The Era of the Simple Keyword Is Over
Something critical has evolved about how individuals find information, and most marketing groups have yet to catch up. There is still the traditional search process. Google still handles hundreds of millions of daily searches.
However, the landscape is no longer simply a single channel; it is now fragmented among traditional Search Engine Results Pages (SERPs), Google’s AI Overview (AIO) and a developing ecosystem of conversational chat interfaces including ChatGPT, Perplexity, Claude, Gemini and Microsoft Copilot each utilizing their own indices, rank their own sources and synthesize information differently than ever before.
The marketer who only optimizes for the ten blue links is playing an incomplete game.
The behavioral evolution that is underlying all of these changes is even greater than the technological ones. People are changing. While once a person may have typed “best CRM” into a search bar and scrolled through the results, today they now open an AI interface and type something much more intentional:
“Recommend a CRM for a small e-commerce business operating in Europe, with less than twenty users, comparing price levels under €50 per user.”
The ambiguity that characterized the keyword era and that SEO practitioners were able to exploit, is disappearing. Promoters do not rely on the engine to infer their intent. Instead, they declare it. The consequences of ignoring this trend are not hypothetical.
According to G2’s 2025 Buyer Behavior Report, 50 percent of B2B software buyers now begin their buying journey with an AI chatbot instead of Google, a 71 percent increase in only four months.
For enterprise technology buyers, a Treble/Censuswide study of 300 CIOs, CISOs, and CTOs discovered that 47 percent now initiate vendor research using AI assistants, more than Google Search at 43 percent.
Additionally, AI-referred visitors are not casual browsers. Research by Whitehat SEO’s AEO Team found that AI search traffic converts at 4 to 9 times the conversion rate of traditional organic search visitors, since they arrive pre-informed, pre-qualified, and beyond the awareness stage.
That is the thesis: We must evolve from traditional SEO to SEO + GEO (Generative Engine Optimization), a hybrid strategy that retains keyword volume research for traditional SERP visibility while incorporating prompt research to capture user intent, conversational context, and AI citation authority. The organizations that make this transition early will not only be seen in more searches. They will also be the referenced sources that AI systems reference, trust, and recommend, which, increasingly, is the only reference that matters.
How do reviews shape pages without a writer?
Landing pages no longer require manual drafting from scratch. Gemini prompts and customer reviews proactively extract, refine, and structure content based on user pain points, emotional triggers, and social proof. To convert consistently, copy must be built for psychological resonance—supported by sentiment analysis, thematic clustering, and authentic testimonials—so AI systems can confidently generate high-impact headlines and value propositions without relying on generic marketing templates alone.
"Strategic landing page development is no longer a manual exercise. By feeding Google Review data into Gemini, marketers transform raw customer sentiment into a singular, high-converting narrative with precision."
Deconstructing the Shift: Keywords vs. Prompts
Understanding the importance of the strategic shift requires understanding the structural differences between keywords and prompts, which, while not necessarily about length, fundamentally influence how we think about this process.
In terms of structure, keywords represent ambiguous signals. Generally, keywords are 2-5 words in length; have no context due to search bars; and do not tell you anything about who typed those keywords. Someone typing “project management software” could be a sole freelancer, a 500-person company, a non-profit operating on a shoestring, or a student doing research for a class. The only thing the keyword tells you is that there are a lot of possible users.
Because of the ambiguity of the keyword, the search engine’s job was to use all of the possible context clues available (location, search history, etc.) to infer the searcher’s intent and present a ranked list so that the user could sort out the best options.
The SEO practitioner’s job was to optimize for that ranked list, i.e., get your content ranked higher than others for that particular keyword. Prompts, however, are explicit statements of intent, are conversational in nature, generally are 10-25+ words in length, contain role context, constraints, desired output formats, and various scenarios; and require no inference on the part of the search engine.
Using the previous example, the same individual who would search for “project management software” could potentially create a prompt such as: “Compare the top three project management tools for remote teams of less than 50 people with a budget of less than $20/user, that integrate with Slack.”
The traditional guess work search engines have used to determine the intent of their users (users type in a few keywords, search engines try to figure out what they want) is being significantly reduced by AI chat systems (users now type in long, descriptive, conversational questions that include the context for what they are trying to accomplish).
The reason many professionals refer to the volume trap in traditional keyword research is due to this trend. Traditional keyword research uses tools like Ahrefs, SEMrush, and Moz to measure the demand of users searching for products and services by using data from Google Search results. However, the long, conversational questions users ask to purchase items through AI chat systems rarely produce measurable volumes in these tools.
