Hey AI, buy my product: How LLMs are rewriting the customer journey
As search engines give way to answer engines, businesses are not fully prepared to start persuading large language models—not just people.
Marketers were early adopters of Gen AI, using it to create ad campaigns, generate product descriptions, and automate customer outreach. But in the rush to optimize marketing ops, many have overlooked a more profound shift in how consumers and machines will be making decisions.
In a world where consumers increasingly consult LLMs before making purchase decisions and, soon, where AI agents will make choices on their behalf, you need to rethink who you need to influence, and how.
Having a Gen AI strategy that doesn’t account for this B2AI wave feels like you are sharpening your pencils during a printing press revolution.
1. AI as a new front for marketing persuasion
Digital platforms popularized terms like B2B2C—where companies sell to customers through powerful platforms that curate and matchmake. Instacart, for example, partners with grocery retailers and CPG brands, but owns the user experience, data, and customer relationships. Search engines similarly intermediate between brands and shoppers. Now many of us are outsourcing our entire shopping research to AI for everything from consumer use cases like which wallet to buy as a father’s day gift to enterprise use cases such as which consent management platform to license for your business. Tools like ChatGPT are becoming trusted concierges: instead of giving you search results, they search, synthesize, and recommend an option.
And it may not stop there. Today, buyers consult ChatGPT and make the final decisions. Tomorrow, their personalized agents might ask an enterprise’s AI agent for product information, negotiate pricing, compare personalized bundles in real time, and make purchase decisions. The better those agents understand the user and the world, the more they will dominate as the interface between supply and demand. (Yes, this means loss of agency, but the march to that future began long ago with newsfeed personalization and recommendation systems. I discuss this in detail in my book).
2. AI’s impact by the numbers
Adobe reports that 39 percent of consumers in the US have used generative AI for online shopping and Forrester reports that 89% of B2B buyers now use Gen AI in the purchase process for everything from vendor discovery to evaluation. To be clear, the actual volume of traffic coming from LLMs is still a tiny fraction of web traffic for most businesses but the numbers are growing fast. For example, the same Adobe report indicates that “traffic from generative AI sources increased by 1,200 percent” in the 7 months from Jul ‘24 to Feb ‘25.
LLM usage patterns are different from search. Only 30% of LLM questions match traditional search while “the other 70% consisted of unique queries rarely seen in standard search engines” [1] Further, top cited domains are different for different LLMs. Google favors big domains but ChatGPT is comfortable recommending smaller brands and domains [1].
AI Overviews (AIO) in search results is also reshaping what it means to “rank” in search results. Bain reports that 60% of all searches now end without a user clicking through to a website, because the answer is consumed directly from the AIO. AIOs are showing up more often in search results. In Healthcare and education, 90% of search results pages have AI overviews [2]. B2B tech industry saw increase in AIO from 36% to 70%. AIO is rarer for ecommerce today, which likely helps protect Google’s monetization. When Google’s AI Overviews (AIO) is present, click-through rates on the top organic link drop by 34.5%, according to Ahrefs. For paid search as well, CTR drops by 15-20% when AIO is present [3]. For one B2B SaaS startup I invested in, AIO shows up in 74% of their top keyword clusters and this has lowered CTR in both SEO (-37%) and paid search (-9%).
Competing for AI’s “attention,” whether through tools like ChatGPT or AI Overviews in search, is the new marketing battleground. This is variously referred to as Generative Engine Optimization (GEO) or AI Engine Optimization (AEO), where the goal is to be cited or recommended by AI systems. To succeed, brands must understand how they are represented in AI training data, how clearly their value propositions are expressed in structured formats, how they are recommended by search tools used by LLMs, and how their products are interpreted in LLM inference steps.
3. A new problem statement
As AI agents take over product research, evaluation, and decisioning layers of the customer purchase journey, traditional assumptions about influence, messaging, and brand visibility no longer apply. We must ask:
How do we rewire marketing and CX for a future where our primary audience is equally human and AI?
How do we ensure AI systems are reliable and accurate ambassadors for our brand?
