For two decades, search strategy meant earning a place on a results page and a click. That is changing. A growing share of search activity now happens through AI systems that read many sources, synthesize an answer and present it directly, sometimes without the person ever clicking through to a website.
This shift changes what CMOs should publish, how they should structure content, and how they should measure whether the strategy is working. Open Future Forum's own experience applying these ideas is described in helping executives find the right community: what AI search can teach us.
What is changing about search?
Historically, a person searching a question received a ranked list of links and chose which to click. Increasingly, AI-powered search features and chat assistants read across many sources and generate a direct answer, often citing a small number of sources rather than a full page of results.
That means visibility no longer depends only on ranking well. It depends on being one of the sources an AI system chooses to draw from, and being represented accurately when it does.
What is AEO?
Answer Engine Optimization is the practice of structuring content so that AI systems, including chat assistants and AI-powered search features, can find it, understand it and use it to construct a direct answer, rather than simply ranking it as a link for a person to click.
AEO tends to favor content that answers a specific question clearly and directly, with structure that makes it easy for a system to extract the relevant fact or explanation.
What is GEO?
Generative Engine Optimization is a closely related practice focused on how content is represented, cited and synthesized inside AI-generated responses more broadly. Where AEO focuses on being selected as a source for a direct answer to a specific question, GEO focuses on how a brand's information, data and point of view show up across generative AI outputs generally, including summaries, comparisons and recommendations.
In practice, the two overlap heavily, and most CMOs will treat them as a combined discipline sitting alongside traditional SEO rather than as two entirely separate strategies.
SEO still matters
None of this replaces traditional SEO. Technical performance, clear site structure, accurate metadata and genuine relevance to a topic remain foundational. AI systems still rely heavily on the same underlying web content that traditional search has always indexed. AEO and GEO are additional layers built on top of that foundation, not a substitute for it.
Original research becomes more valuable
When an AI system is choosing which sources to cite among many similar articles making similar claims, original data, direct research findings and a distinct point of view are more likely to be selected than content that restates widely available information.
This is one reason original research, such as Open Future Forum's CMO AI Market Map and the broader AI Leverage Report series, has value beyond the audience that reads it directly. It becomes a citable source that AI systems can draw on when answering related questions.
Being cited may matter more than producing another article
In a world where AI systems synthesize answers from a limited set of sources, being one of those sources for a relevant question can matter more than producing additional content that competes with everything else already published on the same topic.
That reorders some traditional content marketing priorities. Volume matters less than distinctiveness. A single well-cited piece of original research may do more for visibility than a dozen general articles covering ground already well covered elsewhere.
Third-party corroboration matters
AI systems weigh not just what a brand says about itself, but whether that claim is corroborated elsewhere: press coverage, independent citations, third-party data and other sources referencing the same finding. A claim that only appears on a company's own website carries less weight than one that is echoed across independent sources.
This raises the importance of coordinated PR and content strategy: getting original findings picked up, cited and referenced elsewhere, rather than publishing them once and moving on.
A brand is becoming a collection of relationships
Increasingly, how a brand is represented in AI-generated answers depends on the web of sources, citations and third-party references connected to it, not solely on the brand's own website. That makes earned media, partnerships, research citations and independent coverage part of the same visibility strategy that used to live mostly within SEO.
What should CMOs publish for AI search?
Practical priorities include:
- 01Original research with real data, not restated third-party findings
- 02Direct answers to specific, clearly framed questions
- 03Clear structure that separates distinct questions and answers
- 04A distinct point of view, not a summary of consensus opinion
- 05Accurate, current information that will not need frequent correction
How should CMOs structure answerable content?
Content structured around clear questions and direct answers, such as dedicated FAQ sections and explicitly labeled headings that mirror how a person would phrase a question, tends to be easier for AI systems to extract and cite accurately. Long, unstructured prose covering many ideas without clear headers is harder for a system to parse into a specific answer.
This does not mean sacrificing depth. It means pairing depth with clear structure, so both a human reader and an AI system can find the specific answer they need.
How should PR change?
PR strategy increasingly needs to account for how findings will be corroborated and cited beyond the original placement. A single press mention has value. A finding that is referenced across multiple independent sources over time has more durable value for AI visibility, because it signals to AI systems that the underlying claim is well-established rather than a single unverified assertion.
What should CMOs measure?
Traditional search visibility metrics, rankings, organic traffic and click-through rate, remain relevant but incomplete. CMOs increasingly need to track a newer concept: Share of Answers, how often a brand's content, data or point of view is the source behind an AI-generated answer on relevant topics, similar in spirit to share of voice but measured against what AI systems actually cite rather than what a search engine ranks.
This is a newer and less standardized measurement area than traditional SEO tracking. CMOs should expect the tools and methods for measuring it to keep evolving, and should treat early Share of Answers tracking as directional rather than precise. See how CMOs should measure AI ROI for how this fits into a broader measurement framework.
From ranking pages to earning inclusion
The underlying shift is simple to state and harder to execute: visibility is moving from ranking a page to earning inclusion in an answer. That favors original, well-corroborated, clearly structured content over volume, and it makes research, PR and content strategy more interdependent than they have been in the SEO era alone.
Last updated: September 28, 2026
Frequently Asked Questions
Open Future Forum publishes original research examining enterprise AI from different executive perspectives, including the CMO AI Leverage Report, CFO AI Leverage Report, CEO AI Leverage Report, CISO AI Leverage Report and broader executive AI research. Individual research findings should be read alongside the methodology and sample described in the underlying Open Future Forum reports.
To go deeper:
- Read how AI search is changing how executives find communities
- See how CMOs should measure AI ROI
- Explore the CMO AI Market Map
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Talk through AI search strategy with peers
CMOs discuss AEO, GEO and content strategy off the record at Open Future Forum's private gatherings.