2026 State of AI Recommendations in Europe: Original Research

This report presents findings from our analysis of AI recommendation patterns across European markets. We examined how AI assistants are influencing consumer and business purchase decisions, which categories are most affected, and what separates brands that succeed in AI recommendations from those that remain invisible.

Key Findings

AI Recommendation Adoption

• 47% of European internet users have used AI assistants for product or service research in the past 12 months
• 31% report that AI recommendations directly influenced a purchase decision
• B2B buyers show 23% higher AI recommendation usage than consumer buyers

Brand Visibility in AI

• The top 3 recommended brands in each category capture 78% of AI recommendation mentions
• 64% of mid-sized European companies have zero presence in AI recommendations for their primary category
• Brands with active AI optimization strategies see 2.4x higher recommendation rates

Geographic Variation

• UK and Netherlands show highest AI adoption for purchase research (54% and 51% respectively)
• Germany shows highest B2B AI recommendation usage (38% of business buyers)
• Southern European markets show fastest growth in AI adoption (41% year-over-year increase)

Methodology

This research combines three data sources:

1. AI Recommendation Testing: We systematically tested 2,400 product and service queries across ChatGPT, Claude, Perplexity, and Gemini in 8 European languages over a 6-month period.

2. User Survey: Online survey of 3,200 European internet users across 12 countries regarding their AI assistant usage and trust levels.

3. Brand Analysis: Examination of 450 European companies across 15 categories to identify factors correlating with AI recommendation success.

AI Adoption for Purchase Research

Consumer Usage Patterns

European consumers increasingly use AI assistants during the product research phase of purchase decisions. Rather than replacing search engines entirely, AI assistants are becoming a complementary research tool, often used to synthesize information and narrow consideration sets.

Frequency of AI use for product research (European consumers):

• Daily: 8%
• Weekly: 19%
• Monthly: 20%
• Occasionally: 29%
• Never: 24%

The 24% who have never used AI for product research represents a significant decrease from 41% in early 2025, indicating continued rapid adoption.

Purchase Categories Most Affected

AI recommendations have uneven influence across product categories. Categories requiring research and comparison show highest AI influence, while routine purchases show minimal impact.

Categories with highest AI recommendation influence:

1. Software and SaaS (67% of buyers consult AI)
2. Consumer electronics (58%)
3. Financial products (54%)
4. Travel and hospitality (51%)
5. Professional services (49%)
6. Health and wellness products (44%)
7. Home improvement (41%)
8. Automotive (38%)

Categories with lowest AI recommendation influence:

1. Groceries and everyday items (12%)
2. Fashion and apparel (18%)
3. Entertainment subscriptions (21%)
4. Personal care products (23%)

B2B Buyer Behavior

Business buyers in Europe show notably higher AI recommendation usage than consumer buyers. This likely reflects the complexity of B2B purchase decisions and the research burden that AI assistants can reduce.

B2B AI usage for vendor research:

• Used AI to research potential vendors: 38%
• AI recommendation influenced shortlist: 29%
• AI recommendation directly influenced final selection: 17%

German B2B buyers show the highest AI adoption rates in Europe, with 44% reporting AI use for vendor research. This aligns with German business culture's emphasis on thorough evaluation before purchase decisions.

Brand Performance in AI Recommendations

Concentration of Recommendations

Our testing revealed significant concentration in AI recommendations. Across categories, the top recommended brands capture disproportionate share of AI mentions.

Distribution of AI recommendation mentions:

• #1 recommended brand: 34% of mentions
• #2 recommended brand: 26% of mentions
• #3 recommended brand: 18% of mentions
• #4-5 recommended brands: 14% of mentions
• All other brands: 8% of mentions

This concentration has significant implications. Brands outside the top 3 in their category receive minimal AI visibility, regardless of their actual product quality or market position.

Factors Correlating with AI Recommendation Success

Analysis of the 450 companies in our study identified factors that distinguish brands with strong AI recommendation presence from those without.