How do we prepare for an AI-to-AI world in which customers’ AI agents will want to talk to our enterprise AI agents about our products and services?
These are not purely technical challenges, nor are they purely creative ones. They touch everything from how we describe our products and structure our data to how we anticipate customers will use AI in the future. And while we don’t yet have a complete playbook for answering these questions, some elements of a solution are emerging.
4. Elements of a playbook
Your presence in AI results is shaped by many factors: (i) Whether LLM crawlers have discovered your content (ii) how your brand is represented in the LLM training dataset (iii) Which search tools are used by the LLMs and how you show up in the results (iv) The actual content of your pages and how it impacts LLM inference.
Discovery by LLM crawlers: Many websites unintentionally block AI crawlers through their robots.txt or CDN settings. For instance, Cloudflare recently began blocking LLM crawlers by default. That may make sense if you're a publisher monetizing your content (like a news outlet), but for most businesses aiming to be discoverable, it’s worth reviewing those settings. To explicitly make your site accessible to LLMs, implement a llms.txt file; it is a new standard that helps LLMs find and understand your content. There are two main formats: (i) llms.txt: A lightweight index of your pages, with titles and descriptions. (ii) llms-full.txt: A more comprehensive version that includes full page content. You can try out a generator like Firecrawl’s tool to get started.
Beyond access, focus on machine readability. It's not enough for AI crawlers to read your content; they need to understand it. That’s where structured data (or schema markup) comes in. Structured data adds a machine-readable layer to your site, by adding tags to clarify whether a piece of content is a product, a review, a FAQ, or something else entirely. While structured data has long played a role in SEO, it’s just as important in the Gen AI era.Training Data: Just like humans have emotional reactions to brand names, LLMs are predisposed to view brands one way or another based on how those brands up in AI’s training data. This is not merely about your first-party data being crawled by LLMs but also includes your brand shows up on Reddit, Youtube, Substack, and more. OpenAI, for example, has started licensing data from Reddit. Similarly, many LLMs have started licensing data from news outlets, and how you show up in news media matters as well. While search engines will show a list of results to a user and therefore present a range of perspectives on your brand, a Neural Network’s weights reflect the net impact of all the training data. As a result, it is less likely to present a range of perspectives but a single viewpoint informed by all of its sources.
Search engines: For many queries, tools like ChatGPT use real-time search capabilities to retrieve the latest information and then synthesize a response. The good news? Your SEO efforts still count. If your site ranks well in traditional search engines, it’s more likely to surface when an LLM uses search tools. But the catch is that different models use different search backends. Gemini uses Google, Anthropic’s Claude uses Brave Search, and ChatGPT defaults to Bing (though there’s growing speculation that it may be incorporating Google as well). That means you can’t optimize solely for Google anymore. In the short term, improving your search rankings is the fastest path to visibility in LLM-powered experiences. That’s because LLM training data are only updated every 6–9 months, whereas search-based responses reflect real-time content. What’s more important — being cited or just showing up in the results (yes, I just used an em-dash there and no, it’s not AI)? Citation is harder to achieve and may not reliably drive clicks, since many users don't even view the citation details. So for now, focus on strong visibility in the results.
Website Content: Just as certain messages are more emotionally persuasive to human brains, some words on your website are likely to activate certain parts of the LLM’s Neural network and likely to push your website higher in the AI results. Unfortunately (or should I say fortunately), there is no golden rule here on which types of phrases help and it requires significant experimentation. There are a couple early “best practices” I have heard but no formal academic study has proven these two strategies: (i) Prioritize your first 100 words. LLMs like ChatGPT often quote or cite the first meaningful paragraph they find. Placing key insights or summary content near the top of your page might help. (ii) Experiment with FAQ formats. LLMs are trained heavily on question–answer pairs, so that format may align more naturally. Overall, this is a very promising AEO approach but is also the one that is least understood and can benefit from some formal A/B tests.
