Unilever’s cookie-scented Dove drop didn’t just clean up on TikTok - it scrubbed away any lingering doubt that AI-powered, creator-led marketing is the new playbook for FMCG relevance. This campaign offers a sharp case study on how big brands can marry technology and culture to drive sales and social clout. But it’s not without its trade-offs. Let’s break it down.
🔍 Campaign Recap: The Scent of Success
To launch its limited-edition Crumbl cookie-inspired Dove body care line, Unilever went big on scale, speed, and scent-driven storytelling. A vast influencer network helped the brand pull in over 3.5 billion earned impressions, and 52% of customers who bought the product were new to Dove. Crucially, these results weren’t just down to influencer volume - they were enabled by a smart AI-powered content infrastructure.
Unilever used Nvidia’s Omniverse platform to create digital twins of its products - down to packaging, label and language variants - and fed these into its own Gen AI Content Studios to generate thousands of visual assets a week. These assets were then deployed across its influencer network, and remixed again via AI to fit platform-specific formats and audience segments.
✅ The Pros: What Worked
1. Speed and Scale Without Creative Burnout
Unilever moved from generating “single digit” assets per month to thousands per week. That level of scale is unheard of in traditional CPG creative workflows. It gave influencers fresh, brand-ready content to work with - allowing for faster campaign launches, A/B testing, and trend responsiveness.
2. Influencer ROI That Converts
A powerful stat backs this up: 49% of consumers now make purchases monthly as a result of influencer content. In beauty and personal care, where trial is key, social validation is often more persuasive than legacy brand equity. This campaign showed that tapping into trusted creators still delivers - especially when amplified by AI-enabled formats.
3. New Customer Acquisition at Volume
The standout figure: 52% of sales came from first-time Dove buyers. That’s a strong result for a legacy brand, proving that culturally relevant limited editions can act as a brand gateway. It’s also a rare example of influencer work tied directly to incremental growth.
4. Asset Remixing for Maximum Reach
By using AI to resize, reformat and reposition creator content, Unilever extended campaign life and adapted it for each platform. From TikTok sound-on formats to Instagram carousels and Stories, the brand avoided creative fatigue and ensured higher content fit.
⚠️ The Cons: Risks and Trade-Offs
1. Too Much Volume Can Kill the Vibe
There’s a point where content volume becomes noise. Pushing thousands of branded assets per week may satisfy algorithms, but risks audience fatigue. Without strong creative direction, brands can drift into generic, forgettable content.
2. Authenticity at Risk
While influencer content performs best when it feels spontaneous, AI-optimised assets can tip into over-produced territory. If creators become mere content distributors instead of storytellers, the trust that underpins influence starts to erode. Already, over a third of marketers cite authenticity concerns when using AI-generated content.
3. The AI Influencer Question
Unilever hinted at exploring AI-generated influencers in the future. But 37% of consumers already say they find AI avatars less trustworthy than humans - raising the risk of backlash, especially in categories like skincare where emotional connection and credibility matter.
4. Lack of Guardrails
AI-generated content and virtual influencers remain largely unregulated. Only around 22% of brands currently have AI influencer disclosure guidelines, despite 78% planning to implement them. Without governance, the risk of reputational damage - through undisclosed AI usage or misleading content - is real.
🧭 Key Takeouts for Brand Marketers
1. Start with Humans, Scale with AI
The most effective campaigns still begin with culturally fluent creators. AI is a tool for scale and speed - not a replacement for taste and tone. Use it to adapt, not to dictate.
2. Put Cultural Relevance First
Don’t confuse tech innovation with cultural impact. The Crumbl collab worked because it tapped into a real trend - food-inspired beauty - not just because it used AI. Culture is the soil; AI is the fertiliser.
3. Treat Content Like Inventory
You don’t just need more content - you need the right mix, at the right time, for the right channel. Build modular content ecosystems that allow for remixing, personalisation and localisation. Think of assets as a supply chain, not a finished product.
4. Future-Proof Your AI Ethics
Start building internal playbooks for AI disclosure, content governance and creator transparency. It won’t just help mitigate risk - it will build trust with consumers and regulators alike.
🧠 Final Thought: The New Brand OS
Unilever didn’t just run a campaign - it demonstrated what the future brand operating system could look like. Creators fuel culture, AI enables distribution, and digital twins make agility real. For FMCG brands looking to remain culturally relevant and commercially viable, this is the blueprint.
But let’s not forget: while AI helps you move fast, it’s still the humans who decide where to go.
Check out the WSJ’s take here: https://www.wsj.com/articles/how-unilever-used-ai-to-make-soap-go-viral-8e723717?mod=cio-journal_lead_story