Beyond share of AI: Why brands need to understand what drives AI recommendations

AI search visibility is changing how consumers discover, compare, and evaluate products. Instead of moving through a predictable sequence of search results, retailer pages, and product listings, shoppers can increasingly ask generative AI search platforms such as ChatGPT, Gemini, and AI-enabled retail experiences for direct recommendations based on their individual needs.

This creates a new visibility challenge, as it is no longer enough to understand where a product ranks in traditional search or how often it appears across retailer websites. Brands also need to know the following:  

• Are their products being included in AI-generated responses?
• How are their products being represented?
• Which competitors are being recommended instead?
• What is influencing those outcomes?

Measuring Share of AI is an important starting point. It can show how frequently a brand appears relative to competitors across relevant AI-generated responses. The harder challenge is understanding why that visibility exists and what should happen next.

This was one of the key themes of our recent eClerx Talks discussion on AI search optimization and the changing path to product discovery. As AI becomes more influential in how consumers research products, brands need to move beyond simply monitoring their products’ visibility in AI search and start understanding the context behind each recommendation.

Key takeaways

• Share of AI shows how often a brand appears, but not why it is recommended.
• Prompts reveal specific customer needs, preferences, and purchase intent.
• Connecting prompts to products, SKUs, competitors, and sources provides deeper AI-search insights.
• Third-party content, reviews, retailer pages, and editorial coverage can influence how products are represented.
• AI visibility data becomes more valuable when combined with commercial and category expertise.
• Brands should focus on recurring, commercially significant patterns rather than individual AI responses.

AI search visibility is only the starting point

A Share of AI metric can provide a useful benchmark for understanding whether a brand is gaining or losing visibility within AI-powered discovery experiences. It can also reveal differences between competitors and help teams track how their presence changes over time.

The limitation is that a high-level score cannot explain the full story. For example, a coffee machine brand may perform strongly when consumers ask about the best premium options in the category. This is because the question or search is broad. However, the coffee machine brand may disappear when the search is focused on a specific need, such as finding a machine suitable for a small apartment.

Both scenarios relate to the same product category, but they reflect different customer priorities. The difference in recommendations may be influenced by any of the following factors:

• Product positioning
• Available content
• Third-party information
• Customer feedback
• The way competing products are represented across the wider digital environment

An overall visibility score can highlight that something is happening, but it cannot always explain why.

Brands therefore need to understand AI search optimization at a more detailed level, including which platforms, prompts, products, markets, and competitors are influencing the result.

Prompts reveal a new layer of consumer intent

Traditional search has taught brands to understand consumer behavior through keywords. AI product discovery introduces a much richer signal – the prompt.

Prompts often combine category interest with specific needs, preferences, product attributes, and purchase considerations. A shopper searching for “noise-cancelling headphones” is expressing general interest in a category. Someone asking an AI platform for “the best noise-cancelling headphones for long-haul flights that are comfortable with glasses” is providing significantly more information about what will influence the purchase decision.

That makes prompt intelligence valuable beyond AI search visibility alone. By analyzing the types of questions being asked and the products appearing in response, brands can begin to understand which customer needs AI platforms associate with their products and where competitors may have an advantage.

A brand may discover that its products perform strongly around durability but rarely appear when consumers prioritize convenience, sustainability, value, or a specific use case. These patterns can reveal gaps in AI product discovery, and allow the brand to fix product positioning, messaging, content, or the wider information available about the product.

Prompt intelligence can also help brands identify how customer language is evolving. Consumers may describe their requirements differently when interacting with conversational AI than when entering a traditional keyword search. Understanding those differences can give marketing, eCommerce, and AI search optimization teams a more current view of the questions shaping product consideration.

The goal is not to optimize every product for every possible prompt. Brands need to identify recurring patterns and determine which ones are commercially significant.

The shift from traditional search to AI-powered product discovery changes not only how consumers search, but also what brands need to understand about their visibility.

Traditional search	AI-powered product discovery
Keyword-driven	Prompt-driven
Consumers search for products using keywords	Consumers describe needs, preferences, and use cases
Brands focus on rankings and listings	Brands focus on recommendations and representation
Results show up at category level	Results show up at product and SKU level
What matters: Performance across categories and search terms	What matters: Performance across prompts, products, and SKUs
Metric: Share of search	Metric: Share of AI

Connecting prompts, products, and sources

Understanding the prompt is only one part of the picture as brands also need to know which products are appearing in response and what information may be influencing the recommendation.

Two products from the same company may perform very differently depending on the question being asked. One SKU may consistently appear when consumers prioritize a particular feature, while another strategically important product rarely gets recommended. At a brand level, that difference can easily be hidden.

