Producing digital advertising at global scale has become less about one standout campaign and more about volume, speed, and consistency. For consumer brands operating across dozens of markets, the challenge is not creativity alone, but how to keep content flowing without repeating expensive production cycles.

That pressure is pushing some large companies to test where AI fits inside everyday marketing work. At L’Oréal, AI-generated creative tools are being used to support parts of the digital advertising process, particularly video and visual content. The aim is not to replace human teams, but to reduce friction in a system that demands constant refresh.

The shift offers a useful view into how enterprise AI adoption is unfolding in creative functions, where speed and control matter as much as originality.

Scaling content without scaling production

For a global beauty group, digital advertising is no longer a seasonal exercise. Content is needed continuously across social platforms, ecommerce sites, and regional campaigns, often with small variations in language, format, or visual emphasis.

Traditional production models struggle 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 extend it into new formats without having to start from scratch every time.

At L’Oréal, AI tools are being used to help generate or adapt visual content that fits specific digital channels. This includes polishing footage, modifying formats, and creating versions for different platforms. Human teams continue to monitor creative direction and final output, but AI speeds up the time between idea and delivery.

The practical value is not about producing something altogether new. It is about producing enough usable content to meet the pace of digital advertising.

Why L’Oréal keeps AI under tight creative control

One reason large brands move cautiously with 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.

Rather than handing over creative decisions, companies like L’Oréal are using AI as a support layer. AI-generated output is examined, adjusted, and approved using existing workflows. This keeps accountability with internal teams and external agencies, while still gaining efficiency.

This approach reflects a broader pattern in enterprise AI adoption. Tools are being introduced into workflows that already exist, rather than reshaping how decisions are made. In marketing, that often means AI assists with production, not with defining brand voice.

Cost, speed, and repeatability

Digital advertising budgets are under pressure, even for large consumer groups. Media prices fluctuate, platforms change their restrictions, and audiences expect constant 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 stretch the value of each shoot. This is especially important in areas where campaigns must be quickly changed, or when local teams want specific assets but lack full-scale production support.

The result is not a dramatic cost cut in one area, but incremental savings across hundreds of minor decisions. Over time, those savings shape how marketing teams plan campaigns and allocate expenditures.

What this says about enterprise AI maturity

L’Oréal’s use of AI-generated creative work is less about experimentation and more about operational fit. The tools are used in situations where output is predictable, quality can be measured, and mistakes may be caught before release.

This mirrors how AI is being adopted across many enterprise functions. Instead of broad, open-ended use, companies are identifying narrow tasks where AI can reliably assist without introducing new risk. In marketing, those tasks often sit between creative concept and final distribution.

The approach also emphasises a key constraint. AI works best in environments with existing data, rules, and review processes. Creative freedom still belongs to people, while AI supports scale.

Implications for marketing teams

For marketing leaders, the lesson is not that AI will replace agencies or internal creatives. It is that production models built for slower cycles are becoming harder to sustain.

Teams are being asked to deliver more content, more often, with tighter budgets and faster turnaround. AI tools offer one way to manage that demand, but only if they fit existing controls and expectations.

This places new demands on governance. Marketing teams need clear rules on where AI can be used, how outputs are reviewed, and who remains accountable for final decisions. Without that structure, efficiency gains can quickly be offset by risk.

What L’Oréal’s approach signals for enterprise AI adoption

What stands out in L’Oréal’s approach is restraint. AI is applied where it reduces friction, not where it reshapes the role of creative teams. That makes it easier to integrate into large organisations with established processes and brand safeguards.

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

For now, AI-generated creative work 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. López Vega)

See also: Disney is embedding generative AI into its operating model

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