Beauty no longer depends on the eye of the beholder, but is at the fingertips of the GenAI prompter. Based on its impact on the beauty industry alone, GenAI could contribute $9 billion to $10 billion to the global economy, and early actors are already testing the technology, but scaling up these experiments will be a challenge given the pace of GenAI's innovation.

Once the leaders of the beauty industry successfully deploy GenAI at scale, the gap between laggards and leaders will only widen. Fast movers will become faster, more agile, and better able to anticipate and meet consumer needs, while laggards may find it difficult to hold on to their meagre market share.

Beauty industry players should focus on priority use cases and tailor GenAI to their needs to realize the full potential of this technology.

Four use cases for GenAI in the beauty industry

More than a dozen GenAI use cases applicable to the broader consumer sector are also applicable to the beauty industry, covering all aspects of an organization, from front end to back end, from user experience to customer support.

In prioritizing use cases, we took into account the beauty industry's reliance on getting products to market quickly and responding quickly to consumer feedback, and based on this, the following four GenAI use cases are likely to have the greatest impact: Hyper-personalized targeting, experiential product discovery, rapid packaging concept development and innovative product development are all use cases at different stages of GenAI tool adoption, some of which (e.g., GenAI customer chatbots) are already quite widely used by beauty industry players, while others are emerging but promising.

Hyper-personalized positioning

One of the most important things a beauty brand can do to survive in the highly competitive beauty industry is to develop a unique value proposition, but beauty industry players must also ensure that their carefully positioned products reach the consumers most likely to accept them.

Today, most beauty companies can only target a few consumer segments because of their limited ability to personalize messages on a larger scale, and this broad consumer segmentation approach has left much of the market untapped, but with GenAI, beauty brands can create hyper-personalized marketing messages, according to our observation, This can increase conversion rates by up to 40%.

AI can analyze massive consumer data sets, detect patterns, and create micro-subgroups based on pattern recognition algorithms. From there, beauty brands can train their GenAI platform using a variety of inputs, including customer data, inputs that describe the brand's voice, and product information. When entering new markets, beauty brands can train GenAI models based on internal product data as well as external market research such as customer surveys, and GenAI can then create and test different variations of text and images to find what resonates best with each consumer segment.

Consider a hypothetical automated text sent to a fictional customer named Camille. The beauty brand knew that Camille lived in France, had low annual expenses and had recently purchased a facial sunscreen. Camille has responded positively to promotions in the past. Before GenAI, an automated text sent to Camille might have said, "Exciting news! New products come out. Up to 20% off shopping promotions." After using GenAI, automated text might say, "Bonjour, Camille! You know what? Get 20% off our exclusive cleansing foam for facial sunscreen removal now! It goes perfectly with your recently purchased facial sunscreen."

Marketing professionals should review AI-generated messages before sending them to ensure they reflect the brand's philosophy and value proposition, while avoiding plagiarism or potentially harmful associations. Seemingly innocuous messages can damage a brand's image. In the previous example, the greeting GenAI created might say, "Good evening, beautiful lady," instead of "Bonjour." Customers may find the tone of the message offensive or inappropriate, or contrary to the overall philosophy of the brand. The marketing team should provide feedback to the GenAI model - perhaps scoring its output via a like or tap mechanism, and entering detailed comments in a free text field, which the GenAI platform can then process and turn into new training data.

Beauty brands will also need to integrate their GenAI model with assets in Digital Asset management (DAM) systems, which are repositories for all digital creative assets used by brands, as well as with the brand's campaign management tools. GenAI can classify the creative assets in the DAM system - a task that would otherwise have to be done manually. This automation saves time for marketing teams, allowing them to focus on higher-value tasks.

Even if large beauty companies continue to work with marketing agencies on brand strategy and dedicated campaigns, they may consider investing in in-house hyper-personalization capabilities, which will bring two major advantages: companies can use their own consumer data to train GenAI models, and they can create and test personalized communications with greater speed and flexibility.

