how geographic results are combined, and how volatility between repeated generations is handled.
Without those details, outside analysts cannot independently reproduce a brand's AIV score from raw model outputs. That does not make the metric invalid; MIV itself is a proprietary Launchmetrics algorithm. It does mean publications and brands should identify AIV as a proprietary score rather than presenting it as an objective industry-wide unit.
NYFW will become a live test of whether media impact translates into AI impact
New York Fashion Week gives Launchmetrics an ideal environment for demonstrating the difference between MIV and AIV. Fashion week produces intense bursts of runway coverage, celebrity appearances, reviews, social conversation and commercial storytelling. Traditional media analytics can show which brands generated the largest measurable impact from that activity.
AIV adds a second question: did the event change what AI systems say? A brand could dominate fashion-week conversation yet see limited movement in generative recommendations if the coverage is repetitive or poorly aligned with the questions consumers ask. Another label could generate less total media value but gain meaningful AI visibility because authoritative stories connect it to a specific trend or category.
That comparison could help brands distinguish virality from retrievability. MIV measures the impact of media exposure under Launchmetrics' established framework; AIV is intended to measure how the resulting information environment manifests inside AI answers. Seeing both on the same event ranking makes the distinction visible to marketing teams that previously had no common dashboard for it.
The real opportunity is narrative diagnostics, not the leaderboard
The easiest use of AIV will be competitive ranking: Brand A scored higher than Brand B. The more valuable use may be diagnosing why. Launchmetrics says its dashboard surfaces themes, narratives and specific publications associated with AI visibility, as well as an activation map showing where brands might focus future communications work.
For a communications team, that can turn a vague complaint—“Gemini doesn't understand our sustainability story”—into a research problem. Does the model lack recent authoritative coverage? Are competitors more frequently associated with the topic? Are the sources being cited in one city different from another? Is an old narrative continuing to dominate newer messaging?
Those questions are actionable even if the exact AIV formula remains proprietary. The score can serve as an alert, while the underlying citations and narratives provide the evidence needed to decide what to do.
Fashion is becoming an early laboratory for GEO measurement
Fashion is unusually well suited to this kind of experimentation because brand perception is already measured through complex mixes of media, influence, geography and cultural relevance. The industry is accustomed to proprietary benchmarks, event rankings and attempts to quantify the value of editorial attention.
Generative AI adds another layer to that existing measurement culture. Consumers can now ask conversational systems which brands matter, what they stand for, which products fit a particular aesthetic and how competitors differ. Those answers can be assembled from years of editorial coverage, current reporting, social discussion and brand-owned information.
Launchmetrics is effectively arguing that this machine-generated interpretation has become a measurable media surface in its own right. Whether AIV becomes a widely adopted standard will depend on transparency, consistency and whether brands can connect score movements to meaningful commercial or communications outcomes.
A new metric does not yet mean a new standard
For now, AIV is best viewed as a notable industry-specific attempt to operationalize GEO. It gives fashion brands a score for AI visibility, benchmarks that score against competitors, compares markets and exposes some of the editorial voices associated with AI citations. During New York Fashion Week, it will sit beside Media Impact Value in Launchmetrics' brand rankings.
That is a meaningful change in how fashion marketing performance is being framed. AI visibility is no longer only a screenshot of a ChatGPT answer passed around a communications team. It is becoming a recurring metric that executives can watch alongside established media indicators.
But the number should not outrun the methodology. Until the full weighting behind AIV is publicly documented, brands should use it as one proprietary lens on generative visibility and examine the underlying answers, sources, cities and narratives before drawing strategic conclusions. The most important development is not that fashion suddenly has a definitive score for AI influence. It is that the industry has decided AI-generated brand perception is important enough to put on the same dashboard as the metrics it has used for years.