Strongly correlated factors:

• Content volume addressing category queries (r=0.71)
• Third-party review quantity and sentiment (r=0.68)
• Backlink authority metrics (r=0.64)
• Technical documentation depth (r=0.59)
• Case study and customer evidence availability (r=0.57)

Moderately correlated factors:

• Company age and market tenure (r=0.43)
• Social media presence and engagement (r=0.39)
• News and press mention frequency (r=0.36)

Weakly or not correlated factors:

• Advertising spend (r=0.12)
• Website design quality (r=0.08)
• Price positioning (r=0.04)

These findings suggest that AI recommendations reward content and authority investments over traditional marketing spend. Companies with strong content foundations outperform those relying primarily on advertising.

European vs American Brand Performance

Our analysis found that American brands outperform European brands in AI recommendations across most categories, even when considering European market queries specifically.

AI recommendation presence by company origin (European queries):

• American brands: Present in 72% of relevant recommendations
• UK brands: Present in 45% of relevant recommendations
• German brands: Present in 38% of relevant recommendations
• French brands: Present in 31% of relevant recommendations
• Other European brands: Present in 24% of relevant recommendations

This disparity largely reflects historical content marketing investment differences. American companies have invested more heavily in digital content and authority building, and these investments now translate into AI recommendation advantages.

However, the gap is closing in language-specific queries. When queries are conducted in German, French, or other European languages, European brands perform significantly better, though still below their American competitors in most categories.

Trust and Influence Dynamics

Consumer Trust in AI Recommendations

European consumers show moderate but increasing trust in AI recommendations. Trust levels vary significantly by country and demographic factors.

Trust in AI product recommendations (% agreeing or strongly agreeing):

• "I trust AI recommendations as much as search results": 41%
• "I trust AI recommendations as much as expert reviews": 34%
• "I trust AI recommendations as much as friend recommendations": 28%
• "I have purchased products based primarily on AI recommendation": 31%

Younger users show substantially higher trust levels. Among 18-24 year olds, 52% have purchased products based primarily on AI recommendations, compared to 19% of users over 55.

Country-Level Trust Variation

Trust in AI recommendations varies across European markets:

Highest trust countries:

• Netherlands: 48% trust AI recommendations
• Sweden: 46%
• UK: 44%

Moderate trust countries:

• Germany: 39%
• France: 37%
• Spain: 36%

Lower trust countries:

• Italy: 31%
• Poland: 28%

These variations appear to correlate with general technology adoption rates and digital literacy indicators in each country.

Implications for Brands

The Closing Window

Our data suggests that AI recommendation patterns are becoming increasingly stable. Brands that establish strong positions now will benefit from persistent recommendation presence, while those that delay optimization will face greater difficulty gaining visibility.

Recommendation stability indicators:

• 73% of brands in top 3 positions were there 6 months ago
• Average position change per quarter is decreasing (0.8 positions in Q1 2025 vs 0.4 positions in Q4 2025)
• New entrants to top 3 require increasingly aggressive optimization efforts

Priority Actions

Based on our findings, European brands should prioritize:

1. Content investment addressing common AI queries in their category
2. Customer evidence development including case studies and reviews
3. Language-specific optimization for target European markets
4. Authority building through third-party validation and expert endorsement
5. Systematic monitoring of AI recommendation presence

Opportunity in Underserved Markets

Smaller European language markets show significant opportunity due to lower competition. Brands optimizing for Dutch, Swedish, Danish, or other smaller language markets can establish dominant positions with less effort than in English or German markets.

Similarly, certain product categories show less AI recommendation competition than others. Categories where AI recommendations are becoming influential but few brands have optimized present outsized opportunity.

Looking Ahead

AI recommendation influence will likely continue growing across European markets. Several trends suggest acceleration:

• AI assistant integration into smartphones and operating systems
• Improved language support for European languages
• Growing comfort with AI tools among older demographics
• Increasing AI capabilities for complex purchase decisions

Brands that establish AI recommendation presence now will be better positioned as these trends continue. The research indicates that early investment in AI optimization generates compounding returns as AI assistants become more central to how European consumers discover and evaluate products.

About This Research

This report was produced by the Noserp research team based on data collected between July 2025 and January 2026. For questions about methodology or findings, contact research@noserp.com.

Updated research will be published quarterly to track evolution of AI recommendation patterns across European markets.