The SEOFOMO State of AI Search Optimization Report – 2025 Edition

Most popular locations of respondents:

• United States of America: 61
• United Kingdom: 30
• Germany: 13
• India: 13
• Spain: 13
• Australia: 9
• Canada: 7
• Italy: 7
• France: 6
• Ireland: 4
• Austria: 3
• Brazil: 3
• Mexico: 3
• New Zealand: 3
• Romania: 3
• Belgium: 2
• Netherlands: 2
• Poland: 2
• South Africa: 2
• Sweden: 2
• Switzerland: 2
• Vietnam: 2

About these results SEO Consultants say:

• Kevin Indig, Growth Advisor and Author of Growth Memo:
  “+90% of participants say they were asked about the company’s AI Visibility over the last year. AI is a topic for everyone. And the weakest answer you can give is “just do good SEO” – even if that might be true! I think this is an opportunity to get SEO tickets done that collected dust in the backlog for years, maybe even getting bigger budgets.”
• Orit Mutznik, Head of Organic Channels at Farfetch: 
  “Absolutely. I don’t know any senior SEO leader in any vertical who has not been asked about how their brand can get surfaced on AI search. The role of the in-house (and agency) SEO in this new era of AI search is to align expectations with the senior leadership team and come up with a plan that balances AI-search readiness whilst not losing focus on core SEO initiatives, that according to this survey still constitute over 95% of the ROI of SEO, and probably the same percentage describes the overlap with “traditional” SEO best practices. Given that the percentage of AI search traffic and revenue is still up to 5% (or much less) for the website, it is important to keep a rapport of AI search trends with leadership, focus on attribution, keep an open mind on continuous testing on AI search, whilst continuing with showcasing growth from initiatives related to both AI and traditional search”

About these results, SEO specialists say: 

• Magdalena Baciu, Founder & AI Search Optimization Strategist at On Target AI:
  “Most marketers I know call it “AI Search Optimisation” as well. It’s simple, straightforward, and easy to understand. And that’s even more important for decision makers who are not involved in the day-to-day activities. To me, this is the term that makes the most sense.”
• Clara Soteras, SEO and Digital Strategy for News Publishers Consultant:
  “I’m really surprised by how clients are referring to AI optimization, often just calling it “AI search optimization” or even “SEO for AI platforms.” The reality is that terms like GEO, AEO, or LLMO have largely been invented by the industry itself for marketing purposes. This risks confusing clients into thinking that all the optimization done until now is no longer relevant—which is not the case. In fact, many professionals in the field, myself included, believe the real focus should be on strengthening brand presence and becoming recognized as a trusted authority.”

About these results, SEO specialists say: 

• Jono Alderson, technical SEO Consultant:
  “It’s striking that 75% of companies put AI search under the SEO team, with almost nobody giving it dedicated, cross-disciplinary ownership. That frames this as a tactical extension of SEO (about markup, mentions, visibility), when in reality AI systems are reading the whole ecosystem: press, social, reviews, behaviour. If we treat this as just another ticket on the SEO backlog, we miss the point. AI search optimization needs executive-level stewardship that connects SEO, PR, brand, content, and product into a single, governed strategy.”
• Clara Soteras, SEO and Digital Strategy for News Publishers Consultant: 
  “As we can see, 75% of the responses indicate that SEO teams are considered responsible for AI Optimization within companies. SEOs have always been the ones expected to bring innovation and lead change, and this highlights that SEO goes far beyond pure optimization. It’s about business, strategy and brand.”

About these results, Gianluca Fiorelli, International SEO Consultant, says: 

“The results of the poll confirm that the revenue impact of AI Search is still very small in the vast majority of the cases. A good reminder that SEOs should not abandon and stopping to put all their attention to classic search, while working on optimizing and improving the websites’ visibility for the new search surfaces.”

About these answers, Lily Ray, VP of SEO Strategy and Research at Amsive, says: 

“We have launched an “AEO” (answer engine optimization) offering at Amsive for clients who want extra reporting, insights, strategy and new tactics to specifically boost AI search visibility. While many approaches are similar to our existing SEO offering, the tooling, competitive landscape, reporting and insights have evolved.”

