Preparing Your Brand for AI-Driven Discovery
Quick Summary Learn how AI decides which brands to recommend, how this differs from SEO, and what enterprise DTC brands must do to be discoverable in an AI-driven commerce landscape.
We are seeing a growing number of brands ask the same question: How do we get AI to recommend our products?
Beneath that question is a deeper issue. There is a widening gap between how brands perceive themselves and how AI systems actually understand them.
Customers are increasingly delegating early-stage decision-making to AI. They are not asking for recommendations because they want more options. They are asking because they want fewer. They want complexity reduced and risk removed before they commit their time or money.
AI systems respond by assembling shortlists based on credibility, fit, and explainability.
In these moments, AI is not impressed by scale, awareness, or visual sophistication. It evaluates whether a brand can be clearly categorized, consistently described, and confidently defended.
This blog breaks down how AI-driven discovery works and what it takes to show up in AI recommendations.
How AI Actually Forms a Recommendation
AI does not recommend brands the way search engines rank pages. Before making any recommendation, AI systems first eliminate options that introduce uncertainty. This filtering happens upstream of search, traffic, and attribution.
When a customer asks AI for recommendations, the system does not begin by comparing every possible brand. It begins by removing brands that are difficult to explain. Unclear positioning, inconsistent descriptions across sources, or weak external validation all increase perceived risk. Risk leads to exclusion, regardless of brand size or awareness.
So what determines whether a brand makes the shortlist?
Consistency
AI builds understanding by comparing how a brand is described across its website, reviews, editorial coverage, and community discussion. The more aligned those descriptions are, the easier it is for the system to form a stable, repeatable understanding of the brand.
Comparability
AI cannot recommend what it cannot contextualize.
To explain why a brand fits a specific need, AI must be able to place it relative to known alternatives. Brands that avoid comparison or rely on abstract differentiation force the system to infer meaning. Inference introduces risk.
Clear comparison is not commoditization. It is what makes recommendations possible.
Defensibility
AI systems are optimized to reduce risk on behalf of the user. Brands that clearly state who they are for, how their products work, and where they may not be the right fit are easier to justify and more likely to be included in recommendations.
Why Most Brands Are Optimizing for the Wrong Thing?
Most fast-growing DTC brands continue to optimize for visibility because that is what traditional commerce metrics reward. Rankings, impressions, traffic, and awareness all signal reach. SEO plays a critical role in capturing demand once it exists and ensuring brands are discoverable when customers are actively searching.
AI-driven discovery, however, operates at a different layer.
AI systems increasingly influence consideration before traditional search begins. They are used when customers want help narrowing options, validating credibility, and reducing decision-fatigue. In these moments, visibility metrics do not reflect whether a brand is legible enough to be recommended.
Strong awareness and polished creative can build brand equity. High-ranking pages can drive traffic. Neither guarantees inclusion when an AI system is quietly assembling a shortlist based on clarity, consistency, and confidence.
Understanding this shift requires separating two systems that are often conflated.
AI Versus SEO: Parallel Systems, Different Jobs
AI-driven discovery and SEO are often framed as competing forces. In reality, they solve different problems at different moments in the customer journey.
SEO captures demand once intent is explicit. It performs best when customers already know what they are looking for and need help finding the most relevant page. It is built to surface answers efficiently and direct users to destinations.
AI operates earlier, before intent is fully formed. Customers turn to AI when they want help thinking through options before committing attention. In these moments, AI is not trying to surface pages. It is deciding which brands and products are even worth considering.
This is the fundamental difference.
SEO asks: Can this page satisfy the query?
AI asks: Which brands are credible, appropriate, and safe to recommend?
Because of this, the evaluation model changes.
SEO evaluates pages and websites in isolation.
AI evaluates brands in aggregate, across sources and signals.
SEO rewards relevance and visibility.
AI rewards clarity, consistency, and explainability.
Both systems matter. But they do not do the same job, and optimizing for one does not guarantee performance in the other.
How Brands Should Optimize for AI-Driven Discovery?
Optimizing for AI is not about chasing new algorithms or publishing more content. It is about making your brand easier for AI systems to understand, evaluate, and defend.