Connecting AI search visibility to individual products and SKUs makes it possible to investigate where meaningful gaps exist. If a product repeatedly fails to appear for an important set of prompts, teams can look more closely at AI search analytics and edit how that product is positioned and represented across digital channels. The sources associated with AI-generated responses add another layer of context.

AI platforms draw on information available across a broad digital ecosystem. Brand websites, retailer pages, product information, reviews, editorial content, and other sources can all contribute to the way products are understood and represented.

A brand may have accurate and detailed product information on its own website while third-party sources contain incomplete or outdated information. A competitor may be more visible because its products are more consistently represented across the sources influencing AI responses.

AI search intelligence becomes more actionable when brands can connect the platform where a response appears with the prompt being asked, the product or SKU being recommended, and the sources influencing that recommendation.

Making these connections helps brands move beyond simply knowing that a recommendation occurred to understanding the context behind it. This matters because when a strategically important product consistently fails to appear for a particular type of prompt, teams can investigate whether gaps in content, positioning, or external sources may be contributing to the result.

Turning AI search analytics into action

This need for deeper context is behind the latest AI in Search capabilities in eClerx Market360™.

The capability enables brands to analyze AI visibility across dimensions including AI platform, prompt, country, category, brand, product, and SKU. Teams can move from a high-level Share of AI view into the individual products appearing within responses and gain greater visibility into the sources associated with those recommendations.

This is more than just another AI dashboard; the value comes from connecting the different signals so brands can understand why performance is changing, where competitors may be gaining an advantage, and which opportunities deserve further investigation.

For example:

• A decline in Share of AI becomes more useful when teams can determine whether it is concentrated on a particular platform or market.
• A competitor gaining visibility becomes more meaningful when brands can identify the prompts and use cases where that competitor consistently appears.
• A missing product recommendation becomes more actionable when teams can investigate the sources contributing to how that product is represented.

Technology can surface these patterns, but commercial context remains essential.

Not every missing recommendation represents a problem, and not every competitor appearance requires a response. Brands still need to understand the importance of the product, the customer need behind the prompt, the competitive landscape, and whether the pattern is significant enough to warrant action.

This was another important theme from our eClerx Talks conversation. AI can help organizations capture and analyze new forms of intelligence, but people are still needed to interpret what the data means and decide how the business should respond.

The most effective approach combines technology with category and market expertise. Instead of reacting to individual AI responses, brands can identify recurring patterns, understand where those patterns have commercial significance, and focus on the opportunities most likely to influence visibility and growth.

Preparing for AI-driven product discovery

AI-powered search is still evolving but its impact on product discovery is already changing what brands need to measure.

Although traditional search visibility remains important, consumers increasingly have another way to explore categories, compare products, and receive recommendations. Understanding performance within those environments requires more than a single visibility score.

Share of AI can show whether a brand is present, and connecting that visibility to prompts, products, competitors, and sources starts to explain why.

That is the more important opportunity for brands; the organizations that understand these relationships will be better positioned to identify where they are gaining or losing influence as AI becomes a more established part of the customer journey.

To explore these changes in more detail, read our white paper, From search rank to AI recommendation, which examines how AI is reshaping product discovery and the new signals brands should consider as part of their digital commerce strategy.

You can also watch our latest eClerx Talks episode for the full discussion on AI-driven discovery, prompt intelligence, and what this shift means for brands.

Frequently Asked Questions:

What is AI search visibility?

AI search visibility refers to how often and in what context a brand or its products appear in AI-generated responses. Share of AI is one way to measure this, showing how frequently a brand appears relative to competitors across relevant responses. However, visibility is more useful when brands can also understand which prompts, products, and sources are influencing those recommendations.

How do I optimize for AI search?

AI search optimization starts by understanding how your products appear in AI-generated responses across relevant prompts, platforms, and markets. Analyze the customer needs and use cases associated with those recommendations, then investigate how product positioning, content, third-party information, and customer feedback may be influencing the results. Focus on recurring, commercially significant patterns rather than trying to optimize for every possible prompt.

What should brands focus on first?

Start with a clear view of your AI product discovery. Share of AI can provide a useful benchmark, but the next step is to understand which prompts, products, competitors, and sources are influencing that visibility. AI search analytics helps brands identify meaningful gaps and prioritize opportunities that may have a commercial impact.

How can brands improve their AI product discovery?

Brands can improve AI search visibility by identifying where important products are missing from relevant recommendations and investigating the factors that may be contributing. This includes reviewing product positioning, content, third-party information, and customer feedback, while using AI search analytics to understand which prompts and use cases matter most. The goal is to improve how products are represented across the wider digital environment, not just to increase the number of appearances.