Experiential product discovery

Despite the advances in consumer technological innovation in product discovery over the past few years, there is still a lot of room for improvement. For example, the answers provided by the first generation of consumer chatbots are relatively fixed and can be frustrating for consumers to use. When a consumer asks for a blush recommendation for dark skin, a chatbot might provide a generic list of products, rather than personalizing the conversation for a particular shopper and engaging with them in depth. While helpful, virtual makeup trials can be malfunctioning or fail to accurately reflect how the product will look in actual use. In these cases, online purchases often result in expensive returns, as returned beauty products are often not resold.

Genai-powered chatbots can help improve the shopping experience and reduce the likelihood of returns, and these large language model (LLM) chatbots, trained on large amounts of product data and consumer preferences, can respond to a wider range of questions and provide more personalized recommendations, both of which help increase conversion rates. A global lifestyle business has developed a GenAI-powered shopping assistant that has increased conversion rates by up to 20%.

The virtual tryon experience, which has proven successful in other consumer categories such as accessories and eyewear, can also be enhanced with GenAI. Using the same technology that powers the image GenAI tool, consumers can see how they are using different products in different scenarios, or see potential improvements to the appearance of a product that can be used over time. For example, online shoppers looking to tone down their blemishes can virtually try the product by uploading a photo to a beauty brand's website and running a program that simulates the effect the brand's blemish serum is likely to have on their skin in the coming months.

GenAI can also enhance experiential product discovery in physical stores. Today, interactive touch-screen displays in stores showcase products available both in-store and online, allowing customers to browse SKUs, select items they want to view in person, or scan QR codes for exclusive offers. Despite their limited capabilities, these displays have been shown to improve in-store shopping experiences and conversion rates. GenAI can improve the effectiveness of these displays. For example, when a shopper with location enabled in a beauty brand's app walks into a company's store, GenAI can generate personalized content for that consumer based on customer profiles and purchase history. Given what we know about the effectiveness of personalized content, these principles can be applied to store environments, although large-scale implementation has yet to materialize.

Rapid packaging concept development

When evaluating beauty products, consumers consider both the product itself and its brand and packaging. Beauty brands often spend months developing new brand and packaging concepts - a process that often requires designers, copy editors, strategists, and packaging experts to hammer out ideas.

GenAI doesn't necessarily eliminate this process, but it can dramatically speed it up, and here's how it works, with packaging designers asking the GenAI platform for the following tips: "Show me five packaging options for night moisturizers that emphasize skincare benefits and sustainable packaging materials." Designers then modify the output of the GenAI platform based on information about customer preferences, which may come from focus groups and customer surveys. Next, AD designers use mock-up images of the new packaging in digital ads, testing whether the images appeal to consumers based on how the new AD interacts on the Web. This data is then used to further refine the GenAI-driven concept creation and prototyping. With this basic approach, one beverage company reduced its concept development time by 60%.

Innovative product development

Creating new beauty product formulations is a multi-year process that requires beauty companies to work with research institutions to study ingredients and test formulations to determine the safety, stability and efficacy of new products.

GenAI can speed up this process, and once the GenAI model is trained on a beauty product's bill of materials, raw material usage, process parameters, internal research data, and other data (such as product patents or previous product trials), it can identify the ingredients that are likely to be most suitable for a new product, predict the efficacy of the product, and recommend formulations.

In the case of a night moisturizer, suppose a formula scientist could prompt the GenAI tool to create a new formula that emphasizes neuropeptides, a popular skincare ingredient, prioritizing anti-aging benefits while reducing the cost of the formula. Once the tool has created a potential formulation, scientists will conduct laboratory tests to assess the compatibility and stability of the ingredients in the formulation, as well as additional safety, consumer testing, and clinical trials (if applicable). Based on consumer feedback, the formula will continue to be iterated.

While the physical testing process still takes time, McKinsey's analysis found that GenAI tools can reduce the development time for new products from weeks to days, which can help save up to 5 percent in raw material costs when developing these products.

Buy, lease or build?

The GenAI enterprise platform market is growing, but which approach, if any, is best for beauty businesses?

