I’ve been tracking our brand awareness metrics for 5 years. Something fundamental shifted in 2025.
What our data shows:
Traditional discovery channels (social, search, referral) are flat or declining. But something new is growing.
When we survey new customers: “How did you first hear about us?”
| Channel | 2024 | 2025 | Change |
|---|---|---|---|
| Google Search | 38% | 32% | -6% |
| Social Media | 22% | 21% | -1% |
| AI Recommendation | 4% | 15% | +11% |
| Word of Mouth | 18% | 16% | -2% |
| Direct | 12% | 11% | -1% |
| Other | 6% | 5% | -1% |
15% of new customers now first heard about us from AI.
That’s from nearly zero 18 months ago.
Questions:
Sarah, your data matches industry trends we’re seeing.
The fundamental shift:
Search discovery: “Here are 10 options, you decide” AI discovery: “Based on your needs, I recommend these 2-3 brands”
Why this matters:
In search, being on page 1 gave you a chance to be evaluated. In AI, not being recommended means not being considered at all.
The AI discovery dynamics:
| Aspect | Search Discovery | AI Discovery |
|---|---|---|
| Options shown | 10+ | 1-3 |
| User effort | High (must evaluate) | Low (pre-filtered) |
| Trust in source | Variable | High (AI as advisor) |
| Winner-take-most | Moderate | Strong |
| Unknown brand chance | Possible | Rare |
The implication:
Brand discovery becomes more binary. You’re either in the AI recommendation set or you’re invisible.
The “winner-take-most” dynamic is concerning. We’re a mid-size brand competing against giants.
Is there hope for challenger brands in AI discovery, or do market leaders dominate?
There’s actually an opportunity for challenger brands:
AI recommendations are context-dependent. Leaders dominate broad queries: “Best CRM” → Salesforce
But specific queries open doors: “Best CRM for creative agencies” → Could be anyone with the right positioning
The niche discovery advantage:
AI is good at matching specific needs to specific solutions. If you own a niche, you can win discovery for that niche.
What we’ve seen:
Challenger brands that clearly position for specific use cases get recommended for those use cases, even over leaders.
The strategy: Don’t try to win “best [category].” Win “best [category] for [your specific audience/use case].”
Consumer research perspective on AI brand discovery:
How consumers use AI for brand research:
We surveyed 1,000 consumers on their AI usage:
| Behavior | % of Consumers |
|---|---|
| Have asked AI for brand recommendations | 47% |
| Trust AI recommendations | 68% |
| Would try a brand AI recommends | 72% |
| Have discovered a new brand via AI | 34% |
The trust factor:
Consumers trust AI recommendations more than ads, similar to word of mouth. They perceive AI as objective (whether it is or not).
What drives AI-discovered purchases:
The consideration phase is compressed. AI does the comparison work users previously did themselves.
Startup perspective on AI brand discovery:
AI discovery is our biggest new-customer source.
We’re a 2-year-old company. No brand awareness in the traditional sense. But we’re getting discovered through AI.
Why startups can win AI discovery:
Our discovery story:
New customer: “ChatGPT told me you’re the best for [specific use case]”
We never ran an ad. We just optimized for AI discovery for our specific niche.
For challenger brands:
AI discovery can level the playing field IF you have clear, specific positioning that AI can understand and recommend.
Brand strategy implications of AI discovery:
The new brand positioning question:
Old: “What do we want to be known for?” New: “What do we want AI to know us for?”
Brand attributes AI can recommend on:
The positioning clarity imperative:
AI needs clear signals to recommend you. Muddled positioning = no recommendations.
Brands with crystal-clear “we are the best X for Y” positioning get recommended. Brands with broad “we’re great for everyone” positioning get overlooked.
CMO perspective on adapting to AI discovery:
How we’re changing brand marketing:
Less:
More:
Budget shifts:
We moved 20% of awareness budget to AI visibility efforts:
The measurement challenge:
Traditional brand metrics (aided awareness, recall) don’t capture AI discovery. We added new metrics:
Media planning perspective:
The brand discovery funnel is changing:
Old: Awareness → Consideration → Intent → Purchase New: Need → AI Recommendation → Validation → Purchase
What this means for media:
Awareness advertising loses value if AI makes the recommendation. Performance advertising at the validation stage gains value.
How we’re adapting:
The attribution challenge:
If AI recommends you, who gets credit? The AI? The content it cited? The reviews it read?
We’re treating AI discovery as organic channel and measuring by presence.
Technical perspective on AI brand discovery:
What makes AI recommend a brand:
The brand discovery signals:
| Signal | Impact on AI Discovery |
|---|---|
| Wikipedia presence | Very High |
| Major publication mentions | High |
| Review platform presence | High |
| Comparison content | Medium |
| Own website content | Medium |
| Social media | Low |
For brand discovery:
Third-party validation matters more than self-promotion. Build brand presence through earned media, reviews, and authoritative mentions.
Competitive intelligence on AI brand discovery:
What we track:
For each competitor and our brand:
Insights from tracking:
Our monitoring approach:
Weekly tracking with Am I Cited across 100+ queries. Dashboard shows:
This thread transformed how I think about brand building. Key insights:
The shift:
New brand strategy elements:
Budget reallocation: Moving 15-20% from traditional awareness to:
New metrics to track:
The 15% AI discovery share will likely be 25-30% by end of 2026. We need to be ready.
Thanks everyone for the strategic perspectives.
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