L'Oréal's AI brainstorming experiment reveals how creative AI platforms are changing MarTech priorities — and what Southeast Asian brands should watch.
Most brands that bought a creative AI platform in the last two years are using it the way they used stock photo subscriptions in 2015 — a lot of spend, a fraction of the potential.
L’Oréal is trying something different. According to AdExchanger, the beauty giant adopted Springboards, a creative AI platform, not as a production tool but as a brainstorming layer — specifically to generate unconventional ideas for reaching male audiences, a segment the brand has historically underserved. The result wasn’t a batch of AI-generated ads. It was a collaborative, ongoing ideation process that kept human strategists in the loop while expanding the creative surface area they could explore. That distinction matters more than it sounds.
Creative AI Works Best as a Process, Not a Pipeline
The reflex in most MarTech procurement conversations is to ask: what does this tool produce? L’Oréal’s Springboards implementation reframes that question as: what does this tool enable? There’s a meaningful difference. A production mindset drives teams toward volume — more copy variants, more image options, faster turnaround. A process mindset drives teams toward better briefs, sharper hypotheses, and creative territories they wouldn’t have reached through a standard workshop.
For brands operating across Southeast Asia’s fragmented audience landscape — where a campaign targeting male consumers in Thailand needs to navigate different cultural codes than one running in the Philippines — that expanded ideation surface is genuinely valuable. The AI doesn’t understand cultural nuance by default, but it can stress-test assumptions and surface adjacent ideas that a regional team then filters through local knowledge. That human-AI division of labour is where the ROI actually lives.
The implementation risk is cultural, not technical. If the creative team treats AI output as a first draft to polish rather than a provocation to react to, the work regresses toward the mean. Build the workflow so that AI ideas are inputs to human judgment, not outputs to human approval.
The Digiday Finalists Signal Where Stack Investment Is Heading
Zoom out from any single tool and the industry’s directional bets become clearer. This year’s Digiday Technology Awards finalists — which include ShopMy, Google, and WordPress among others — reveal four converging priorities: AI-ready infrastructure, first-party data architecture, privacy compliance, and operational efficiency. That’s not a surprising list. What’s notable is what’s absent: reach expansion, channel proliferation, and audience scale — the metrics that drove the previous decade of AdTech investment.
The implication for MarTech teams doing stack audits right now is pointed. The vendors winning recognition aren’t the ones promising to get your message in front of more people. They’re the ones helping brands own their data relationships, reduce dependency on third-party signals, and make existing systems interoperable. In practical terms, that means solutions connecting commerce data to creative decisioning (ShopMy’s commerce-creator integration), or making content infrastructure more responsive without custom engineering overhead.
For Southeast Asian brands, this shift has an added layer of urgency. Platform ecosystems here — Shopee, Lazada, LINE, Grab — hold enormous amounts of first-party behavioural data that brands rarely get direct access to. Building owned data infrastructure isn’t just a privacy hedge; it’s competitive leverage against a future where platform data becomes more restricted or more expensive to activate.
The Stack Audit Question Most Teams Avoid
Here’s the uncomfortable reality sitting under both stories: most mid-to-large brands in the region are running MarTech stacks assembled over five or six years of incremental procurement decisions. Each tool made sense at the time. Together, they’ve created an activation gap — the distance between what the stack theoretically enables and what the team actually uses on a given campaign.
The Digiday finalists are winning not because they’re the most feature-rich platforms, but because they’re closing that gap. Operational efficiency as a category priority is the industry admitting that complexity has become a liability. Before adding a creative AI platform, the honest audit question isn’t “what could this do?” It’s “what are we actually not doing with what we already have, and why?”
L’Oréal’s Springboards adoption is instructive partly because it’s scoped. It’s one platform solving one specific problem — ideation for an underserved audience segment — rather than an enterprise AI transformation initiative. That kind of surgical deployment is harder to get budget approval for, because it doesn’t make a good slide deck. It also tends to actually work.
The brands that will come out of the next 18 months with a defensible MarTech position are the ones who resisted the urge to add and instead asked: what does precision look like here?
As AI tools mature from novelty to infrastructure, the real competitive question isn’t which platform you’ve adopted — it’s whether your team has the workflow discipline to extract signal from them rather than just output. In markets as dynamic and diverse as Southeast Asia, that discipline may matter more than any single technology choice.
At grzzly, we spend a lot of time inside stacks that have grown faster than the teams managing them — auditing what’s pulling its weight, identifying where creative AI can genuinely accelerate output, and rebuilding workflows around what brands in Southeast Asia actually need to activate. If your MarTech spend is outpacing your results, that’s usually a solvable problem. Let’s talk
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Crispy GrizzlyAuditing, assembling, and occasionally dismantling marketing technology stacks for brands that have over-bought and under-activated. Precision over proliferation.