This article is part of the Future of AI, a series exploring how artificial intelligence will shape the fashion and beauty industries in the coming years.
It is likely that AI will eventually touch every aspect of our lives. For fashion brands to stay relevant, they must therefore understand the evolving desires, behaviors, and concerns of consumers who are themselves adapting to an AI-driven world. So what do fashion-conscious consumers actually want from AI?
To find out, we surveyed 250 readers of Vogue, Vogue Business, and GQ in the UK, the US, and Europe. The key finding is that consumers are cautious, and the current execution of early AI applications in fashion is what’s holding them back. Those who have used AI for shopping are primarily motivated by convenience and efficiency, but many are held back by concerns over trust and authenticity.
That said, significant opportunities exist for brands that can successfully balance convenience with authenticity.
Current Perception of AI
Our survey revealed a gap between consumers’ general use of AI and their use of it for fashion and beauty shopping. Broader usage of AI chatbots like ChatGPT, Google Gemini, and Perplexity is relatively high: 43% of respondents use them always or regularly, 26% use them occasionally, and 32% use them rarely or not at all.
However, adoption of AI for fashion and beauty shopping is still in its early stages and inconsistent. Over half (54%) have never used AI for this purpose. Among those who have, usage is occasional rather than habitual. Only 2% of respondents always use AI chatbots when shopping for fashion and beauty, while 12% use them often.
When consumers do use AI chatbots for fashion and beauty shopping, the majority (63%) use OpenAI’s ChatGPT, followed by Google Gemini (38%) and Anthropic’s Claude (23%). Just 10% use Perplexity for shopping.
Respondents are more likely to trust influencer recommendations (27%) over AI chatbots (8%), though 49% trust neither. Those who trust influencers often see fashion as an inherently human domain. With over 70% saying they would never trust an AI influencer, there is a general consensus that AI lacks the personal, emotional, and creative touch of a real person—as one respondent noted, “AI can’t physically try the products,” unlike influencers where you can “see the clothes on their body.” Ultimately, consumers understand that influencer recommendations can be biased, but they feel they can recognize these nuances, whereas AI feels “more murky,” according to another.
When asked about their biggest concern regarding fashion brands using more AI, responses were fairly evenly split. Almost a quarter (23%) fear a loss of creativity most. Another fifth (19%) worry about the technology replacing human jobs, while a similar number (18%) fear reduced human interaction in fashion. Data and privacy is the top concern for 17%, and a loss of the luxury feeling concerns 11%.
These findings suggest consumers are not opposed to AI in fashion, but they are concerned it could diminish the creativity, humanity, and exclusivity that define the industry. Forty-six percent agree that AI is exciting and promising for fashion’s future, and 58% agree it can be a tool to aid creativity. However, less than a quarter (24%) think AI-generated images and videos for brand campaigns can be as valuable as human-made content, and just over half (51%) would view a brand more negatively if it used AI to create a luxury fashion or beauty product. The findings point to a disconnect: efficiency can sometimes erode desirability.
AI in Fashion Should Be Discreet
For luxury brands, the most immediate opportunity…The real opportunity for AI in luxury retail lies not in consumer-facing tools, but in enhanced behind-the-scenes systems that customers never directly interact with. As technology advances, physical stores offer a space for luxury brands to differentiate themselves by using what might be called “invisible AI”—applications that improve the customer experience without requiring active engagement or extra data sharing.
In-store shopping remains the preferred way to buy luxury goods among our respondents, with 40% favoring physical retail and another 37% using a hybrid approach. The main reasons are to check quality (43%) and fit (31%). Our survey makes it clear that consumers do not want to feel like they are interacting with AI when visiting a luxury store. Two-thirds (66%) say their shopping experience would suffer if an AI robot assisted them in person. This underscores a lasting need for human interaction, but it also shows where AI can work quietly in the background. Examples include AI-driven inventory management to ensure products are in stock, or clienteling tools that help sales associates with relevant insights and highly personalized service.
