How LLMs Change the Economics of Top-of-Funnel Keywords

AI keyword economics are rewiring how top-of-funnel search creates value for your business. For years, broad informational keywords were treated as cheap awareness plays that you could scale with blog posts and generic paid campaigns. Large language models, AI Overviews, and chat interfaces have broken that assumption by inserting an intelligent middle layer between the query and your site. The result is a completely different relationship between impressions, clicks, and revenue.

To keep winning high-intent attention, you now have to understand how LLMs interpret queries, decide when to surface answers, and choose which brands to cite or ignore. This article walks through how that shift changes the economics of top-of-funnel keywords, introduces practical models for valuing opportunities, and shows how to rebuild your acquisition strategy around the new AI discovery layer.

Advance Your SEO

Rethinking AI Keyword Economics at the Top of the Funnel

At its core, AI keyword economics describes how value flows through a query in an environment where LLMs mediate discovery. It combines three dimensions: how often a topic is searched or prompted, how much of that demand turns into visits or assisted conversions, and what it costs to win visibility across AI summaries, chat responses, and traditional SERPs. For top-of-funnel keywords, that economic equation has become far more complex than “rank, get clicks, measure last-click conversions.”

In the pre-LLM era, the model was simple: higher organic rank plus acceptable CPC equaled more visitors and a predictable share of leads or purchases. Search volume, click-through rate, and on-site conversion rate were enough to guide most TOFU decisions. Now, AI Overviews can answer the query without a click, chatbots can summarize multiple sources into one response, and users can explore an entire category without ever landing on your domain.

This means the “supply” of clickable impressions for a given keyword has shrunk, while competition for the remaining opportunities has intensified. At the same time, each individual visit can be more valuable, because users who do click often arrive later in their decision process, after doing more research inside AI environments. To navigate that trade-off, you need to treat AI keyword economics as an explicit modeling exercise, not a vague side effect of algorithm changes.

Leaders are already reallocating budget accordingly. 65% of senior executives identify leveraging AI and predictive analytics as primary contributors to growth in 2025. That is a signal that acquisition strategies, including search, are being rebuilt around AI-native forecasting and decision frameworks rather than historical rank reports.

For top-of-funnel, this mindset shift is critical. Early-stage queries often have the highest volumes but the weakest direct conversion rates, making them easy to over-invest in when content was expensive and measurement was crude. LLMs flip that logic: content production is cheaper, measurement is richer, but the window to capture a user is smaller. AI keyword economics helps you decide where that new combination still pays off.

From keyword lists to entity-based demand portfolios

Traditional SEO and paid search planning began with long lists of keywords: thousands of variations grouped into ad groups or content clusters. In an LLM-first world, models think in terms of entities and relationships, not isolated phrases. They build knowledge graphs that connect brands, problems, solutions, and attributes, then use those graphs to generate answers regardless of the exact wording of a query.

That has two economic implications. First, visibility is increasingly portfolio-based: strengthening your authority around a problem space can improve your chances of being cited or surfaced for dozens of related prompts. Second, budgets start to shift from optimizing individual keywords to building out entity clarity and structured data. Enterprises will invest up to five times more in LLM-focused optimization than classic SEO by 2029, reflecting this move toward holistic visibility.

Practically, you should think less about “owning keyword X” and more about “owning the conversation around problem Y.” That means mapping the full set of questions, misconceptions, and workflows your buyers explore in AI tools, then building content, tools, and data structures that make your brand the most reliable entity for those conversations. AI keyword economics becomes the method for deciding which problem spaces are worth a heavier investment.

How LLM Search Reshapes the Value of Top-of-Funnel Traffic

LLM-powered search engines and assistants are compressing what used to be multi-click research journeys into a few conversational turns. For many informational queries, users now get a synthesized answer at the top of the SERP or in a chat interface, with a handful of cited sources but far fewer actual visits. Understanding how that compression works is the first step in updating your economic model for TOFU keywords.