About the results, Metehan Yesilyurt, Chief Growth Officer at AEO Vision, says:

“The survey results highlight that 35% already have AI search optimization strategies in place for most sites, and 33% are actively planning to develop them, showing strong momentum in this area. From my side, yes, I’ve built dedicated strategies for most of the brands I work with, structured around three core pillars: Citation Engineering, Entity & Embedding Coverage, and AI-Friendly Content Workflows, ensuring consistent visibility across AI search engines.”

Key Themes

• Schema & structured data (most frequent mention).
• Digital PR & citations (Reddit, Wikipedia, UGC, listicles, directories).
• Content restructuring for retrieval (chunking, TL;DRs, FAQs, Q&A).
• Tracking & measurement (AI visibility tools, GA4 setups).
• Brand building & authority (EEAT, cross-platform presence).
• Technical accessibility (bot access, CSR/JS audits, Core Web Vitals).
• Emerging GEO practices (query fan-out, retrievability scoring, semantic triples).
• Split in mindset → proactive adopters vs “stick to SEO fundamentals.”

1. Technical SEO & Crawlability for LLMs

• Ensuring bot/LLM access (robots.txt, llms.txt, crawl logs, JS audits).
• Reducing reliance on CSR, improving server performance, Core Web Vitals, TTFB.
• Auditing crawlability and indexability specifically for AI agents.
• Chunking content for retrieval and summarization.
• Fan-out simulation and clustering tests.
• Semantic HTML and interpretability checks.

2. Schema, Structured Data & Entity Optimization

• Heavy emphasis on schema (Organization, FAQ, Product, HowTo, ItemList, Local Business, etc.).
• Strengthening entity clarity, triples, and semantic completeness.
• Optimizing for Knowledge Graphs and entity-driven connections.
• Expanding structured data to all content types, FAQs, reviews, and local pages.
• Increasing adoption of semantic clustering, entity mapping, and topical hubs.

3. Content Structure & Formats for AI Retrieval

• Direct answer formats: TL;DR sections, FAQs, Q&A, summaries, bullet points.
• Breaking content into smaller, AI-digestible sections.
• Query fan-out coverage (breadth and depth of related questions).
• Reorganizing content pillars, topical maps, and glossary/listicle style content.
• Testing semantic triples, answer-first design, and passage-level optimization.
• Multimodal content (video, tables, timelines, graphics, etc.).

4. Brand Mentions, Citations & Authority Building

• Strong push on Digital PR and authoritative media placements.
• Focusing on mentions in platforms commonly cited by LLMs: Reddit, Wikipedia, LinkedIn, YouTube, listicles, directories (Yelp, TripAdvisor, BBB, Angi’s, etc.).
• Diversifying reviews across multiple platforms (not only Google).
• Targeting roundup pages, comparison articles, expert quotes, and UGC forums.
• Protecting brand presence across multiple ecosystems for consistency.
• Some even exploring buying mentions/placements on cited sources.

5. Analytics, Tracking & Measurement

• Setting up dashboards for AI search visibility (GA4 AI traffic segments, Profound, Peec.ai, BrightEdge, Ahrefs).
• Tracking citations, mentions, share of voice across ChatGPT, Perplexity, Gemini, etc.
• Analyzing which landing pages are getting AI referrals vs. not, and reverse-engineering differences.
• Defining new KPIs for AI search (citation rate, AI mentions, prompt coverage).
• Running experiments comparing traditional SEO vs AI-optimized pages.

6. Content & Brand Strategy Alignment

• Building topical authority and long-term brand visibility.
• Adjusting keyword research → query fan-out & intent coverage.
• Shifting to thematic clusters and authority signals instead of keyword chasing.
• Doubling down on EEAT (authorship, bios, recency, fact-checking, brand voice).
• Integrated strategies with PR, social, and content marketing for co-occurrence.
• Emphasis on recency + authority as signals for AI inclusion.

7. AI-Specific Experiments & Tools

• Creating GEO/AI-Search guidelines for writers.
• Testing AI citation/retrieval audits, “Language Radar” retrievability scoring.
• Prompt testing against AI platforms to see what content is surfaced.
• Adding agentic or interactive content (tools, calculators, custom chatbots).
• Prototyping new AI workflows: relevance engineering, cosine similarity, MCP/WordLift knowledge graphs.
• Testing semantic clustering, relevance scoring, and retrieval pipeline adjustments (dense + sparse hybrid).

