Adobe LLM Optimizer for Ecommerce: What It Is and Why It Matters

Adobe LLM Optimizer helps ecommerce brands get found by AI assistants like ChatGPT and Perplexity. Here's what it does, how it works, and why B2B commerce teams should care about generative engine optimization now.

February 6, 2026

Your customers are asking ChatGPT for product recommendations. They’re using Perplexity to research suppliers. They’re having conversations with AI assistants before they ever visit your website.

And when those AI assistants answer, your brand might not show up at all.

This is the new discovery problem for ecommerce—and it’s different from SEO.

Adobe launched LLM Optimizer in October 2025 to address exactly this. It’s the first enterprise tool designed specifically to help brands track and improve their visibility in AI-generated responses. If you’re running B2B ecommerce on Adobe Commerce (or considering it), this is worth understanding.

The Problem: AI-Powered Discovery Bypasses Your Website

Traditional SEO optimizes for Google’s search results page. You rank, people click, they land on your site.

AI assistants don’t work that way.

When someone asks ChatGPT “What’s the best hydraulic fitting supplier for industrial applications?” the AI synthesizes an answer from its training data and (increasingly) real-time web access. It might recommend your competitor. It might recommend no one. It might hallucinate a company that doesn’t exist.

The point is: you have no control over this answer. And increasingly, buyers trust these answers enough to skip the Google search entirely.

This creates a new category of optimization: Generative Engine Optimization (GEO), also called Answer Engine Optimization (AEO). The goal isn’t to rank in a list of blue links—it’s to be the brand that AI assistants recommend.

What Adobe LLM Optimizer Actually Does

Adobe LLM Optimizer is part of the Adobe Experience Cloud. It integrates with Adobe Experience Manager (AEM) Sites and connects to the broader Adobe marketing ecosystem.

Here’s what it does:

1. Tracks AI Visibility

LLM Optimizer monitors how your brand appears (or doesn’t) in responses from major AI platforms:

ChatGPT (OpenAI)
Perplexity
Google Gemini
Claude
Other emerging AI browsers and assistants

It shows you what queries mention your brand, what queries mention your competitors, and where you’re absent from conversations you should be part of.

This is visibility you don’t get from Google Search Console. GSC tells you about search rankings. LLM Optimizer tells you about AI recommendations.

2. Identifies Content Gaps

The tool analyzes your existing content against the queries where your brand should appear but doesn’t. It identifies:

Topics where you have no authoritative content
Product information that isn’t machine-readable
Structured data gaps that prevent AI systems from understanding your catalog
Competitor content that’s being cited instead of yours

This is where GEO differs from SEO. It’s not just about keywords—it’s about whether your content is structured in a way that AI systems can extract and cite.

3. Suggests and Auto-Optimizes Content

LLM Optimizer doesn’t just report problems. It suggests fixes:

Content recommendations for missing topics
Schema markup improvements
Product data structure enhancements
FAQ content that addresses specific AI query patterns

For AEM Sites users, some of these optimizations can be applied automatically. The system can update structured data, add schema markup, and enhance product descriptions without manual intervention.

4. Supports Agent-to-Agent Protocols

This is the forward-looking piece. Adobe LLM Optimizer supports:

A2A (Agent-to-Agent): Protocols for AI systems to communicate with each other
MCP (Model Context Protocol): Standards for how AI models access and cite external data

As agentic commerce grows (AI shopping agents making purchases on behalf of humans), these protocols become critical. Your product data needs to be accessible not just to humans, but to AI agents making purchasing decisions.

Why This Matters for B2B Ecommerce

B2B buying behavior is shifting. Research from Morgan Stanley suggests ~50% of online shoppers will use AI shopping agents by 2030, accounting for 25% of ecommerce spending.

For B2B, the shift might be even more pronounced. B2B buyers research extensively before purchasing. They’re already using AI assistants to:

Compare suppliers
Understand technical specifications
Find solutions to operational problems
Get recommendations for complex purchasing decisions

If your brand doesn’t appear in these AI-generated recommendations, you’re invisible during the research phase. By the time the buyer visits your website (if they ever do), they may have already decided on a competitor.

The B2B Visibility Gap

Here’s what we’re seeing with B2B ecommerce clients:

Problem 1: Product data isn’t machine-readable. Catalogs with 50,000+ SKUs often have inconsistent descriptions, missing specifications, and no structured data. AI systems can’t parse this reliably.