AI Overviews, chat answers, and the shrinking click pool

Google AI Overviews now appear in 21% of all search results, a dramatic jump from the low single digits only a short time ago. Every time an overview appears, it crowds traditional organic listings lower on the page and satisfies a portion of the query’s informational intent without requiring a click. For low-stakes, general questions, that can mean most of the “old” traffic never materializes.

On the paid side, top-of-page real estate has become even more precious. Paid search CPCs increased by 18% on average between Q4 2024 and Q4 2025, reflecting more competition for fewer high-visibility slots. That increase directly changes the cost side of AI keyword economics for broad terms whose visible ad inventory has been compressed by AI answer boxes and chat entry points.

Even with fewer clicks to go around, click-through rate still matters because it determines how much of the remaining demand you actually capture. Google and other platforms continue to reward assets that earn engagement, whether they are ads, organic listings, or AI-cited sources. Work on titles, descriptions, and snippet intent remains critical, as outlined in this explainer on why CTR still matters in an AI-driven search world, but the objective shifts from maximizing raw traffic to maximizing high-quality interactions that AI hasn’t fully absorbed.

Which top-of-funnel queries still drive clicks?

Not all top-of-funnel queries are equally vulnerable to zero-click AI answers. Some categories continue to generate healthy site visits because users need depth, interactivity, or brand reassurance that goes beyond a single synthesized paragraph. Identifying those categories is crucial for deciding where to invest content and media budget.

Four types of TOFU queries tend to retain strong click potential:

• High-stakes research, where users want to validate multiple sources before trusting an answer.
• Tool- or template-seeking queries that require interactive experiences, calculators, or downloads.
• Complex comparisons that benefit from visual tables, demos, or nuanced trade-off explanations.
• Location- or inventory-dependent searches where users need specific, up-to-date availability.

AI chat shifts user intent even within these categories, because people ask more conversational, multi-step questions and expect the assistant to keep context across turns. Understanding that behavioral change, as explored in depth in this analysis of how user intent shifts when traffic comes from AI search engines, helps you design landing experiences that match the more informed, context-rich mindset of visitors who do click through.

From an economic perspective, these resilient query types deserve disproportionate attention in your AI-era TOFU portfolio. They offer enough residual click volume to justify investment, while the LLM layer often pre-qualifies visitors by answering the simplest questions upstream. That combination (fewer but more valuable visits) is exactly what a modern AI keyword economics model should capture.

AI Keyword Economics Model: Frameworks, Formulas, and TOFU Playbooks

Once you understand how LLMs reshape supply and demand for attention, the next step is turning those insights into a repeatable model. Rather than treating every new AI feature as a one-off disruption, you can build a framework for AI keyword economics that guides content, paid media, and experimentation decisions across your entire top-of-funnel portfolio.

Core AI keyword economics formula for TOFU decisions

A practical way to operationalize AI keyword economics is to estimate the expected value of a keyword or topic cluster as follows:

Expected Keyword Value = (AI-adjusted impressions × LLM-visible CTR × assisted conversion rate × profit per conversion) − (content production cost + media cost + AI/tool cost allocation)

AI-adjusted impressions start with traditional search volume but discount for how often an AI overview or chat answer fully satisfies the query without a click. It also incorporates your expected share of LLM citations or surfaces; for example, if you are the primary source for a popular definition, your effective impression share inside AI environments may be higher than your classic rank suggests. LLM-visible CTR then estimates what portion of those opportunities leads users to interact with your listing or brand.

Top-of-funnel measurement requires focusing on the assisted conversion rate rather than on last-click performance alone. Many TOFU touches influence opportunities, pipeline, or eventual purchases days or weeks later. You can approximate this by looking at multi-touch attribution, cohort analyses, or lift tests in which you increase coverage in a problem space and measure downstream changes. The cost side of the equation includes both the incremental expense of content and creative, which may be lower with generative AI, and the incremental media or tool investments needed to win and maintain AI visibility.