In other words, the tools are measuring the wrong channel.
Although a query may have zero search volume in a keyword tool, there could still be thousands of users who are asking the exact same question about making a purchase each day using AI systems like ChatGPT or Perplexity.
Traditional search methods do not have the same advantages that prompts offer. By using prompts, users can tell an AI system what kind of output they want (e.g., a summary of data in a table format), what role they want the system to take (e.g., the system will be acting as a CFO evaluating potential vendor), and what other constraints there are (e.g., the tool or technology has to be available in the European Union and meet GDPR requirements).
As a result, user behavior is changing. Rather than simply looking up information with a passive approach, users are telling AI systems what they need to accomplish.
For marketers, this represents a very obvious transition. Instead of users asking AI where to find websites, users are asking AI to recommend solutions, generate lists of vendors, and help complete their research.
How to Conduct Prompt Research (New Methodologies)
Prompt research is not one particular method or technique; it is an entire area of research (a discipline) and a manner by which we identify how end-users interact with conversational AI interfaces in natural language and how we can provide appropriate responses to their expressions.
The three major methods for conducting prompt research that may be worth investigating include:
Method 1: Reverse-Engineering Methodology
To get a better understanding of how users are using search prompts there are many ways to start reverse engineering the language from those searches. An easy way to get started is by analyzing the “People Also Asked” (PAA) portion of Google’s results page. The PAA area has groups of user generated questions which represent similar types of questions that people are asking when they’re looking for answers.
As shown below, the question “what CRM is best for a small business with a low budget” is far more representative of how a person might communicate with AI than the very short keyword “best CRM”.
When you collect and organize these questions related to your cluster of topics, you’ll have a collection of natural language prompts that can serve as a valuable resource for optimizing your content so it aligns with how most people tend to frame their queries when performing conversational search.
Use Of Reddit Threads And Forums
The best ways to discover the true raw, unedited and unfiltered language that shows the buyer’s intention from a buying perspective in natural language are niche forums and Reddit. Because of this, conversational dialogue and responses from systems such as ChatGPT, Perplexity and Google AI Overviews, represent the raw unfiltered way users communicate about their issues.
A good example of this is when a user posts on a SaaS related subreddit stating that they are looking for a client invoicing automation tool that is less expensive than QuickBooks and also does not require them to hire a developer. The above example represents the type of language someone may use in a prompt when using an AI assistant in the future.
GenSpark has the ability to speed up your discovery process but going through relevant subreddits manually can provide emotionally charged and/or more nuanced language than GenSpark’s previous methods.
In addition, there are browser extensions called chatGPT search query extractor tools. These extensions display how an AI system takes one prompt (best HR software for a 200-person manufacturing company) and breaks it down into multiple backend searches. This is known as query fan-out.
Once you know how a single prompt (best HR software for a 200-person manufacturing company) is broken down into multiple searches, you will have a better understanding of the multiple layers of intent that exist behind a single question. And having a better understanding of the layers of intent behind a question, you will have a better understanding of the types of content that need to be developed to address each layer of intent in a question.
Method 2: AI-Assisted Persona Simulation
The second most valuable use of raw intent signals is to build out a series of how real people will experience all aspects of the Artificial Intelligence (AI) Discovery Process — not simply at one point in time — but through the entirety of their research process.
Role-playing Scenarios
Build a complete and accurate representation of a person who describes more than demographics, or characteristics of a person:
“Sarah is the Head of Operations for a 150 employee logistics company. Her CEO instructed her to replace their current Enterprise Resource Planning (ERP) System because the previous ERP Implementation failed. Sarah’s company has nearly zero IT Staff, and an $80K budget.”
Take this person and walk them through a Large Language Model (LLM) and have them mimic Sarah’s entire Research Journey. What questions would Sarah be asking herself on a daily basis from the time she first became aware of the subject matter, to when she was researching multiple vendors, to when she was evaluating the risk of implementing a new ERP?
It often produces High Intent, Middle-Funnel, and Bottom-Funnel Prompt Structures which are typically unidentifiable by utilizing conventional Keyword-Based Research Methods.
Root Cause Drilling: The 5 Whys Drill
Starting with a generic term or keyword (e.g., “sell car,” “change insurance,” “upgrade server”), that can be used as a foundation to create a list of Jobs To Be Done (JTB), take the same generic keyword and ask “why would anyone search for this” five times in a row to finally arrive at the underlying reason for the JTB.
