AI
How Does AI Help with Personalization?
February 12, 2026
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12 min read
Summary
AI Personalization
Relevance
Trust
Data Minimization
Inclusion
Recommendation
Real-time
Control
Fairness
Sustainable UX
Expectations, Competition, Information Overload
When Relevance Becomes Distraction
Use AI Responsibly
Purposeful Personalization as a Guideline
Understanding Data, Signals, Feedback Loops
Classifying Recommendations, Ranking, Exploration
Use Cases from Onboarding to Support
Quickly Assess Personalization Potential
Avoid Bias, Filter Bubbles, Dark Patterns
Create Data Protection, Consent, Explainability
Choose Roadmap and KPIs Pragmatically
Clarify Roadmap and Data
Generative AI and Privacy Tech
Costs, Data, Impact, Risks
Open Questions on AI Personalization
How much data do we need to personalize effectively?
Is AI personalization automatically GDPR-compliant?
How do we avoid personalization being "creepy"?
Which KPIs show if personalization truly adds value?
Which risks are most common in practice?
Do we need to build a data science team for this?
How does personalization fit with sustainability and accessibility?
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