Advance Your SEO

LLM top-of-funnel keyword taxonomy

With a formula in place, you need a way to categorize TOFU opportunities based on how people actually use AI tools. Instead of classic “informational vs transactional” labels, it is more helpful to group queries by the role they play in an LLM-mediated journey. That taxonomy guides what assets you create and how you expect them to perform economically.

• Problem-framing prompts — “Why am I losing pipeline after demo?” or “What causes high cloud bills?” Users are clarifying the root issue.
• Concept-exploration queries — “Explain usage-based pricing” or “What is zero-party data?” Users want digestible explanations and mental models.
• Solution-category explorations — “Best B2B analytics platforms” or “alternatives to traditional ERPs.” Users look for landscape overviews and comparison frameworks.
• Workflow and job-to-be-done prompts — “Create a go-to-market plan for a PLG SaaS” or “Outline a monthly budget.” Users seek templates and step-by-step guides.
• Tool-augmented queries — “Generate a customer survey in Google Forms” or “Build a dashboard in Looker Studio.” Users want concrete outputs.

Each category suggests different tactics. Problem-framing and concept-exploration prompts benefit from authoritative explainers and visual mental models that LLMs can safely summarize and cite. Workflow prompts call for templates, checklists, and calculators that are hard to fully reproduce inside a chat response, increasing the odds of a click. Tool-augmented queries often respond best to interactive experiences and product-led content that turn AI-assisted curiosity into hands-on engagement with your solution.

Hybrid paid and organic decisions in an LLM world

AI keyword economics should govern both organic and paid investment, especially at the top of the funnel, where margins can be thin. Some keywords will have high AI-adjusted impressions but poor economics for paid due to elevated CPCs; others might justify aggressive bidding as “entry points” into profitable nurture sequences or expansion motions. Your model should flag which topics merit hybrid coverage and which should lean heavily on one channel.

One effective approach is to identify a small set of TOFU clusters where you deliberately over-invest in both AI-optimized content and paid campaigns, accepting break-even or slightly negative first-touch economics because the assisted revenue they generate is strong. For other clusters, your objective might be to dominate AI answers and organic visibility while using negligible media spend. Using AI to monitor overlap and identify PPC keyword cannibalization helps prevent wasting budget where organic and AI visibility are already doing the job.

Scenario planning is essential here. You can use an AI search forecasting framework for modern SEO and revenue teams to estimate how shifts in AI overview prevalence, citation share, or CPCs would change keyword-level profitability. Feeding those scenarios into your portfolio model turns AI keyword economics from a static spreadsheet into a living planning tool that supports quarterly budgeting and experimentation.

For organizations that want to move quickly, partnering with specialists in SEVO (Search Everywhere Optimization) and AI-era paid media can accelerate the transition from ad hoc reactions to a coherent AI keyword-economics strategy.

Execution, Measurement, and Governance in AI Keyword Economics

Designing a model is only half the battle; you also need an execution system that keeps your AI keyword economics assumptions aligned with reality. That system spans data collection, reporting, content operations, and governance to ensure you neither overreact to noisy AI signals nor underreact to structural shifts in how people discover solutions.

Data stack for AI keyword economics

Measuring AI-era performance requires combining multiple data sources that historically lived in separate silos. Search Console and analytics platforms still matter for understanding residual organic clicks and on-site behavior. Paid search and social platforms provide auction prices, impression share, and assisted conversion paths. Newer inputs include AI SERP scrapers, LLM citation tracking tools, and prompt logs from internal or product-embedded assistants that reveal how prospects phrase their problems.

Forward-looking teams are also tracking LLM-specific KPIs, such as citation frequency, conversational share of voice, and entity coverage, across major models. Those metrics plug directly into the “AI-adjusted impressions” and “LLM-visible CTR” parts of your economics model, even when traditional rank reports look flat or misleading.

