Creating digital advertising on a global scale has become less about one standout campaign and more about volume, speed, and consistency. For consumer brands operating in dozens of markets, the challenge is not just creativity, but how to keep content flowing without repeating costly production cycles.
This pressure is leading some large companies to test where AI fits into their daily marketing operations. At L’Oréal, we use AI-generated creative tools to support parts of our digital advertising process, especially video and visual content. The goal is not to replace human teams, but to reduce friction in systems that require continuous updates.
This shift provides a useful perspective on how companies’ AI adoption is unfolding in the creative sector, where speed and control are as important as originality.
Grow your content without scaling your production
Digital advertising is no longer a seasonal activity for global beauty groups. Content is continually needed across social platforms, e-commerce sites, and local campaigns, often with slight variations in language, format, or visual emphasis.
Traditional mass-produced models are struggling to keep up. Each new asset typically involves planning, filming, editing, and approval. AI-generated image and video elements allow you to reuse old content and extend it to new formats without having to start from scratch every time.
At L’Oréal, AI tools are used to help generate or adapt visual content to fit specific digital channels. This includes polishing the footage, changing the format, and creating versions for different platforms. While human teams continue to oversee the creative direction and final deliverables, AI shortens the time from idea to reality.
Practical value is not about creating something completely new. It is important to create content that is usable enough to keep up with the pace of digital advertising.
Why L’Oréal puts AI under strict creative control
One reason major brands are wary of implementing AI in creative work is brand risk. Visual identity, tone, and messaging are tightly regulated, and small inconsistencies can be amplified when content is distributed at scale.
Companies like L’Oréal are using AI as a support layer rather than handing over creative decision-making. Outputs generated by AI are inspected, adjusted, and approved using existing workflows. This increases efficiency while maintaining accountability to internal teams and external agencies.
This approach reflects a broader pattern in enterprise AI adoption. Rather than changing the way decisions are made, tools are introduced into existing workflows. In marketing, this often means that AI helps create a brand voice rather than defining it.
Cost, speed, reproducibility
Digital advertising budgets are under pressure, even for large consumer groups. Media prices fluctuate, platforms change limits, and viewers expect continuous updates. AI offers a way to absorb some of that pressure by lowering the marginal cost of producing additional assets.
By reusing footage and applying AI-based enhancements, brands can increase the value of each shoot. This is especially important in areas where campaigns need to change quickly or where local teams need specific assets but lack full-scale production support.
The result is incremental cost reductions across hundreds of small decisions, rather than dramatic cost reductions in one area. Over time, these savings shape how marketing teams plan campaigns and allocate spend.
This Talks About Enterprise AI Maturity
L’Oréal’s use of AI-generated creative work is less about experimentation and more about operational suitability. This tool is used in situations where the output is predictable, quality can be measured, and mistakes can be discovered before release.
This reflects how AI is being implemented into many functions of the enterprise. Rather than broad, unrestricted use, companies are identifying narrow tasks that AI can reliably assist without introducing new risks. In marketing, these tasks often sit between creative concept and final distribution.
This approach also highlights important constraints. AI works best in environments with existing data, rules, and review processes. Creative freedom still belongs to humans, but AI supports scale.
Impact on marketing teams
The lesson for marketing leaders is not that AI will replace agencies or in-house creative. That means production models built for slower cycles are becoming harder to sustain.
Teams are being asked to deliver more content, more often, with smaller budgets and faster turnaround times. AI tools offer one way to manage that demand, but only if they fit within existing controls and expectations.
This places new demands on governance. Marketing teams need clear rules about where AI can be used, how the output can be reviewed, and who is responsible for the final decisions. Without that structure, efficiency gains are quickly offset by risks.
What L’Oréal’s approach means for enterprise AI adoption
What stands out about L’Oréal’s approach is restraint. AI is applied to reduce friction, not to reshape the role of creative teams. This facilitates integration into larger organizations with established processes and brand protection capabilities.
A similar pattern is emerging as more companies turn to AI to improve productivity. AI will no longer be in the headlines, it will be part of your workflow. Success is measured by time savings and consistency, not novelty.
For now, AI-generated creative works still play an ancillary role in corporate marketing. Its real impact lies in how it quietly changes the economics of content production, one asset at a time.
(Photo courtesy of Helio E. López Vega)
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