L’Oréal integrates AI into everyday digital advertising production

Digital advertising at a global scale is no longer about delivering a single standout campaign. For consumer brands operating across dozens of markets, the real challenge lies in maintaining volume, speed, and consistency—without repeatedly incurring high production costs.

That pressure is pushing major companies to rethink how artificial intelligence fits into routine marketing operations. At L’Oréal, AI-powered creative tools are increasingly being used to support everyday digital advertising production, particularly for video and visual content. The objective is not to replace creative teams, but to streamline processes in an environment that demands constant content refresh.

L’Oréal’s approach offers a clear snapshot of how enterprise AI adoption is evolving within creative functions, where efficiency and control are as critical as originality.

Scaling content without expanding production cycles

For a global beauty group, digital advertising has become a continuous exercise rather than a seasonal one. Content is required across social platforms, ecommerce channels, and regional campaigns—often with only slight variations in language, format, or visual emphasis.

Traditional production models struggle to keep pace. Each new asset typically involves planning, filming, editing, and approvals. AI-generated visuals and video elements help extend the lifespan of existing content, allowing teams to adapt and repurpose assets without starting from scratch each time.

At L’Oréal, AI tools are used to enhance footage, adjust formats, and create platform-specific variations. While human teams retain responsibility for creative direction and final approval, AI significantly shortens the gap between concept and delivery.

The value lies less in producing entirely new creative and more in generating sufficient, high-quality content to meet the relentless pace of digital advertising.

Maintaining strict creative control

One of the main concerns for large brands adopting AI in creative workflows is brand risk. Visual identity, tone, and messaging are tightly governed, and even small inconsistencies can be magnified when content is distributed at scale.

Rather than delegating creative decisions to algorithms, L’Oréal uses AI as a support layer within existing workflows. AI-generated outputs are reviewed, refined, and approved through established internal and agency-led processes, ensuring accountability remains firmly with human teams.

This reflects a broader enterprise trend: AI is being embedded into current workflows instead of redefining how decisions are made. In marketing, that typically means AI supports production efficiency rather than shaping brand voice or strategy.

Cost efficiency, speed, and repeatability

Even for large consumer groups, digital advertising budgets face increasing pressure. Media costs fluctuate, platform rules evolve, and audiences expect frequent updates. AI offers a way to ease this strain by lowering the marginal cost of producing additional assets.

By reusing footage and applying AI-driven enhancements, brands can extract greater value from each production shoot. This is particularly useful when campaigns need rapid adjustments or when local teams require tailored assets without access to full-scale production resources.

Rather than delivering dramatic savings in a single area, AI contributes incremental efficiencies across hundreds of decisions. Over time, these gains influence how marketing teams plan campaigns and allocate budgets.

A marker of enterprise AI maturity

L’Oréal’s use of AI-generated creative content signals a move beyond experimentation toward practical deployment. The tools are applied where outputs are predictable, quality can be assessed, and errors can be caught before publication.

This mirrors broader enterprise AI adoption patterns. Companies are focusing on narrow, well-defined tasks where AI can add value without introducing significant risk. In marketing, those tasks often sit between creative ideation and final distribution.

The model also highlights a key limitation: AI performs best in structured environments with clear rules, data, and review mechanisms. Creative judgment remains human-led, while AI supports scale and efficiency.

What it means for marketing teams

For marketing leaders, the takeaway is not that AI will replace agencies or in-house creatives. Rather, production models designed for slower cycles are becoming increasingly unsustainable.

Teams are expected to deliver more content, more frequently, with tighter budgets and faster turnaround times. AI tools can help manage this demand—but only when integrated with clear governance and oversight.

That makes internal controls essential. Marketing teams must define where AI can be used, how outputs are reviewed, and who holds final accountability. Without such guardrails, efficiency gains may be outweighed by brand and reputational risk.

A measured path forward

What stands out in L’Oréal’s approach is restraint. AI is deployed where it reduces friction, not where it disrupts creative ownership. This makes adoption easier within large organisations that rely on established processes and brand safeguards.

As more enterprises pursue AI-driven productivity gains, similar patterns are emerging. AI becomes part of the workflow rather than the headline. Success is measured in time saved and consistency preserved, not novelty.

For now, AI-generated creative remains a supporting tool in enterprise marketing. Its real impact lies in how quietly it reshapes the economics of content production—one digital asset at a time.