What is Virtual Beauty Try-Ons ?
Virtual try-ons are becoming a global standard in beauty marketing, offering instant experimentation and high engagement. However, their growing influence introduces concerns in accuracy, bias, consumer expectations, and data privacy.
I chose to respond to the MBA DMB article AI and Augmented Reality at the Service of Cosmetics, written by Samya Ziatt in 2024, because her work explores a fast-growing trend in the beauty sector. She argues that AI and AR “redefine the online shopping experience” by offering a “unique, personalized, and interactive” way to try products. This topic is central in today’s marketing landscape.
Yet, while reading her article, I felt the need to add a more critical perspective. Virtual try-ons create new opportunities, but they also raise concerns about accuracy, data privacy, bias, and consumer expectations. As a student in digital marketing & business based in Shanghai, I see these tools everywhere. They are more advanced here than in most markets. Because of this, I want to enrich the debate and examine not only the strengths of AI and AR, but also their limits and potential risks.
The Appeal of Beauty Tech: Engagement, Speed, Convenience
In her article, Samya writes that brands like L’Oréal, Sephora, and Estée Lauder use AR to “better meet new expectations.” She explains how tools like ModiFace apply lipstick or foundation in real time and how AI “maps facial features in real time” to produce a natural, smooth finish.
These innovations improve engagement. They also reduce hesitation. Many users enjoy trying products instantly, from anywhere, without touching anything. Moreover, there is a rise of phygital experiences in stores. Samya mentions “interactive screens” and “digital tutorials,” which help customers test looks safely.
This vision is true. Beauty tech does simplify discovery. It also supports brand loyalty and reduces returns. She notes that AR tools create “a noticeable reduction in product returns due to unmet expectations.”
However, this positive view hides deeper challenges.
When Digital Beauty Creates Illusion Instead of Accuracy
The first risk is accuracy. Virtual try-ons often produce results that look too perfect. Gloss appears brighter. Foundation looks smoother. Skin texture seems more uniform. Yet the real product may not match this digital illusion.
Samya describes AR as providing a “realistic simulation.” But many simulations are not realistic. They often beautify and correct imperfections.
The danger appears when the virtual result is better than the physical product. If customers buy based on a beautified digital image, they may feel deceived when they see the real effect. This breaks trust, especially for premium brands.
This issue is not small. A McKinsey report shows that 71% of beauty consumers value authenticity over innovation. When AR filters distort reality, authenticity disappears.
Bias in Beauty Tech: When Inclusion Fails
A second problem is bias. AI tools often work better on lighter skin tones. Many datasets lack diversity. When AR struggles to map darker skin, the final simulation becomes inaccurate or distorted. This harms inclusion.
Beauty should empower all users. However, biased algorithms do the opposite. They exclude. They frustrate. And they reduce trust. Therefore, brands must train AI models with balanced data. They must test tools on a wide range of skin types, lighting conditions, and camera qualities.
Data Privacy: The Hidden Cost Behind the Experience
Samya briefly mentions ethical concerns about data protection. AR and AI tools analyse faces, emotions, behaviours, and biometric patterns. These are very sensitive data.
Many users do not know how apps store and access their information. Because some AR solutions come from external tech companies, the risk increases.
According to the European Data Protection Board, facial recognition data is among the most sensitive categories of personal data. Brands must therefore apply strong security standards. They must also communicate clearly.
Without transparency, innovation loses legitimacy.
New Perspectives From Other Articles
This debate becomes richer when linked to other MBA DMB blog posts.
For example, Le Digital: Révolution dans l’Industrie du Parfum explains how digital experiences reshape sensory marketing. Yet it also reminds us that digital cannot replace real emotions. This applies directly to AR makeup. A filter cannot replace the physical texture of a lipstick or the finish of a foundation.
Additionally, Masterclass Retail: An Overview of Phygitalization argues that technology must support human interaction. When AR works alone, it may mislead. When it works with expert advice, it becomes more reliable.
Finally, An Inclusive Revolution in the Cosmetics Industry by Lancôme explores how brands must respond to diversity. This reinforces why AI bias must be addressed early.
Marketing Impact: Innovation vs. Consumer Trust
AR attracts attention. It increases engagement. It also improves conversion.
But the long-term impact depends on trust.
If users expect perfect results because of digital filters, they may feel disappointed. If AI misreads their skin tone, they may lose confidence. If brands do not explain how they collect data, they may damage loyalty.
Therefore, AR must be transparent. Simulations must match the real product as closely as possible. Brands also need clear disclaimers. Something as simple as “Virtual results may differ under natural light” can protect trust.
Conclusion: A Fair and Honest Future for Beauty Tech – AR Should Guide, Not Transform
AR should guide users, not transform them. It must help people test products without creating digital fantasies. When simulations stay realistic and inclusive, they support trust. When they beautify beyond reality, they become a mask.
AI and AR bring value, but they must evolve with accuracy, fairness, and transparency.
Let’s push the industry toward honest, inclusive, and human-centered digital beauty.