There are three ways businesses can introduce GenAI tools, which we call the taker, shaper, and maker approaches. Most beauty companies probably won't take the maker approach, where companies build their own large language models (LLMS) from scratch. This will require more capital expenditure and investment in talent than most beauty companies can reasonably explain, and it may needlessly distract beauty companies from their core competencies, however, there are two other ways beauty companies can still derive value:

• Acquirer method. The acquirer approach involves integrating off-the-shelf third-party GenAI solutions into an enterprise's workflow with little customization required. This is the least costly and resource-intensive of the three methods, making it an attractive option for beauty brands that rely on retailers for distribution (and therefore have less consumer data to customize their models), have less technical talent, or have less money to invest.

When evaluating GenAI tools or platforms, beauty companies should ask the following questions: What data privacy and encryption protocols are implemented at the vendor? Will the vendor use the brand's data to train third-party or first-party proprietary models? Who owns the copyright of the output? How easy is it to integrate with the beauty company's internal systems? (For example, does the vendor have an application programming interface? Do they integrate with platforms such as Google Analytics to support a wider range of use cases?)

Of course, pilot testing of tools is crucial. Most reputable GenAI vendors offer low-cost pilots for a limited time, usually about a month.

• The shaper approach. Being a shaper means using a company's own data and insights to train third-party GenAI models to meet specific geographic, industry, organizational, and business needs. For example, to enable hyper-personalized targeting, data may include information such as brand tonality, customer demographics and preferences, or successful marketing campaigns. For innovative product development, raw data from clinical trial results can help train models.

Large beauty brands or retailers with rich consumer data may opt for the shaper approach. They will need a team of technical talent that can add new components to GenAI tools, integrate them into existing workflows, and deploy them across the organization.

Beauty companies can apply GenAI using a mix of acquirer and shaper approaches, depending on their specific needs and use cases. For beauty companies, speed (that is, speed to market and speed to respond to consumer demand) is particularly important. Therefore, beauty organizations should consider adopting modular GenAI components, which make it easier to switch between large language model providers and thus easier to scale. GenAI may streamline and automate processes in the beauty industry, but the industry is both a science and an art, and involving humans to examine risk and inject unique human creativity is critical in areas like marketing and packaging design.

How to implement GenAI at scale

To stand out in the digital and AI space, consumer packaging companies should consider key questions such as "Where is the value?" And "Are business unit leaders actively involved in the transformation?" In addition, there are four steps beauty companies can take to truly integrate GenAI into their business:

• Align leadership views on vision, values and roadmap. To transition from the pilot phase to the scaling phase, beauty companies should determine which of the four use cases described earlier in this article will deliver the greatest revenue boost, time and cost savings, and customer experience impact. To calculate this potential and build a roadmap accordingly, executives must assemble leaders from diverse functional areas such as marketing, customer service, and product development.

• Empower. While GenAI holds great promise, to use it effectively over the long term, beauty leaders need to assess how it fits into and is supported by organizational capabilities, including operating models, data and technology practices, and talent. Companies should set up cross-functional teams to assess the organization's existing capabilities and the need for additional capabilities. These teams should deploy skills upgrading programs to help address capability gaps within their teams.

• Test, learn, improve, repeat. Beauty companies should test GenAI's output in a controlled environment to determine what works. For example, in A marketing use case, a beauty company would select a channel (such as email, SMS, or paid media) and use A/B testing to measure the effectiveness of an AD created by GenAI, both quantitatively (such as sales impact or click-through rate) and qualitatively (such as by asking, "Does the AD fit the brand image?"). ). The GenAI platform can then be trained based on these learning outcomes to produce better results in subsequent tests.

• Adopt a risk framework. Beauty products often resonate with consumers on an emotional level. Because of this and the highly social nature of the category, beauty companies must establish strict guardrails to prevent and control the risks involved in using GenAI. This risk framework should consider the interpretability and reliability of GenAI's output, security threats, impaired or biased fairness, intellectual property infringement, risks of using third-party AI tools, and privacy concerns. GenAI should augment, not replace, the work done by a beauty company's marketing or product development team.

While many products in the beauty industry are cosmetics, GenAI's application in the beauty field goes far beyond the surface. Combining this technology with other digital and AI tools and boosting organizational capabilities could enable leaders in the beauty space to stand out in the coming years.