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- Why Google-style SEO no longer suffices for beauty brands
- RoC’s three-point strategy to win in generative search
- How Borghese rebuilt product data to be AI-friendly
- Where generative engines pull influence — and what that means
- Governance, skepticism and human oversight for AI content
- Unexpected wins: product insights and smarter roadmaps
- Operational changes brands are making today
As artificial intelligence reshapes how shoppers find products, two long-standing skincare names are rewriting their digital playbooks. Executives at a recent industry forum described clear shifts in priorities. They are rebuilding product information, testing new content approaches and setting rules for AI use to keep brand stories accurate and discoverable.
Why Google-style SEO no longer suffices for beauty brands
AI-driven search changes the rules for product discovery. Traditional SEO tactics focused on rankings and backlinks. Now, generative engines synthesize answers from many sources.
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That means brands must supply verifiable, structured data where AI can find it. If product facts are missing or inconsistent, generative tools may surface inaccurate or competing information.
Brands that ignore this shift risk losing visibility in moments when consumers ask AI for recommendations or comparisons.
RoC’s three-point strategy to win in generative search
RoC Skincare is treating AI as a channel problem and an operations problem at once. Their approach centers on three practical areas.
Core pillars
- Generative search visibility — making sure product facts and narratives appear in AI answers.
- Content optimization — refining product pages and creative assets to match how people ask questions.
- Community and earned media — leaning on trusted third-party sources instead of only buying placements.
To execute, RoC is using simulation tools that mimic AI search behavior. Those tools reveal which publisher pages and retailer product detail pages most influence generative answers.
One surprising insight: legacy fashion and beauty magazines that moved online still carry strong weight in AI source rankings. That pushed RoC to invest more in earned coverage with the same vigor it applies to commerce partners.
RoC also built hundreds of Q&A pairs into product detail pages. Those snippets align with conversational queries consumers ask in AI chat or voice search.
The focus is efficiency. Each AI initiative must simplify workflows or improve conversion, not add layers of complexity.
How Borghese rebuilt product data to be AI-friendly
Borghese, an Italian brand known for its Fango mud mask, discovered a hard truth: its flagship product was virtually invisible to generative search.
After repeated tests, the brand could not reliably surface the Fango treatment through AI queries. That triggered a company-wide effort to reorganize product content.
Project PDP: what it entails
- Audit and cleanse ingredient and formula data.
- Extract and surface scientific benefits in plain language.
- Rewrite product copy and expand long-form content where useful.
- Standardize metadata and distribution across retail and editorial partners.
Part of the strategy is seeding verified information across platforms that generative models use, such as a dedicated, properly sourced Wikipedia entry for the Fango product.
AI is only as reliable as the sources it reads, the team noted. Controlling authoritative signals helps ensure AI systems answer with the brand’s true claims and context.
Where generative engines pull influence — and what that means
Both companies mapped the sources that most affect AI answers. The list includes:
- Retailer product detail pages (PDPs)
- Brand-owned long-form content and FAQs
- Digital archives of legacy magazines
- Public knowledge bases and encyclopedias
That mapping led to tactical changes. Retailer PDPs became priority content to optimize. Earned editorial mentions were treated like high-value SEO assets.
Brands now think beyond links. They aim to plant clear, factual signals where AI crawlers can index them.
Governance, skepticism and human oversight for AI content
Executives warned that fast adoption carries risks. They advocated for internal guardrails to protect accuracy and brand integrity.
- Train teams to question AI outputs and check sources.
- Establish regulatory review paths for any claim derived from AI tools.
- Define KPIs to measure whether AI saves time or creates extra work.
- Require human sign-off for customer-facing AI-generated content.
“Be AI skeptics,” one leader said, encouraging staff to ask where models pull their data and whether that data is reliable.
Unexpected wins: product insights and smarter roadmaps
Using AI to analyze formulations yielded more than copy changes. Borghese discovered that some performance claims were already supported by their ingredients.
Rather than develop new SKUs, the brand chose to tell a clearer story about existing formulas. That saved development time and highlighted hidden product benefits.
For RoC, tying AI work to team KPIs kept the focus practical. Projects had to move the needle on conversion, reduce manual effort, or improve discovery metrics.
Operational changes brands are making today
Across teams, the operational checklist includes:
- Comprehensive product data cleanups and metadata standards.
- Testing creative and PDP variants with AI-aware A/B frameworks.
- Seeding accurate brand facts on trusted public platforms.
- Formalizing editorial and regulatory reviews for AI outputs.
These steps reflect a broader shift: search optimization is evolving into a data and verification discipline. Brands that move quickly can shape the narratives AI serves to consumers.