“Why would someone search for ‘change insurance’?” Because their premium went up. “Why did their premium go up?” Because they made a claim. “Why did they make a claim?” Because they got into an accident. “Why did they get into an accident?” What do they really want? They want to find the best insurers that will not penalize them for filing their first claim.”
The above example shows how the 5 Whys can help you determine the actual reason behind the generic keyword and thus what a buyer that is interested in finding answers to the product they need can be searching for as they interact with a chatbot like ChatGPT.
Job To Be Done (JTBD) Framework
JTBD has primarily been used as a product development strategy. However, the Job To Be Done concept can be used to assist with prompt research. The reason why people buy a product is to be able to do a job. So, for instance, when a person types “buy vpn,” they don’t want a VPN. They want to be able to protect their digital identity as they travel to different countries overseas. They want to be able to view restricted content from different countries. They want to be able to secure confidential documents/files as they view the internet via public Wi-Fi. By asking an AI to create a list of the jobs that your product was hired to do, a list of natural language prompts can be created that correspond to each job.
Method 3: Prompt Testing and Validation
Until you verify it, research is essentially speculative. The last level of validation occurs when you verify it against your target audience in an AI system, and you measure the degree of cross-citation between you and your AI Native Competitor.
Insert each of your top 10-20 prompts that best represent your top value customer intent for each of your top competitors. Monitor each brand name, article title, tool name, and domain cited for each prompt. In most cases, you’ll find a surprise waiting for you. Your Google Page One Competitor may NOT be your new AI Native Competitor.
For example, a Thought Leadership Blog with a good information architecture and quality citations for references can dominate all response model outputs for all AI Responding Systems for your niche. However, it will probably never appear on Page One of Google Search Results for the same keyword(s).
These AI Native Competitors will become your measure of success.
Identify where the AI Overviews are on Google Search Results and where there are NO citations of your brand name. These are your Number One GEO Optimization Targets.
Semrush Enterprise AIO, Generative Parser by BrightEdge, and other platforms such as Profound Now offer measurable Share of Voice Data for prompts down to the prompt level across all ChatGPT, Perplexity, Gemini, and Google AI Mode, and measurable Share of Voice Data across all AI channels and not limited to traditional Search Engine Results Pages (SERPs).
43%
declined
in search traffic over the next 3 years.
280+
News executives surveyed
Optimizing Content for the Prompt Era (GEO Strategies)
The main problem with using AI is identifying the specific intent of a user in a query, or when searching for information online. Once you have identified this, the next requirement is for the content to be organized/structured in such a way that an artificial intelligence system can access the content; understand the content, and refer to it repeatedly and reliably. This is called Generative Engine Optimization, or GEO, and is based on a completely new paradigm of optimization compared to Search Engine Optimization (SEO).
The Structure for Retrieval-Augmented Generation (RAG)
All of the major Search Engines utilizing AI for searches (Google AI Summaries, ChatGPT Search, Perplexity, Claude, etc.) utilize some form of retrieval-augmented generation (RAG) to generate answers to searches. RAG generates responses through a process of searching for relevant documents/paragraphs at the time of the query and then synthesizing those retrieved portions into a cohesive response.
There are significant implications to the fact that RAG search engines do not rank content, but instead evaluate content based upon how easily it can be retrieved and referenced. To consistently be retrievable by RAG search engines, content must be structured in ways that allow the RAG search engine to recognize the semantic relationship between elements.
Examples of this include:
- Documents that are logically organized and have clear hierarchical heading structures that allow RAG search engines to determine the relationships between the components
- Using bullet points and comparative tables to break down dense amounts of information regarding specific attributes and to simplify extracting those attributes as discrete pieces of information
- Including definition statements at the beginning of each section to enable RAG search engines to generate a clean, citeable summary of the content without altering its original meaning
- Content may not be broken into micro-posts or thin FAQ pages; RAG search engines perform best when generating comprehensive, self-contained documents that are sufficiently deep to thoroughly address a subject area.
The Princeton GEO Study found that utilizing GEO optimization techniques such as statistical data integration, structuring format, and referencing authority sources, the visibility of content in generated responses by an AI search engine can be increased up to 40%.
Token efficiency is a consideration: a table comparing prices, features, etc. requires far fewer tokens to parse than an equivalent paragraph of prose for an LLM. Therefore, creating content that is optimized for both human and machine readability will result in the greatest citation rates.