8. Skepticism / Minimal Investment

• A significant group sees AI search as overhyped or low-value today.
• Many prioritize “getting the SEO basics right” (content, internal linking, backlinks) and see it as enough for both SEO + AI.
• Some explicitly deprioritize AI optimization, focusing only on scalability or other channels.
• A minority expressed frustration (e.g., “AI steals content, so we stopped investing in content creation”).

About these results, SEO specialists say: 

• Yagmur Simsek, Independent SEO & Content Strategist: 

  “In the context of AI search optimization, the brand and metrics area feels especially interesting right now.

  Rather than relying only on traditional rankings or traffic, I’ve been paying closer attention to how brands are being mentioned, cited, and trusted across multiple platforms. These signals seem to be playing a growing role in how AI systems surface information.

  On the metrics side, I think we’re still in the early stages of figuring out what’s most useful. Beyond impressions or CTR, things like brand citations in AI answers, share of voice in AI tools, and sentiment could become valuable indicators.

  My own priority before the year ends is to review a few more AI tools that provide metrics and actionable insights for building brand authority in this shifting landscape. I want to shortlist the ones that fit best with my projects, so I can bring more consistent reporting to clients in the new year, while still staying open to testing new tools as they emerge.”

• Dan Petrovic, Director at DEJAN: 

  “I find it really interesting there’s not even a single mention of selection rate optimization or an equivalent term as a successor to click-through rate.

  We, at DEJAN are obsessing over it and testing aggressively to understand what makes a model click… or I should say, *select* as they don’t click.

  So basically if we have 20 grounding choices the model will use internal bias during its decision making process. Some sites will be filtered out as seen irrelevant and some will be included. This largely depends on model’s primary bias which can be seen once grounding is turned off.”

About this result, Bengü Sarıca Dinçer, SEO Manager of Designmodo says: 

“We keep hearing the old “SEO is dead” line popping up again. Yet, this data shows a clear recognition that SEO is evolving and integrating AI. What I really like is seeing many clients, managers, and decision-makers think the same. Budgets are actually shifting toward this future, treating AI as a way to make SEO smarter, not replace it. Honestly, it feels validating because it’s exactly what I’ve been saying for months: SEO isn’t gone, it’s just expanding its toolkit by infusing AI.”

About this result, Gerry White, SEO & Growth Consultant, says: 

“All businesses will need to continue investing in ‘good SEO’ now is not the time to take your foot off the SEO campaigns, however the activities might shift slightly to more classical ‘house keeping’ as well as optimisation of content on and off site, reporting and research is the most challenging aspect right now, we can’t easily understand as much about user behaviour as we’ve been able to see in the past.”

About these results, Gus Pelogia, Sr. SEO and AI Product Manager at Indeed, says: 

The way to understand how AI surfaces are using our pages will rely a lot on AI bots. Large prompt databases are great for understanding sentiment, but if you want to analyse if your content is being used, then AI bots should give a better view than tracking prompts.

I know SEOs have a hard time getting access to log files, but this seems to be changing according to the survey. The majority of answers say that SEO professionals are using them for some or all of their websites. This could also decrease our dependency on third-party tools.

About this result, Gerry White, SEO & Growth Consultant, says: 

“Benchmarking is a challenge, especially if you have an ambiguous brand, using tools like Sistrix to see mentions is a fantastic start, but if your brand name is something such as Apple which can be a fruit or a brand, it is very challenging to differentiate, especially if (unlike Apple) your brand name overlaps with the intent, this is common on many many brands.”

The top mentioned tools are:

• Ahrefs (73 mentions)
• Google Analytics 4 (64)
• Semrush (61)
• Google Search Console (29)
• SE Ranking (22)
• ChatGPT (20)
• Profound (14)
• Screaming Frog (11)
• Google Gemini (9)
• Sistrix (9)
• Looker Studio (8)
• Peec.ai (8)
• Gumshoe (6)
• Surfer SEO (6)
• Adobe Analytics (5)
• Botify (5)
• BrightEdge (5)
• Otterly (5)
• Ziptie (5)
• DataForSEO (4)
• Plausible (4)
• Similarweb (4)
• Trakkr (4)
• Waikay (4)
• Advanced Web Ranking (3)
• Frase (3)
• Rankscale (3)
• SiteGuru (3)
• seoClarity (3)
• Authoritas (2)
• Conductor (2)
• GDDash (2)
• Hall (2)
• Make (2)
• Pi Datametrics (2)
• Promptwatch (2)
• SEOGets (2)
• SEOmonitor (2)
• SERPRecon (2)

Key Takeaways

• Visibility & citations is the most consistent near-term goal.
• Traffic & conversions is the ultimate business outcome, but fewer have concrete targets yet.
• Brand awareness & authority is seen as both a driver of visibility and an outcome in itself.
• KPIs and measurement frameworks are still evolving — many are experimenting.
• A non-trivial share are skeptical or in wait-and-see mode.