Problem 2: Technical content doesn’t exist. When a buyer asks “What’s the best ERP integration approach for a mid-market distributor?” and you have no content addressing this, you won’t be recommended—even if you’ve done dozens of these projects.

Problem 3: No monitoring. You can’t fix what you can’t see. Most B2B companies have no visibility into how they appear (or don’t) in AI-generated responses.

What to Do About It

You don’t need Adobe LLM Optimizer to start improving AI visibility. But you do need a strategy.

Immediate Actions (No Tool Required)

1. Audit your product data for machine-readability.

Pull a sample of your product catalog. Can you extract structured attributes (dimensions, materials, specifications, compatibility) programmatically? If your product descriptions are unstructured prose, AI systems will struggle to use them.

2. Check your structured data coverage.

Run your key pages through Google’s Rich Results Test or Schema.org’s validator. Do you have Product schema on product pages? Organization schema sitewide? FAQ schema on relevant content? These signals help AI systems understand and cite your content.

3. Test AI assistants directly.

Ask ChatGPT, Perplexity, and Gemini questions your buyers would ask. Do you appear? Do competitors? What content are they citing?

This manual testing isn’t scalable, but it reveals gaps immediately.

4. Create content that answers specific questions.

AI assistants cite content that directly answers user queries. “What’s the best [product category] for [use case]?” pages perform better than generic category descriptions.

Medium-Term Strategy

5. Build a GEO content program.

Traditional SEO focuses on keywords with search volume. GEO focuses on questions buyers ask AI assistants—which may have zero traditional search volume but high commercial intent.

Identify the questions your sales team answers repeatedly. Create authoritative content for each. Make sure it’s structured, specific, and citable.

6. Implement comprehensive structured data.

Go beyond basic Product schema. Implement:

Detailed product specifications (weight, dimensions, materials, compatibility)
Pricing information (where appropriate)
Availability data
FAQ markup on relevant pages
HowTo markup on instructional content
Organization and LocalBusiness markup sitewide

7. Monitor AI visibility systematically.

Whether you use Adobe LLM Optimizer, a third-party tool, or a manual process, establish regular monitoring. Track which queries you appear in, which you don’t, and how this changes over time.

Long-Term: Prepare for Agentic Commerce

8. Make your commerce platform agent-accessible.

AI shopping agents will increasingly need to query product catalogs, check availability, and complete purchases programmatically. Your commerce platform needs APIs and data formats that support this.

This isn’t hypothetical. Klarna’s ChatGPT plugin already enables purchases through AI conversations. The infrastructure for agentic commerce exists—the question is whether your catalog is accessible to it.

Who Should Care About Adobe LLM Optimizer Specifically

Adobe LLM Optimizer makes the most sense if you:

Already run Adobe Experience Manager Sites (native integration)
Have a large product catalog (manual optimization doesn’t scale)
Need enterprise-grade monitoring and reporting (dashboards, historical data, competitive tracking)
Want automated optimization (vs. manual content updates)

If you’re running Adobe Commerce without AEM Sites, or if you’re on a different platform entirely, the underlying GEO strategies still apply—you’ll just implement them differently.

The Competitive Landscape

As of early 2026, very few agencies specialize in Adobe LLM Optimizer implementation or GEO strategy for commerce.

In the Adobe partner ecosystem, valantic (a large German agency with early access to LLM Optimizer) is one of the few actively marketing GEO services. In the broader agency space, firms like NoGood have built proprietary AI visibility tracking tools, but their focus is general marketing rather than B2B commerce operations.

This is a first-mover opportunity. The brands and agencies that build GEO expertise now will have a significant advantage as AI-powered discovery becomes mainstream.

Bottom Line

Adobe LLM Optimizer is a tool. GEO/AEO is a strategy.

The tool helps if you’re in the Adobe ecosystem and need scale. But the strategy matters regardless of your tech stack.

Your buyers are using AI assistants to research and recommend. If your brand doesn’t appear in those recommendations, you’re losing deals you never knew existed.

The question isn’t whether to optimize for AI visibility. It’s whether to start now—while the space is uncrowded—or later, when everyone is competing for the same recommendations.

Want to assess your AI visibility? We help B2B commerce teams understand how they appear in AI-generated recommendations and build strategies to improve it. Request an AI visibility assessment.