The “They Ask, You Answer” (TAYA) Framework
The methodology was for creating an inbound marketing approach; however, the method has been expanded upon and is now considered to be one of the most effective methods for establishing a GEO content strategy. This is due to its replication of the conversation and research process used by today’s AI-era buyers.
There are five categories of content identified in the TAYA methodology that have the greatest degree of user confidence and, as such, the greatest number of AI citations:
- Pricing and Cost (“How much does X cost?”, “What determines the price of Y?”)
- Problems and Pain Points (realistic portrayals of where products or categories fall short)
- Comparisons and Versus Content (“X vs. Y: What is best for Z?”, etc.)
- Reviews and Validation Content
- Best-in-class Lists (“Top 5 Tools for X in Y Scenario”, etc.)
These categories of content produce the highest AI citations for a reason – they mirror the language and intent structure of search queries from high-stakes purchase research inquiries.
Therefore, the only practical take away from this is that if a buyer is typing this into ChatGPT to help identify a vendor for their inquiry, you should create a detailed, honest, and well-organized page on your website that answers that question. If you don’t answer that question with a webpage, then the competing vendor that does will receive the AI citation for answering that question.
Mirroring User Language
One of the most persistent failure modes in content marketing, keyword stuffing, jargon-heavy copy, and brand-voice-first writing, becomes actively harmful in the GEO era. AI retrieval systems prioritize content that directly mirrors the language of the user’s query. If a user asks “How do I fix an SSL certificate error in Chrome?”, content that begins “To fix an SSL certificate error in Chrome, follow these steps” is far more retrievable than content that begins with a brand introduction and buries the answer in paragraph four.
This principle extends to specificity. Vague, hedged content, “There are many factors to consider when choosing a CRM”, does not get cited because it cannot be directly attributed to a specific answer. Content structured as direct, explicit responses, “The three most important factors for choosing a CRM for e-commerce are: integration with your payment platform, native multi-currency support, and automation of abandoned cart follow-up”, provides AI systems with citable, extractable factual claims.
The practical standard: every piece of content should contain at least one sentence that could stand alone as a direct, cited answer to a specific prompt.
Focus on Freshness and Authority
AI systems are increasingly sophisticated in their evaluation of content authority and recency. Freshness matters because generative AI platforms actively prioritize up-to-date information; stale statistics, outdated comparisons, and superseded recommendations erode citation rates. The practical implication is a content refresh cadence: major GEO-targeted pages should be reviewed and updated on a 90-day cycle, with dates prominently displayed so AI retrieval systems can confirm recency.
Authority signals are equally important. Transparent author bios with demonstrated expertise, inline citations to primary research and recognized data sources, and cross-platform brand presence, across LinkedIn, Reddit, industry publications, and YouTube, all contribute to the trust signals that AI systems use to determine citation worthiness.
Content cited 9,000+ times across LLMs consistently shares these characteristics: named authors, verified data, logical structure, and a direct match between the content and the specific jobs buyers are trying to accomplish.
The Future is Conversational
The most significant aspect to understand regarding the transition from using keywords to creating prompts is that this is an evolutionary process rather than a revolutionary one. Traditional Search Engine Optimization (SEO) is not dead. Although Google still exerts massive influence over how people consume digital content, keyword-based traffic continues to generate considerable volumes of traffic throughout nearly all industries. While the ten blue links that were once the primary interface for consumers to discover new content continue to exist, they are now accompanied by another parallel discovery mechanism that is based on a very different type of reasoning.
The fundamental difference is that the completion requirement of a contemporary search strategy has become much greater than in the past. A keyword list is insufficient to complete a modern search strategy. A prompt library alone is likewise insufficient to complete a modern search strategy. Brands that are able to be successful in the AI age will need to use both a structured keyword strategy for achieving SERP visibility and a rigorously researched prompt library that is organized by persona, buying stage, and job-to-be-done to drive GEO content creation and optimization.
The underlying principle remains what it has always been: understand what your customer actually needs, speak to that need clearly and honestly, and make it easy for the systems people rely on, whether search engines or AI assistants, to connect your answer to their question. The tools have changed. The standard has not.
Take Action Now
Take inventory of how visible your company is in all things AI today. Go to Open Perplexity, then go to ChatGPT, and type in your top ten buyer-relevant questions for your company. Who is citing your company? Is it your competition? Where do the gaps exist? Start creating the content to fill those gaps. Buyers who will decide on your company tomorrow have already started asking questions and are moving toward a buying decision today.