Across the survey, trust emerged as the central barrier to adoption—both in terms of AI’s ability to make good recommendations and the data it needs to function. While 69% of respondents use AI chatbots at least occasionally, fewer than a quarter (24%) trust their fashion and beauty suggestions, and more than half (55%) actively distrust them. Beyond skepticism about AI recommendations, there is also a strong underlying concern about data privacy, security, and control, especially regarding sensitive information. While nearly half (49%) would share relatively low-risk data like dress size, clear limits appear around financial and behavioral data: 72% would not share card details, 46% would not share browsing history, and 40% would not share location data. As one respondent put it: “I don’t care if my dress size gets leaked in a data breach. I care if my card details do.” Respondents noted they would only feel comfortable sharing sensitive information like card details if it was encrypted by a trusted third party.
This creates a structural challenge for advanced AI shopping applications, especially those led by AI agents. Only 31% would delegate shopping to an AI agent, even if it knew their taste and purchase history. Even among those open to an AI assistant, acceptance depends on several conditions. Respondents cite accuracy, transparency, and security as prerequisites, along with a strong desire to stay in control. “I would rather keep the control. I don’t want it to make the purchase for me,” said one. Another added, “If it becomes too easy and automatic, I might lose control of my shopping habits.” For many, the perceived risks outweigh the convenience.
Our data reveals an interesting contradiction: consumers’ biggest shopping frustrations are in areas where AI adoption is still weakest. This suggests that what’s holding back uptake is the performance of AI tools, not a lack of demand.
When asked how useful they find AI chatbot recommendations for fashion and beauty shopping, 38% of respondents are undecided, while 35% find them mostly useful. Just 1% think they are “entirely useful.” Trust is also limited. Fewer than a quarter (24%) say they trust the recommendations and summaries from AI chatbots, and over half (55%) express distrust—even though 60% claim to understand how AI gathers information and makes decisions. This points to a gap between awareness of AI and actual confidence in it.Fashion is deeply personal and subjective. When people were asked about their biggest challenges with fashion and styling, the top answers were “putting together outfits with items I already own” and “finding my style within my budget.” These issues are familiar to the fashion and tech industries, which have seen a wave of AI styling and shopping tools like Daydream, Doji, Alta, and Phia emerge to address them. However, awareness and optimism about these tools remain low: 30% say they don’t know what AI shopping assistants are, and only 11% use them for shopping. Just 6% have tried a virtual try-on app, though 25% would be interested.
Even though the biggest shopping challenges are style-related, only 11% use AI for personalized recommendations. Inaccuracy appears to be the main barrier: only 2% say AI “always” gets their style right, with most saying it only “sometimes” (62%) or “never” (21%) does.
The performance of these tools is the key issue. Many respondents find the outputs misaligned or generic. “A machine cannot understand the nuances of what might inspire me,” said one. Another added, “They are just going to input information I could find online… I look for information that can’t be sourced online when looking for fashion and style advice.” Since styling involves taste, identity, and cultural context, AI’s recommendations often don’t feel dynamic enough. Until they become more nuanced, adoption among fashion-forward audiences will likely stay limited. Only 3% of respondents turn to AI chatbots for style inspiration, compared to magazines (57%), street style (47%), and influencers (35%).
The takeaway is that most people still see taste and curation as fundamentally human skills. This creates an opportunity for brands to reposition AI tools not just as data-driven systems, but as extensions of human expertise that reflect taste and brand identity.
In this sense, taste could become a system in itself. This might involve clearer storytelling about how these tools are built—from the data they’re trained on to the creative logic behind their outputs. For luxury brands, this storytelling could go further, framing AI styling tools through heritage by drawing on archives, house codes, and design history. Branded AI styling experiences online could serve as both inspiration and product discovery, offering a significant opportunity to upsell inventory.
However, luxury brands often lack the infrastructure to encode their aesthetic in a scalable way. Archives may not be digitized or tagged for AI use, and data systems are often fragmented. There’s also a risk that even with ample brand data, AI could produce off-brand results that cheapen the image, making human oversight essential at every stage.
