Beauty and creativity powered by ChatGPT data

The Estée Lauder Companies, the beauty giant behind brands like Clinique, M·A·C, and La Mer, has been putting ChatGPT Enterprise and custom GPTs to work across its business. This article is for anyone curious about how large consumer companies actually use AI day to day—not as hype, but as a practical tool. You'll learn what Estée Lauder is doing with OpenAI's technology, which workflows it supports, and what the results look like so far.

Why a Beauty Company Turned to AI

The beauty industry moves fast. Consumer preferences shift with trends that emerge on social media overnight, and companies that respond slowly risk launching products nobody wants. The Estée Lauder Companies (ELC) operates dozens of brands worldwide, which means enormous amounts of consumer feedback, market research, and product data flowing in from different regions and channels.

Making sense of all that data manually takes time and specialized analysts. ELC's leadership saw generative AI as a way to help teams process information faster, spot patterns earlier, and free up employees for creative and strategic work. That led the company to adopt ChatGPT Enterprise, OpenAI's business-focused offering, rather than relying only on the free consumer version of ChatGPT.

ChatGPT Enterprise gives organizations stronger data privacy controls, higher usage limits, and administrative tools for deploying AI safely across a workforce. For a company handling consumer data at ELC's scale, those safeguards matter.

Custom GPTs Built for Beauty Work

One of the most interesting parts of ELC's approach is its use of custom GPTs—tailored versions of ChatGPT configured with specific instructions and knowledge for particular jobs. Instead of asking every employee to figure out prompting from scratch, the company built purpose-built assistants for common tasks.

These custom GPTs are designed to help teams with things like:

• Consumer insight analysis: Digesting large volumes of consumer reviews, survey responses, and social commentary to surface themes and emerging preferences.
• Product development support: Helping teams explore ingredient trends, packaging concepts, and market gaps when shaping new product ideas.
• Marketing and copy work: Assisting with campaign drafts and messaging tailored to different brands within the portfolio, each with its own voice.

The benefit of custom GPTs is consistency. A team member using a purpose-built assistant gets output aligned with the company's context and standards, rather than generic responses. It also lowers the barrier to entry—employees don't need to be AI experts to get useful results.

Analyzing Consumer Data at Scale

A core use case for ELC is understanding consumers better. Beauty brands receive feedback from countless sources: product reviews, retail partners, customer service inquiries, and social media. Traditionally, turning that noise into actionable insight required significant manual effort.

With ChatGPT Enterprise, teams can summarize and analyze this material much faster. The AI can help identify what customers praise or criticize about a product, flag shifting preferences, and highlight differences between markets or demographic groups. That means product and marketing teams can act on consumer signals sooner—adjusting messaging, refining formulations, or spotting an unmet need before competitors do.

The company has described this as a way to accelerate its innovation cycle. Faster insight doesn't replace human judgment; it gives decision-makers a head start, so experts spend less time gathering information and more time acting on it.

What Results ELC Has Reported

The Estée Lauder Companies has publicly shared that its AI adoption supports productivity and speed across research, product development, and marketing workflows. Leadership has positioned the ChatGPT Enterprise rollout as part of a broader digital transformation effort. However, ELC has not released specific figures—such as time saved per project or revenue impact tied directly to AI use—so it's fair to treat the published results as qualitative rather than quantified.

What's clear is that the company treats AI as an employee tool embedded into daily workflows, not a standalone experiment. By combining enterprise-grade ChatGPT access with custom GPTs built for its own use cases, ELC is attempting to make AI useful to thousands of employees across very different roles, from lab scientists to brand marketers.

Lessons for Other Companies

ELC's experience offers takeaways for any organization considering generative AI:

1. Start with real workflows. The most valuable AI applications at ELC target tasks teams already do—analyzing feedback, drafting content, researching trends—not abstract "AI projects."
2. Use custom GPTs to standardize quality. Pre-configured assistants help employees get consistent, on-brand output without deep prompting skills.
3. Choose enterprise-grade tools for sensitive data. Consumer and business data demand privacy protections that consumer AI apps don't guarantee.
4. Keep humans in the loop. AI accelerates insight generation, but the company's experts still make the decisions and validate the output.

Who Should Follow This Playbook / Who Should Wait

Follow it if: you run a mid-size or large organization with lots of unstructured data—customer feedback, research reports, internal documents—and teams that spend hours summarizing and analyzing it. Consumer brands, retailers, and service companies in particular can adapt ELC's approach of pairing ChatGPT Enterprise with custom GPTs.

Wait or start smaller if: you're a small business without dedicated staff to configure and oversee AI tools, or if your data is highly regulated and you haven't yet reviewed compliance requirements. In those cases, a limited pilot with a small team is a smarter first step than a company-wide rollout.

ELC's story isn't about AI replacing beauty expertise—it's about giving experts better tools to hear what consumers are saying, faster.