Beauty companies increasingly use artificial intelligence to transform how they develop and market products, with personalization and convenience at the forefront.
Beauty juggernaut Estée Lauder Companies partnered with OpenAI this month to implement AI across its brands.
The companies built 240 AI applications to analyze consumer data and develop products across its beauty brands.
“We’re thrilled to help Estée Lauder empower employee creativity and better serve customers using ChatGPT Enterprise,” OpenAI Head of Platform Sales James Dyett said in a statement provided to PYMNTS. “Our work together is a perfect example of employees driving AI innovation from the ground up and ELC leadership accelerating their progress and learning along the way.”
The facility processes data from consumer surveys and clinical trials for the company’s beauty brands. Two primary applications have emerged from the development process: one analyzes consumer survey data for fragrance development, while another processes clinical trial results.
Research tasks that previously took hours are now completed in minutes, OpenAI said.
Other beauty companies are investing in AI for consumer-facing and product development. L’Oréal expanded its use of Modiface, an AI and augmented reality platform that lets users virtually try on makeup and experiment with hair colors, streamlining the decision-making process for consumers.
Sephora’s Virtual Artist tool helps customers find their ideal foundation shade using facial recognition and a skin tone database, improving accuracy and inclusivity. The tool has become a key part of the brand’s digital strategy as online shopping continues to grow.
In skincare, Procter and Gamble’s Olay has Skin Advisor, which analyzes a user’s selfie to recommend personalized routines. The approach highlights the increasing role of AI in making beauty solutions more targeted and efficient.
AI is also helping beauty companies gather data and predict trends. Shiseido incorporated AI into its research and development processes, using machine learning to analyze skin conditions and develop more effective formulations. Meanwhile, brands like Nars have experimented with virtual influencers and AI-generated content to engage younger, tech-savvy consumers on social media.
Retailers are using AI to enhance the shopping experience as well. Ulta Beauty, for example, rolled out AI tools to provide personalized product recommendations and predict customer preferences.
Beyond these core functions, Estée Lauder’s laboratory developed tools for creating marketing content and analyzing vendor data. Additional applications continue to be developed based on department needs and technical feasibility.
Three groups staff the facility: business experts, subject matter specialists and technical personnel. Each AI project follows a defined sequence through design, preparation, testing, launch and optimization phases. Teams evaluate applications based on potential impact and development requirements before committing resources.
The laboratory maintains the company’s 75-year consumer data history within its system. Teams standardize their data collection methods before creating new tools and establishing consistent practices across brands.
Business experts from various departments participate in the evaluation process. Subject matter specialists oversee implementation, while technical staff monitor system performance and data security. The laboratory maintains documentation of successful applications and tracks adoption rates across departments.
According to company executives, department requests for AI integration have increased. The laboratory coordinates with individual brands to maintain consistency while accommodating specific market needs.
Teams analyze patterns in adoption rates and adjust deployment strategies based on requirements across regions and brands. Development groups work with brand representatives to identify areas where AI tools could improve existing processes. Applications undergo testing within single brands before wider implementation.
Early data shows reduced timelines for analysis tasks. Technical teams refine applications based on usage data and department feedback. The company prioritizes practical applications over experimental projects, focusing on tools that address immediate business needs.
The laboratory evaluates new AI application suggestions while maintaining existing tools — teams document implementation processes and outcomes. Department heads provide regular feedback on tool performance and integration with existing workflows.
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