The Paradox of Personalization
Personalization has long been touted as a key benefit of AI in retail, but our survey suggests its value is less straightforward, especially in luxury. The challenge is compounded by how algorithms predict behavior, narrowing options to what’s already known. “Just because I bought something once or have browsed for something, it doesn’t mean that’s all I keep wanting,” one respondent noted. Others find that recommendations become repetitive, limiting discovery rather than enabling it. “This is why I’m struggling to find anything new. AI bots are taking away from traditional browsing.”
Much depends on striking the right balance with personalization.Fashion brands must navigate using AI without alienating their luxury customers. The areas where consumers are comfortable with brands employing AI are subtle and may evolve, and there is a risk that personalization could feel limiting rather than helpful.
As one Vogue Business survey respondent put it, “Shopping with AI’s help is just boring.”
Concerns around uniformity also exist. Some respondents question whether AI-driven recommendations might lead to a homogenization of style, especially among consumers with similar profiles. “Shopping with their help is just boring. None of the excitement I would expect to have, no discoveries, just force-fed info,” said one respondent. Another flagged, “I’d worry that people with similar tastes to me would get the same outfits.”
Despite these concerns, consumers do not outright reject personalization. They are open to guidance but not restriction. Lighter-touch applications that improve service are welcomed, while deeper automated systems that feel impersonal are viewed with caution. For luxury, the opportunity lies not in maximizing efficiency, but in balancing predictive suggestions with an element of surprise. The goal remains to offer customers not just what they expect, but something they didn’t know they wanted.
Methodology and Demographics
Vogue Business conducted a 10-minute quantitative online survey, shared with readers of Vogue, Vogue Business, and GQ in the UK, the US, and Europe. The research was carried out by an internal Condé Nast custom research team between March 16 and April 7, 2026. Respondents were required to be aged 16 or over.
In total, 251 respondents completed the survey. Among them, 33% were under 35 and 65% were over 35. Female respondents comprised 76% of the group, while male respondents made up 22%. Geographically, over half (55%) were based in the UK, a quarter (24%) in the US, and the remaining 21% across Europe. In terms of spending profile, just under half (45%) are considered aspirational customers, defined as those with an annual income of less than 100,000 in local currency (USD, EUR, or GBP). Thirty-seven percent earn over 100,000 a year, and the remaining 19% preferred not to answer.
Frequently Asked Questions
Of course Here is a list of FAQs about consumer views on AI based on the theme You Cant Trust a Machine
Beginner Definition Questions
1 What does this survey mean by AI
It refers to the artificial intelligence you encounter daily like chatbots recommendation algorithms voice assistants facial recognition and automated customer service
2 Whats the main finding of the survey
The core finding is a significant trust gap While people use AIpowered services all the time they are deeply skeptical about its fairness privacy and reliability especially for important decisions
3 Who was surveyed
The survey typically includes a broad representative sample of adult consumers not just tech experts to capture general public sentiment
Concerns Problems
4 What are peoples biggest worries about AI
The top concerns are Privacy Bias Fairness Lack of Transparency and Job displacement
5 What does AI bias mean
It means an AI system can produce unfair outcomes like favoring one group over another For example a hiring algorithm might unfairly screen out resumes based on gender or ethnicity if it was trained on biased historical data
6 Dont machines make fewer mistakes than humans
Not always Machines are great at repetitive tasks but can fail in unpredictable ways or amplify errors in their training data They also lack human common sense and empathy which can lead to frustrating or harmful mistakes
7 Why do people feel they cant trust AI with important decisions
Because AIs decisionmaking process is often a black box People want to understand the reasoning behind a loan denial a medical diagnosis or a news recommendation which many AI systems cannot clearly explain
Examples Daily Life
8 Where do I encounter AI that I might not realize
Beyond obvious ones like ChatGPT you find it in social media feeds spam filters navigation apps predicting traffic fraud detection on your credit card and even in the filters on your photo app
9 Can you give an example of an AI trust issue from real life
A common example is a