Introducing: Product-Level AI Market Share

Measuring how AI recommends individual products - not just brands

AI answers have become a new distribution layer.

When users ask AI systems what to buy, which product to choose, or what works best for a specific problem, AI does not think in terms of brands. It thinks in terms of answers - and those answers increasingly map to specific products.

At Aeoh, we are introducing Product (SKU) AI Market Share: a way to measure how often individual products appear in AI recommendations, how strongly they rank, and what sources support those decisions.

This is not brand visibility. This is product-level AI decision visibility.

Why brand-level measurement is no longer enough

In traditional analytics, brands are the primary unit of measurement. In AI systems, they are not.

When users ask:

• "What is the best anti-hair loss shampoo?"
• "Which TV has the best picture quality?"
• "Which iPhone model should I buy?"

AI systems respond with specific product names, variants, or models - not company slogans.

That means:

• Different products from the same company compete against each other
• One SKU can dominate AI answers while another is effectively invisible
• AI preference can shift without any notice, UI change, or warning

Brand-level metrics simply flatten this reality.

What is Product (SKU) AI Market Share?

Product AI Market Share measures:

• How often a specific product appears in AI recommendations
• How highly it ranks when it appears
• How much of the total AI "answer surface" it captures relative to competitors

Each product is treated as a separate entity, even if it belongs to the same brand.

Different models, variants, or formulations = different market positions.

Example: Nizoral vs the market

Consider the anti-hair loss / anti-dandruff category.

AI does not recommend "Nizoral" in general. It recommends:

• Nizoral A-D Ketoconazole Shampoo
• alongside other specific products with different ingredients and claims

In Aeoh's Product AI Market Share view, Nizoral A-D becomes its own measurable unit:

• It accumulates points based on how often it appears
• Higher recommendation positions earn more weight
• Competing products are scored independently, even within the same brand family

This allows a company to answer questions like:

• Which exact product is AI favoring?
• Which competitor product is gaining momentum?
• Are multiple products cannibalizing each other in AI answers?

Beyond brands: models, variants, and configurations

This applies far beyond consumer goods.

Aeoh uses AI-assisted normalization to distinguish between closely related products that AI treats as distinct answers, for example:

• iPhone 15 vs iPhone 15 Pro
• Different storage configurations or editions
• Product lines with similar naming but different positioning

Each variant becomes its own line in the metrics - because AI already treats them that way.

What this unlocks for enterprise and multi-product brands

For large organizations, this changes how AI visibility can be managed.

1. Product-level accountability

Teams can see which SKUs actually drive AI exposure - and which do not.

2. Better portfolio decisions

If AI consistently favors one product over another, that insight can inform:

• content strategy
• product positioning
• messaging consistency
• even roadmap discussions

3. Early detection of shifts

Product-level trends reveal:

• emerging competitors
• declining visibility of previously strong SKUs
• sudden changes tied to source or citation shifts

4. Clearer internal alignment

Marketing, product, and leadership teams can finally talk about AI visibility using the same unit AI uses: the product itself.

A different mental model

Product AI Market Share is not about optimization tricks. It is about measurement fidelity.

AI answers operate at the product level. Aeoh measures at the product level.

That alignment is what makes the data interpretable, actionable, and credible for enterprise use.

What comes next

As AI answers continue to replace traditional discovery paths, the question is no longer:

“Is our brand visible?”

It is:

“Which of our products does AI actually recommend - and why?”

Product-level AI Market Share is our first step toward answering that question with clarity.