AI-driven discovery rewards brands that reduce uncertainty. The work is structural, not cosmetic:
Design for Interpretability, Not Just Conversion
Most high-growth commerce experiences are designed to persuade humans. AI systems need clarity before persuasion.
Brands that perform well in AI recommendations are explicit about:
Who the product is for
What problem does it solve best
How it works
What tradeoffs exist
Example:
Before: “Mouth rinse designed for everyday confidence and an on-the-go lifestyle.”
This reads well to humans, but AI cannot explain what this product actually does or who it is for.
After: “A foam-based mouth rinse designed for people with sensitive gums who want a gentler alternative to traditional mouthwash.”
Now the product is clearly categorized, comparable, and defensible. AI can place it in context and recommend it confidently.
This information should be clear on PDPs, About pages, and core content, not buried in marketing language or visual storytelling.
Normalize Your Brand Language Across Channels
AI builds brand understanding by comparing descriptions across sources.
When your website, PR, reviews, creator content, and product listings all describe the brand differently, confidence drops. This is one of the most common failure points for enterprise brands, especially those with multiple teams owning different narratives.
Brands should:
Use consistent terminology for products and benefits
Reinforce the same primary use cases everywhere
Avoid frequent repositioning or messaging pivots
Consistency creates confidence. Confidence enables recommendation.
Make Comparison Explicit
AI systems rely on context to reason safely.
Brands that avoid comparison in an effort to protect positioning often make themselves harder to recommend. AI needs to understand how your brand and products relate to known alternatives, categories, and expectations.
This does not require commoditization. It requires clarity.
Brands that clearly explain how they differ, where they excel, and where they may not be the right fit give AI the structure it needs to justify inclusion.
Example:
Before: “We are redefining premium skincare.”
This avoids comparison and forces AI to guess.
After: “Our formulas are designed for customers who want dermatologist-tested skincare without prescription strength actives, making them suitable for daily use on sensitive skin.”
This gives AI a reference point, a tradeoff, and a clear audience.
Strengthen Third-Party Validation
AI does not trust first-party claims alone.
AI-driven discovery pulls heavily from external signals, including editorial coverage, creator explanations, reviews, and community discussion. What matters is not volume, but alignment.
Brands should focus on:
Credible third party content that explains the product clearly
Long form reviews and demonstrations
Consistent narratives across independent sources
Example:
Before:
Website: “A performance-driven wellness brand.”
PR: “A luxury lifestyle company.”
Reviews: “Great for beginners, not too intense.”
AI sees inconsistency and cannot form a stable brand model.
After:
Website, PR, and creator briefs all describe the brand along the lines of: “A premium wellness brand designed for first-time users who want effective results without aggressive formulations.”
Repeated, aligned descriptions across trusted sources dramatically increase recommendation likelihood.
Treat Your Commerce Platform as a Knowledge System
For enterprise DTC brands, Shopify is no longer just a transactional layer. It is a source of truth that AI systems retrieve from and summarize.
This means:
PDPs should balance storytelling with explicit detail
FAQs should answer real questions, not avoid them
About pages should explain what you do, how you do it, and who you are for
When AI summarizes your brand, it often paraphrases your own content. Make sure it has something accurate and defensible to work with.
Designing for Agentic Commerce
AI-driven discovery is only the beginning. The more consequential shift is toward agentic commerce, where AI systems do not simply influence consideration but actively participate in decision-making and conversions.
In agentic models, AI systems evaluate structured commerce data to determine what actions are possible and safe. On platforms like Shopify, this means AI agents increasingly rely on product catalogs, PDP content, variant logic, pricing rules, availability, policies, and customer context to guide decisions. The brands that succeed in these flows are not the ones with the loudest messaging, but the ones whose commerce experiences are easiest to interpret and act on.
This is not something that can be solved through hacks or one-off optimizations. It is earned through disciplined platform design, content architecture, and experience strategy over time.
Where Avex Comes In
At Avex, we partner with industry-leading Shopify brands to design commerce experiences that perform for customers and are legible to the systems increasingly shaping discovery and decision-making.
From platform architecture and UX to PDP structure, content systems, and experience strategy, we help brands move beyond visibility and build the clarity required to compete in an AI-mediated and agent-driven commerce landscape.
As commerce shifts toward agentic models, brands will not win by being louder. They will win by being easier to understand, compare, and trust.