Agents: This part is science fiction as of today but it is not set in a distant future. Imagine a future where a buyer’s AI agent knows their budget, health goals, preferences, and schedule, and finds the right travel package without the human ever intervening. Or picture a business AI agent that autonomously researches, selects, and purchases a SaaS or AI tool on behalf of a team. In this world, AI agents for buyers and sellers trade information in real time not unlike how real-time ad buying works today. In this world, it won’t be enough to just make your content machine-readable. Your business will need its own AI agent to interpret, synthesize, and respond to questions from a buyer’s AI agent using your entire body of content. That will be the start of AI-to-AI (i.e. agentic) commerce.
A recent study showed that that the biggest driver of your rank/visibility in LLM answers when search tools are used by the LLM is your rank in the search results. Next, it is how you show up in the training data. A brand that is well represented in the training data may be AI’s top recommendation even if it is consistently ranked lower in search results. The third — and still quite important — factor is your website content/wording and how that activates the Neural network (I am starting to get nervous about my use of em-dash … what a sad day for writers).
5. Next steps
How to kickstart your GEO journey:
Start tracking AI-driven traffic to your site: Even if the numbers are low today, they’re likely undercounting. Why? Because many AI tools don’t link directly to your site. Instead, users see your brand in an AI-generated answer, then type it into their browser. Attribution is fuzzy but the trend line is worth tracking regardless.
Use a basic measurement platform: Start tracking how visible your brand (and your competitors) are in AI-generated answers across key topic areas. While no one knows exactly what people are searching for on LLMs, you can use proxies such as which topics get more search volume on Google. I’m not endorsing a specific tool here, but feel free to DM or email me for suggestions.
Make sure LLM crawlers can access your site: Check your robots.txt and CDN settings to ensure you’re not accidentally blocking AI crawlers. Also, think beyond Google. For example, verify your site is being indexed by Bing. Here is an example of a site that has unknowingly blocked Bing.
Some websites are repeatedly cited by LLMs. If those sites link to you, you're more likely to appear in AI-generated answers. GEO measurement platforms can help you find your top citation domains. These are the partners you should prioritize for affiliate marketing.
How do you measure success?
Your immediate goal over the next quarter should be education. Try, test, and learn. For the next couple quarters, focus on:
Visibility: Is your brand showing up in LLM answers and how high do you show up for high-volume topic areas
Content citation rates: How often is your website being cited (helpful but indirect measure of success)
Performance marketers will not love the above two metrics because they don’t reflect impact on your website traffic or revenues. But I believe that attribution will improve soon. Today, most LLMs don’t show clickable links in their responses. But that’s likely to change especially as these tools move toward monetization and need to track outbound traffic. When that shift happens, brands that have been investing early will be far ahead.
Finall
REFERENCES/REPORTS
Mr.Kartik’s research finds that in the rush to optimize marketing ops, many marketers have overlooked a more profound shift in how consumers and machines will be making decisions.In instant PLAYBOOK titled—“Hi AI, buy my product: How LLMs are rewriting the customer journey”—Mr.Kartik,The Wharton Professor and one of the Greatest AI Thinkers of our times,once again draws our attention to The Third WAVE of AI (after chips & infra). His Specific Knowledge of the changing world where consumers increasingly consult LLMs before making purchase decisions and, soon, where AI agents will make choices on their behalf,emphasise the urgent need to rethink who you need to influence, and what how of what AI marketing is, how to use it, with examples, pros and cons, and strategies that benefit from AI.
I’ve greatly benefited from the article and believe this will transform how marketers plan.
Good points about optimization strategies. On a related note, I've been exploring LLMs.txt implementation for better AI discoverability.
Unlike robots.txt which uses commands, LLMs.txt files communicate with AI systems in natural language. They're becoming essential as AI-powered discovery grows beyond traditional search.
The technical implementation is straightforward, especially for Webflow users since the platform has built-in upload capabilities. There's even a specialized generator (https://flowranker.com/webflow-llms-txt-file-generator-free/) that handles the formatting and optimization automatically.
Interesting to see how web standards are evolving to accommodate AI systems alongside traditional crawlers.