22 TRENDING TECHNOLOGIES
simulations, no matter how sophisticated, remain fundamentally speculative unless validated by human expertise and clinical trials. Without these checks, there’s a risk of brands using AI to create overly optimistic or even deceptive visual claims. Consumers could be misled into believing that skincare products will deliver miraculous results that, in reality, may not materialise. Lack of transparency - when consumers
see before-and-after images, they assume these visuals represent real results. However, with AI simulations, the line between reality and fiction blurs. How can consumers know whether the visual they are seeing is a true representation or a speculative outcome generated by AI? This lack of transparency risks undermining consumer trust in both the brand and the broader industry. Potential for regulatory non-compliance
- international regulatory bodies have strict guidelines around cosmetics marketing, especially when it comes to claims about product efficacy. If brands start using AI tools to simulate results without properly substantiating those claims through clinical trials or scientific evidence, they may face regulatory backlash. Already, we are seeing regulatory bodies exploring new guidelines for AI-generated content in marketing, and brands could find themselves at the centre of legal battles if they are not cautious.
Regulatory challenges: navigating AI The regulatory landscape is still catching up with the rise of AI in beauty marketing, and as such, introduces a grey area where the lines of responsibility and verification can become unclear. There is a growing need for regulatory bodies to develop specific guidelines that address the use of AI-generated simulations in marketing. These may include: ■ Transparency requirements - mandating that brands disclose when AI simulations are
PERSONAL CARE January 2025
being used in place of actual photos. ■ Efficacy validation - Ensuring that any AI-generated visual claims are backed by scientific data and clinical studies. ■ Ethical guidelines - Setting standards for how AI should and should not be used in marketing, especially when it comes to vulnerable populations like young consumers who may be more easily misled by idealised beauty standards.
AI consumer tools Generative AI has transformed cosmetic marketing, offering highly realistic simulations that show consumers what they might look like after using a product for a certain period. These AI tools, powered by ‘posh’ machine learning algorithms (essentially advanced computer programs), analyse vast datasets of skin types, ages, and conditions to make well- informed predictions about how a product could impact an individual’s appearance. The potential benefits are immense —
personalised skincare, cost savings, and focused consumer targeting. However, much like airbrushing skewing perceptions of beauty, AI-driven simulations could similarly mislead consumers if they are not grounded in real clinical results or proven product efficacy. The challenge lies in ensuring that AI-enhanced claims are as authentic as they are compelling! Several AI-driven platforms are already
making waves in the beauty industry, offering tools that, when used correctly, can enhance both product claims and consumer experiences. Some examples include: ■ Revieve Oy - An AI-powered platform that analyses facial skin concerns and recommends personalised skincare solutions.7
Revieve uses
machine learning to predict product efficacy but supplements these predictions with actual dermatological expertise. ■ Proven Skincare - This brand utilizes AI to create customised skincare regimens based
on data from over 20,000 skincare ingredients and millions of consumer reviews, offering claims that are personalised but grounded in real-world outcomes.8 ■ ModiFace (L’Oréal) - One of the pioneers in virtual try-on and skin analysis, ModiFace uses AI to simulate the effects of skincare products on consumers’ faces,9
but these simulations
are carefully vetted by L’Oréal’s in-house scientists and dermatologists. ■
Haut.Ai - Focuses on generative AI technologies designed to improve skincare analysis and personalisation. Their SkinGPT,10 utilises multimodal generative AI to model skin conditions and forecast how skin may react to various skincare products over time. This feature enables users to upload images and visualise potential skin changes.
AI and scientific literature screening For the scientific researchers, one area where AI has helped for many years, is for screening vast amounts of scientific literature, which can be highly beneficial when supporting product claims. However, it too also comes with challenges.11
Pros of AI tools in scientific literature screening 1. Efficiency in processing large data sets - AI tools can process and analyse vast amounts of scientific literature much faster than human researchers. This is particularly useful in the cosmetics industry, where there are a growing number of studies on ingredients, skin conditions, and formulations. AI can quickly identify relevant studies, extract key information, and even summarise findings. This enables brands to substantiate their claims more efficiently and keep up with evolving research. 2. Enhanced accuracy and data mining - AI can detect patterns and correlations in research that humans might miss. In cosmetics, for example, AI can identify subtle trends in
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