AI Beauty: Can Beauty Tech Make Cosmetics Sustainable?

On May 6, Ming S. Zhao, CEO of Proven, a skincare brand that creates customized skincare for customers using an AI-based tool called the Skin Genome Project, was the first cosmetic brand founder to appear on Shark Tank in 5 years.

Zhao actually launched the company in 2018, but felt the time was right to appear on Shark Tank after having secured customer feedback and initial investments from Y-Combinator and Stanford.

The idea of a female founder of a company from an underrepresented market segment on Shark Tank is newsworthy enough. Yet, there is a deeper story that her company represents: ensuring the definition of “Clean” also means “Sustainable.”

The Proven online quiz, which generates a customized formulation set based on inputs like skin concern, sleep deprivation, or travel, is sleek and easy. You glide through a brief survey and on the other side receive targeted answers for top concerns like ‘what ingredients help with anti-aging?’ and  ‘what’s good for moderate pollution and hard water buildup?’

The ingredient results, which typically feature Squalane as a top ingredient supporting moisturization and barrier repair, include a short profile description of top ingredients – from Vitamin C to Calendula to Acetyl Glucosamine.

This alone is exciting to many consumers looking for science-backed, fully digital, and customized skin care. Yet, although the Sharks may not have seen it, Proven’s innovation could be the tip of the (fast-melting) iceberg.

The Sustainable Potential of AI

According to Deloittes’ 2019 Global Millennial survey, 42 percent of millennials say they would start or deepen relationships with companies or products that have a positive environmental or social impact.

Just as Proven has mastered the integration of multiple databases, so too could it integrate data from sustainability databases to provide information to consumers about their environmental, social, and economic impact. AI could be the great equalizer.

Today, multiple certification programs exist because inputs vary by company depending on size, location, and steps in a product life cycle. Yet advanced machine learning tools could help develop common indexes or at least minimum harmonization no matter what standard or framework you use: be it Ecovadis, GRI Environmental standards, UN Global Compact, or carbon offsetting programs such as the CarbonFund.org.

“Artificial intelligence” is not the same as machine learning. Training a machine over time with data via algorithms to rapidly produce an output isn’t artificial intelligence on its own. But for most consumers primarily concerned about a beauty experience, semantics isn’t important. What they really want is to know their products don’t harm the environment or people.

Clean Ingredients Can Power Clean AI

Pre-Covid19, Nielsen expected consumers to spend up to $150 billion on sustainable products by 2021 in the U.S. alone. In April of last year, Nielsen’s Omnibus study found that products that are environmentally friendly and use recycled packaging are the most important to consumers.

Any beauty tech platform that could facilitate product launches that tap into this desire could be a game changer. We’ve written about the blossoming integration of tech and beauty.  What do L’Oreal, Shisheido, LVMH, and Givaudan and have in common? Besides being global brands, they have invested significantly in digital technology, AI, and machine learning. Just recently at CES 2020, L’Oreal showcased Perso, a personalized color cosmetics tool.

In this context, sustainably sourced ingredients, like Neossance Squalane, are more important than ever. AI could be poised to make the next big leap to assess the full scope of “Clean” by reviewing the block chain process of sustainable manufacturing, testing, and product development. Soon it could be completely seamless.

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