On the content side, schema and structured data play a growing role because they help models interpret and trust your information. Detailed product, FAQ, and organization markup clarify which entities you represent and what you are authoritative about. This is especially important as outlined in this discussion of how AI models interpret schema markup beyond rich results, where the impact goes far beyond classic rich snippets and into how LLMs construct their internal knowledge graphs.

Bringing all of this together usually requires a lightweight analytics layer that can join AI visibility data with revenue metrics. From there, dashboards should answer a few simple but powerful questions: which TOFU clusters are gaining or losing AI visibility, how that shift is affecting pipeline contribution, and where incremental content or media spend would change the trajectory.

Industry-specific applications and playbooks

AI keyword economics plays out differently across industries because search behavior and monetization models vary. B2B SaaS often cares most about long, multi-touch journeys and high lifetime value, while ecommerce focuses on shorter cycles and repeat purchase behavior. Financial services and fintech balance strict compliance requirements with the need to educate users about complex products.

For B2B SaaS, AI keyword economics often justifies deep investment in educational hubs around specific jobs-to-be-done, even when direct search traffic declines, because those hubs strongly influence opportunity creation measured in CRM. E-commerce brands may prioritize multilingual expansion to capture AI-driven discovery in new markets, leveraging approaches similar to multilingual AI SEO for translating and localizing at scale. Highly regulated industries, meanwhile, must weigh the incremental value of AI visibility against governance overhead and risk tolerance.

The common thread is that each vertical needs its own mapping from AI surfaces to business outcomes. That mapping informs the threshold at which a TOFU keyword cluster becomes economically attractive, or too risky, to pursue aggressively.

Risk, bias, and experimentation safeguards

Over-optimizing around AI signals can be as dangerous as ignoring them. LLMs hallucinate, over-index on noisy sources, and change behavior as they are retrained. If you blindly chase every apparent opportunity (for example, a sudden spike in suggested prompts from a third-party tool), you risk creating content for phantom demand that never materializes in your revenue data.

To guard against this, build experiments and validation into your AI keyword economics workflow. Before scaling investment in a new TOFU cluster, run controlled tests: publish a small set of assets, lightly support them with paid, and watch both AI visibility metrics and downstream pipeline for a fixed period. Use holdout regions or segments where feasible to distinguish real impact from background noise or seasonality.

Governance also matters. Cross-functional councils that include marketing, product, data, and legal or compliance stakeholders can set guardrails around which AI surfaces you aim to influence and which tactics are off-limits. Clear documentation of your modeling assumptions, data sources, and decision criteria helps future-proof your strategy as models, regulations, and user expectations evolve.

Turning AI Keyword Economics Into a Growth Advantage

LLMs, AI Overviews, and conversational search have permanently changed how top-of-funnel demand behaves, but they have not eliminated the opportunity to win that demand. Instead, they have raised the bar: only teams that treat AI keyword economics as a first-class discipline (grounded in clear models, cross-channel execution, and rigorous measurement) will consistently turn early-stage curiosity into revenue.

For most growth-stage and enterprise organizations, that means shifting from keyword lists to entity-based demand portfolios, from last-click metrics to assisted-value modeling, and from reactive content production to planned experiments across AI, organic, and paid surfaces. When those changes are anchored in a quantitative framework, top-of-funnel stops being a fuzzy brand cost center and becomes a predictable contributor to LTV and payback-period targets.

Brands that act now (rebuilding their keyword economics for an AI-centric world instead of clinging to pre-LLM assumptions) will own the next generation of top-of-funnel discovery. Putting AI keyword economics at the center of your planning today will give you a durable edge in where and how future customers first encounter your story.

If you want a partner that already operates at this intersection of SEVO, AEO, and performance media, Single Grain specializes in building AI-first search strategies that align with your revenue model. Our team blends technical SEO, LLM visibility optimization, and paid acquisition planning to help you operationalize AI keyword economics across Google, Bing, ChatGPT, Gemini, Perplexity, and emerging discovery channels. To see how this approach could reshape your own TOFU portfolio, visit Single Grain and get a FREE consultation with our strategists.

Advance Your SEO