AI as the New Intermediary: Why Brand Loyalty Programs Must Be Rewritten

Event Background

Consulting firm Gale released the "Preference Economy" report in 2026, surveying 3,000 consumers in the US and UK and found: - 56% of consumers are willing to let AI assistants filter brands for them - Nearly 30% have already instructed ChatGPT, Google Gemini to set brand preferences - Gale CEO Andrew Noel predicts that within 2-3 years, 60-70% of consumers will adopt this AI-mediated shopping model

What has changed? The core assumption of traditional brand loyalty strategies (points, discounts, member perks) is that brands can directly reach consumers. But when AI becomes the "gatekeeper" of shopping, brand visibility is no longer determined by ad exposure—it depends on whether a brand can make it onto the AI's recommendation list.

Why This Is a Shift in Intermediaries

Every historical shift in intermediaries has brought a reorganization of power:

1. Newspaper Era (19th century–1980s): Editors decided which news got published and which brands got advertising space. Brands had to please the newspapers.

2. Television Era (1950–2000s): TV anchors decided viewership. Advertisers competed for prime 30-second slots.

3. Search Engine Era (2000–2020s): Google's ranking algorithm determined whether brands could be discovered. Companies invested heavily in SEO and SEM.

4. Social Media Era (2010–2023): Facebook and TikTok algorithms determined reach. Brands had to master content marketing and audience engagement.

5. AI Assistant Era (2025 onwards): Large language models become the first stop in shopping decisions. Whether a brand gets "recommended" depends on entering the AI's training data and prompt logic.

Each shift brings: - Complete rewriting of brand strategy (from "exposure-driven" to "recommendation-driven") - Excessive power for the new intermediary (controlling the gateway = controlling traffic = controlling pricing power) - Rising market concentration (only brands that understand the new rules survive)

First-Party Data Becomes the New Entry Ticket

The report identifies first-party data (customers' preference data) as the key to entering AI recommendation lists.

Why? Because AI assistants are trained on logic that recommends based on consumers' *explicit preferences* and *purchase history*. When Google Gemini can say "I know you hate plastic packaging and prefer local brands," it's because it can access first-party data.

This punishes brands reliant on third-party data (Facebook pixel, Google tracking codes). Third-party cookies were already being phased out (Google disabled them in 2024), and now the AI era makes even broad-based, algorithm-guessing approaches obsolete.

Brands are forced to reorganize their strategy:

1. From "Advertising" to "Data Relationships": Stop paying for ad exposure; instead, invest in accumulating first-party customer data (email, purchase history, explicit preference declarations).

2. From "Mass Tactics" to "AI-Friendly": Brand descriptions, reviews, and recommendation copy must be optimized for LLMs (similar to SEO, but the audience is AI).

3. From "Points Game" to "Preference Alignment": Traditional loyalty programs assume consumers care about accumulating numbers. Now they care about whether "the AI correctly understands my taste."

Why Traditional Loyalty Programs Fail

The traditional model's logic: - Brand pays for advertising → Consumer sees ad → Remembers brand when buying → Accumulates member points → Redeems points to incentivize repeat purchase

This logic's hidden assumption: The first step in consumer decision-making is "recalling a brand," and the brand's job is to fight for top-of-mind awareness.

But AI assistants change the decision flow:

1. Consumer tells ChatGPT: "I want to buy coffee beans—I like single-origin, medium roast, with floral notes" 2. AI directly recommends 3-5 brands that match these criteria (based on web reviews and the consumer's past preferences) 3. Consumer selects from the AI's recommendations, almost never independently searching for other brands

In this flow, traditional advertising has almost zero psychological impact on consumers. Loyalty points become a side effect: consumers don't buy because points are attractive; they buy because AI has already decided for them. Points are just a post-purchase reward and can't change the decision.

Andrew Noel's prediction mentions consumers will "instruct AI to prioritize recommendations from a few preferred brands." This signals the market is contracting from "multi-brand competition" to "oligopoly of a few brands." Once a consumer tells AI "I only choose between Oatly, Nespresso, and Lavazza," every other coffee brand's market opportunity is permanently cut off.

Brand Rewriting Strategy Under New Rules

1. Invest in first-party data infrastructure: Build proprietary platforms (apps, email, loyalty apps) to directly collect customer preference declarations. Every time a consumer says "I like this flavor," that's a signal being fed to AI assistants.

2. AI-friendly content strategy: Don't write for human advertisers anymore; write for LLM retrieval and reasoning. Brand descriptions must be precise, reviews abundant, and stories successfully captured by embedding models.

3. Shift from "Whoever Spends More" to "Whoever Gets Recommended More": Traditional advertising relies on media budgets to win. Competition now centers on "can we enter the AI's recommendation logic." This requires the brand itself to have strong product stories, customer reviews, and community reputation.

4. Preference management becomes the new loyalty tool: Brands don't send points; they invest in making consumers feel "this brand really understands me." This could mean proactive preference feedback ("We noticed you prefer single-origin; here's a special recommendation…").

The Outcome: Winners and Losers

Winners: - Brands with strong product quality and consumer reviews (naturally recommended by AI) - Large brands with rich first-party data (Amazon, Apple, Sephora) - Emerging brands that can quickly adapt to AI-friendly content

Losers: - Brands dependent on ad spending with weak product reputation - Small and medium brands without first-party data systems - Traditional retailers without platforms or direct consumer interaction

This isn't a simple "digital transformation" issue. This is a reorganization of market power structures.