How to Win When People Stop Googling and Start Asking
The brands winning tomorrow aren't optimizing for Google. They're building systematic influence within AI recommendation systems that increasingly control what gets discovered.
The Fundamental Shift: When Ad Reach becomes less efficient
For a decade (or more) , marketing science has leaned on a foundational truth: mental availability drives market share. Byron Sharp’s research, as outlined in How Brands Grow, showed that reaching as many category buyers as possible builds the mental availability that drives brand consideration in buying situations.
Sharp’s model:
Reach (especially light buyers) → Mental Availability (via distinctive assets) → Physical Availability → Market Share Growth
The key insight: frequency matters far less than scale of reach, and the recall in buying situations CEPs, and how easily they can find you on the shelf.
Vogue Business
The Ad Reach based model is starting to break.
Instead of hunting for dermatology appointments or browsing influencer TikToks, consumers are consulting generative AI for personalized skincare advice. According to Vogue Magazine (2024), 68% of Gen Z now prefer AI-generated skincare recommendations over traditional consultations.
So when they ask ChatGPT:
“What’s the best project management tool for a creative team?”
Your reach is irrelevant. They’re not recalling your brand assets. They’re accepting recommendations from systems that have never seen your ads.
The disrupted reality:
Traditional reach → Human memory → AI bypasses memory → AI recommendation → Purchase decision
Mental availability is no longer about building memory structures alone. It’s about becoming the answer in systems that skips memory entirely.
The Architecture of Invisible Influence
Ask an AI:
“What’s the best hyaluronic acid serum for dry skin?”
It doesn’t check a shelf. It checks a network of embedded knowledge sources—Reddit forums, product databases, co-purchase patterns, whitepapers, clinical studies. Not page 1 of Google.
This invisible influence infrastructure operates across four interlocking systems:
1.Data Partnership Networks
Organizations controlling the data pipelines that feed AI training sets essentially control the recommendation layer. Think: research institutions, review ecosystems, open data agreements.
2. API Integration Influence
Preferred plug-ins and integrations appear first when AI suggests tools. This is technical placement as brand strategy.
3. Algorithmic Positioning
Optimizing not for SEO—but for how AI weights source credibility and citation networks. It's a new kind of content architecture.
4. Platform Ecosystem Strategy
Systematic influence through co-development, first-party data relationships, and recommendation engine alignment.
This is the future of reach: Less frequency, more context. Less memory, more machine fluency.
Some forward thinking brands are already demonstrating wins, building AI-Era Mental Availability
The brands winning today aren’t just reaching consumers. They’re training the systems that consumers trust.
Amazon engineered a new form of algorithmic adjacency.
Their analysis showed that co-purchase-only product pairs had 30% stronger complementarity scores than those that also showed up via co-view or browse history. Translation: they trained recommendation systems to see connections others missed—making themselves “mentally available” across unrelated categories.
CeraVe built algorithmic authority
By owning the data trails that influence skincare queries. CeraVe strategically leaned into Reddit communities like r/SkincareAddiction—where disproportionate weight is given by AI systems sourcing product recommendations. Campaign helped generate 32 billion impressions and 25% sales growth, not through advertising, but through systemic presence.
The Wall Street Journal created contextual AI advantage
they positioned their proprietary data inside the AI flows that inform B2B decisions.they positioned their. As reported in their client case studies, “our clients are more likely to spend with us because of it… trust is higher, return is stronger.” They didn’t just market they leveraged trust
The SmoothJazz AI-Era Availability Architecture
We adapted Sharp’s principles for the AI-mediated economy—where mental availability isn’t remembered. It’s inferred.
Phase 1: AI-Era Availability Assessment
"Measuring Your Algorithmic Availability"
Phase 2: Systematic AI Availability Architecture
"Building Algorithmic Mental and Physical Availability"
Phase 3: Systematic Influence Execution
"Operationalizing Invisible Advantage"
Phase 4: Influence ROI Optimization
"Measuring and Scaling Invisible Competitive Advantage"
Inspiration credit for this section
detailed info on GEO, GPT, Chatbot,
How can brands optimize product information for AI search algorithms?
Let’s Build the Benchmarks That Shape the Next Playbook
We're currently exploring early-stage collaborations for brands and teams ready to pioneer the Invisible Influence Infrastructure approach.
If you're:
Already seeing unexpected traction via AI recommendations
Testing ways to boost presence in LLMs, chatbots, or algorithmic suggestion layers
Or simply curious how to position your brand for AI-mediated discovery...
We’d love to connect.
Whether you're working on skincare, SaaS, or supply chains, the questions people ask AI are becoming the real distribution channel—and few brands are measuring, let alone mastering it.
We’re looking to co-develop case studies, run structured experiments, and benchmark early signals as we build out the SmoothJazz.ai whitepaper on Algorithmic Availability and Invisible Influence Architecture.
We’ve already written about how Gen Z is bypassing legacy brands and experts for AI advice, and how the next wave of marketing won’t be about attention—it’ll be about access. This is your chance to be at the table where those insights become systems.
Drop us a line. Bring a problem. Let’s make the future recommend you.
Because in the era of AI, being remembered is optional. Being recommended is everything.
