L’Oréal brings AI into everyday digital advertising production

Digital ad production on a global scale has become less about one distinct campaign and more about volume, speed and consistency. For consumer brands operating across dozens of markets, the challenge isn’t creativity alone, but how to keep content flowing without repeating expensive production cycles.

This pressure is prompting some major companies to examine where AI fits into day-to-day marketing work. At L’Oréal, AI-generated creative tools are used to support parts of the digital advertising process, especially video and visual content. The goal is not to replace human teams, but to reduce friction in a system that requires constant updating.

This shift provides useful insight into how enterprise AI adoption will play out in creative functions, where speed and control are as important as originality.

Expand the scope of content without expanding the scope of production

For a global beauty group, digital advertising is no longer a seasonal exercise. Content is constantly needed across social platforms, e-commerce sites and regional campaigns, often with small differences in language, format or visual focus.

Traditional production models are struggling to keep up. Each new asset typically involves planning, filming, editing, and approvals. AI-generated images and video elements allow you to reuse old content and expand it into new formats without having to start from scratch every time.

At L’Oréal, AI tools are used to help create or adapt visual content that suits specific digital channels. This includes polishing footage, adjusting formats, and creating versions for different platforms. Human teams continue to monitor creative direction and final deliverables, but AI is speeding up the time between idea and delivery.

Practical value is not about producing something completely new. It’s about producing enough usable content to meet the pace of digital advertising.

Why does L’Oréal keep AI under tight creative control?

One reason big brands are moving cautiously toward using AI in creative work is because of the brand risk. Visual identity, tone, and messaging are tightly regulated, and small discrepancies can be magnified when content is widely distributed.

Instead of handing off creative decisions, companies like L’Oréal are using AI as a support layer. The outputs generated by the AI ​​are checked, modified and approved using existing workflows. This maintains accountability to internal teams and external agencies, while still gaining efficiencies.

This approach reflects a broader pattern in AI adoption in organizations. Tools are inserted into already existing workflows, rather than reshaping how decisions are made. In marketing, this often means that AI helps in production, not in defining the brand voice.

Cost, speed and repeatability

Digital advertising budgets are under pressure, even for large consumer groups. Media prices fluctuate, platforms change their limitations, and audiences expect constant updates. Artificial intelligence offers a way to absorb some of these pressures by reducing the marginal cost of producing additional assets.

By reusing footage and applying AI-based enhancements, brands can increase the value of each shot. This is especially important in regions where campaigns need to change quickly, or when local teams want specific assets but lack large-scale production support.

The result is not a significant cost reduction in one area, but rather additional savings across hundreds of simple decisions. Over time, these savings determine how marketing teams plan campaigns and allocate expenses.

What does this say about the maturity of AI in enterprises?

L’Oréal’s use of AI-driven creative work is less about experience and more about operational convenience. Tools are used in situations where output is predictable, quality can be measured, and errors may be caught before release.

This reflects how AI is being adopted in many enterprise functions. Instead of broad, open use, companies are defining narrow tasks that AI can reliably help with without introducing new risks. In marketing, these tasks often fall between the creative concept and final distribution.

This approach also underscores a major limitation. AI works best in environments that have data, rules, and auditing processes in place. Creative freedom still belongs to the people, while AI supports scale.

Implications for marketing teams

For marketing leaders, the lesson is not that AI will replace agencies or in-house creatives. The problem is that production models designed for slower cycles are becoming more difficult to maintain.

Teams are being asked to deliver more content, more often, with lower budgets and faster delivery. AI tools provide one way to manage this demand, but only if they fit existing controls and expectations.

This imposes new demands on governance. Marketing teams need clear rules about where AI is used, how the outputs are reviewed, and who remains accountable for final decisions. Without this structure, the risks can quickly be offset by the efficiency gains.

What L’Oréal’s approach to adopting AI in organizations indicates

What stands out in L’Oreal’s approach is its restraint. AI is being applied where it reduces friction, not where it reshapes the role of creative teams. This makes it easy to integrate into larger organizations with well-established processes and brand collateral.

As more companies look to AI for productivity gains, similar patterns are emerging. AI becomes part of the workflow, not the headline. Success is measured by time saved and consistency maintained, not by freshness.

For now, the creative work generated by AI remains a supporting act in enterprise marketing. Its real impact lies in how quietly it changes the economics of content production, one asset at a time.

(Photo by Helio E. Lopez Vega)

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