Claro's first scalable AI personalization capability

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Context & business goal

As content volume grew at Claro Brazil, existing discovery experiences relied heavily on manual curation and static logic. The company began investing in AI/ML to recommend video and music content more effectively — but the capability needed a user-facing experience that scaled.

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The challenge

• ▸Static, rule-based discovery experiences.
• ▸AI/ML capabilities existed technically but weren't clearly surfaced to users.
• ▸No unified surface to showcase or consume recommendation APIs.
• ▸High risk of 'black-box' AI experiences users wouldn't trust.

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An evidence-based kill decision

We tested an experimental, game-based discovery concept. Usability testing showed users struggled with the mechanics, attention shifted away from content, and trust suffered. We cut the concept and prioritized clarity over experimentation — an explicit, evidence-based product call.

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Experience principles for AI recommendations

• ▸Clarity over novelty.
• ▸Explainability matters — users need context for why content is suggested.
• ▸Scalability by design — reuse across products and content types.
• ▸Human-centered AI — outputs adapt to users, not the other way around.
• ▸Progressive enhancement — AI improves the experience without breaking baseline usability.

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Impact & outcomes

• ▸3+ AI-powered recommendation APIs reused across multiple Claro products.
• ▸Established Claro's first scalable AI-driven personalization capability.
• ▸Created a reusable UX framework for future AI features.
• ▸Improved collaboration between design, product, and data science.