Most marketers are currently thinking about AI through the lens of visibility (if they’re thinking about it all).

How often does my brand appear in ChatGPT or other LLMs? Which prompt responses mention my product or category? What sources are influencing AI-generated answers?

Those questions are useful, but incomplete. The real shift is not just AI visibility, it’s AI-mediated discovery, and soon AI-mediated commerce. Today, most generative AI chatbots and other AI-enabled search tools stop at recommendations. They help consumers research and compare products, but the purchase still largely happens elsewhere.

As AI platforms begin integrating payments, product catalogs, and advertising infrastructure, the discovery and transaction functions will converge. That shift is already underway: OpenAI and Stripe have introduced in-chat checkout, while retailers like Walmart are experimenting with letting customers complete purchases directly inside ChatGPT. When that convergence accelerates (likely later this year), the brands that win will not simply be the most visible, they will be the ones that understand how consumer behavior unfolds inside AI discovery environments. In other words, the future of commerce will be won at the discovery layer.

Defining AI Discovery and Where it Stands Today

Simply put, AI discovery is when people find brands through AI assistants and search rather than through Google, social media,or other channels.

Right now, most AI interactions follow a familiar pattern. A user asks a question such as: “What’s the best running shoe?” The AI responds with a synthesized answer that might reference several products, sources, or methodologies for how to find the right product. The user then clicks through to websites, review platforms, or product pages to continue researching in a more self led way and ultimately buying a product.

This process already has major implications for marketers. The LLMs interpret the prompt or conversation, gather information based on experience, expertise, authoritativeness, and trustworthiness and present answers with sources. As a result, brands increasingly appear through summaries, recommendations, and citations rather than traditional search rankings.

To understand that visibility (and improve it), marketers are scrambling to use Generative Engine Optimization (GEO) or AI Optimization (AIO) tools that track how their brands appear across these AI platforms. But there is an important limitation regarding how the GEO/AIO ecosystem works.

Most visibility tools are just swapping keywords for single-turn prompts. That can give directional insight, but it rarely reflects how people actually interact with AI systems.

Why Single-Prompt Visibility Is Not Enough

When was the last time you researched a product and asked just one question? That’s not how humans explore topics. Real users refine needs, explore tradeoffs, and compare alternatives through a multi-step conversation. That process can heavily influence what an AI ultimately recommends, versus a single contextless question.

For example, a user might start with a broad question: “What marketing automation platforms are best for B2B SaaS?”

Then it quickly evolves:

“Which options integrate well with HubSpot?”

“What’s the pricing range?”

“Which platforms are easiest for smaller teams?”

“Does X platform have APIs for integrating with Dripify?”

By the time the AI delivers a final recommendation, the system has processed multiple rounds of context. It’s effectively compressing what would have been a series of separate searches into a single, continuous conversation, with each turn reshaping the data, citations, and recommendations. If you’re a marketer that only measures visibility in isolated prompts, you’re missing most of that journey.

This gap is one of the biggest challenges in GEO today. Most GEO measurement relies on flooding LLMs with a single prompt and correlating that information with generic clickstream or traditional search data. Today, the actual user prompt data is not available from the frontier labs (LLMs). As a result, visibility metrics are often directionally interesting but difficult to tie directly to outcomes. To better understand AI discovery, marketers need to start thinking less about prompts and more about how users actually interact with AI and LLMs.

Modeling the AI Discovery Journey

One of the most useful frameworks emerging in AI search is behavioral simulation, where instead of testing isolated prompts, marketers simulate how real users research products through multi-step conversations.

For example, if you’re a probiotic soda brand, a traditional SEO approach would focus on ranking for a broad term like “best healthy soda.” or maybe “probiotic soda” The goal is volume.

But AI discovery doesn’t work that way. An LLM doesn’t return a list of links. It generates a recommendation based on who it thinks the user is and the context it has. Without that context, your premium, gut-health-focused product may get grouped with generic “better-for-you” drinks. You didn’t lose on quality, you lost on context.

Now compare that to persona-driven conversations:

A nutrition-focuses consumers asks about beverages for improving gut health, then drills into probiotic strains and efficacy.
A parent asks for a healthier alternative to soda for their kids, focusing on sugar and ingredients.
A biohacker asks about zero-glycemic drinks with live cultures and shelf stability.

Each path leads to different recommendations, sources, and outcomes and your brand may show up differently, or not at all.

This approach allows teams to analyze:

How recommendations evolve across a conversation
Which sources the model relies on at different stages
When competitor brands enter or exit the set
What signals influence the final answer

In practice, this involves running AI agents using persona-based scenarios, where each produces different conversational patterns and recommendation outcomes. This provides a clearer picture of how brands appear in AI-driven discovery.

The Next Phase: AI-Native Commerce and Advertising

Most AI platforms are still in the early stages of monetization, but agentic commerce and LLM ads are expected en masse later this year (they’re already in private beta). We’re already seeing experiments with sponsored recommendations in AI answers, product integrations inside AI interfaces, partnerships with commerce platforms and payment systems, and AI-powered shopping assistants.

As these capabilities mature, the line between recommendation and transaction will disappear and AI systems may support actions like:

Comparing products
Viewing pricing and reviews
Selecting configurations
Completing purchases

And when this happens, the discovery layer becomes even more important. If an AI system only recommends a handful of products and a user purchases directly inside that interface, the brands outside that set may never be seen.

Of course, there will be both paid and organic paths into those recommendations. Sponsored placements will play a role, but AI systems still rely on relevance, context, and trusted signals to generate answers. That means organic discovery will likely determine whether your brand shows up consistently across conversations. This is why understanding how AI forms recommendations is critical before paid options fully mature.

How Marketers Can Prepare

Agentic commerce AI ads are still emerging, but here are four recommendations to help prepare:

Treat AI as a Discovery Channel – Many companies still view AI visibility as an extension of SEO, it’s not. AI platforms synthesize information across sources, interpret content differently, and personalize responses based on conversational context. That means AI discovery should be treated as its own channel with its own measurement frameworks and strategies. As GEO/AIO matures, subscription costs are decreasing dramatically.
Move Beyond Single Prompt Rankings – Prompt testing is useful for directional insight, but it shouldn’t be the only measurement approach. Marketers should begin analyzing multi-step discovery journeys, not just isolated questions. Understanding how recommendations evolve during conversations provides much deeper insight into how AI systems interpret brand signals. New entrants into the GEO space are making this possible.
Analyze the Signals Influencing AI Answers – AI recommendations are shaped by a combination of signals, including editorial sources and citations, product data and structured information, reviews and third-party mentions, retailer and marketplace pages, and content clarity and authority. Understanding which signals influence recommendations the most allows marketers to prioritize improvements more effectively.
Build Discovery Intelligence Before Ads Arrive – Don’t wait for AI advertising products to mature. By the time large-scale LLM advertising launches, the brands with the best organic discovery presence will already have an advantage. Understanding how AI systems interpret your brand today will provide a critical foundation for future paid st...

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