1. Visibility & Citations

• Most frequent theme.
• Goal: ensure brands/products are cited, mentioned, or included in AI Overviews / LLM answers.
• Sub-goals include:
  • Visibility for specific prompts or high-intent queries.
  • Increased share of voice vs competitors.
  • Being listed among top sources in AI answers.
  • Sustained visibility across multiple AI platforms (ChatGPT, Gemini, Perplexity, Claude).
• Representative phrasing: “Increase visibility in LLMs,” “Get more mentions/citations,” “Be included as the authoritative source.”

2. Traffic & Conversions

• Goal: turn AI search visibility into site visits, leads, and revenue.
• Outcomes defined as:
  • More referrals from AI search tools.
  • Improved lead gen & conversion rates (some note AI leads convert higher than SEO leads).
  • Revenue attribution from AI traffic.
  • Replacing lost organic search traffic with AI-driven visibility.
• Representative phrasing: “Drive more sales,” “Increase site visits and conversions from AI platforms,” “50% growth in AI traffic and 20 qualified leads.”

3. Brand Awareness, Authority & Sentiment

• Goal: strengthen brand reputation and trust signals within AI ecosystems.
• Outcomes:
  • Improved brand sentiment.
  • Recognition as a trusted or expert source.
  • Becoming the go-to authority in the niche.
  • Building long-term brand choice preference when AI presents options.
• Representative phrasing: “Increase brand relevance and sentiment,” “Best sentiment score among competitors,” “Thought leadership.”

4. Competitive Advantage

• Goal: stay ahead of or beat competitors in AI search.
• Includes:
  • Higher visibility than rivals in AI answers.
  • Benchmarking share of voice and mentions against competition.
  • Early mover advantage (“be the first in our industry to achieve AI visibility”).
• Representative phrasing: “Stay ahead of the competition,” “Gain greater share of mentions vs competitors.”

5. Readiness & Future-proofing

• Goal: be prepared as AI search grows, even if traffic value is small today.
• Includes:
  • Becoming “AI-search ready.”
  • Monitoring activity to understand footprint.
  • Establishing methodology and KPIs (share of voice, citations, brand accuracy).
  • Learning and testing while the landscape evolves.
• Representative phrasing: “Test and learn,” “Do basics and monitor,” “Ensure readiness for increased AI usage.”

6. Measurement & KPIs

• Goal: establish clear tracking and metrics for AI search efforts.
• Common KPIs:
  • Number of citations / mentions.
  • Share of voice vs competitors.
  • Sentiment score.
  • Visibility percentage in AI answers.
  • GA4-tracked traffic & conversions from AI referrers.
• Representative phrasing: “New KPIs and leads,” “Measure AI traffic attribution monthly,” “Percentage increases in visibility and share of sentiment.”

7. Skepticism / No Clear Goals

• A meaningful minority expressed no agreed goals yet.
• Reasons:
  • Too early, too little business value.
  • Lack of tools or reliable measurement.
  • Leadership confusion or low budgets.
  • Some are only “watching” or “waiting for the dust to settle.”
• Representative phrasing: “None agreed currently,” “No plan yet,” “Still discussing.”

About these results, Alex Moss, Principal SEO at Yoast and Co-Founder & Director at FireCask, says: 

“Something that I noticed here is that the emergence of AI visibility brings concern and anxiety for a lot of SEOs. Yes, the current shift of the landscape is at its most dramatic in a generation – but this should not worry us but instead, as Search marketers, excite us all. The expertise we have all collected over the years will only add more value to wider business objectives as people at board level will take these skills more seriously for us to lead.”

Key Takeaways

• Measurement is the bottleneck. The absence of trusted visibility/attribution frameworks is the #1 blocker.
• Volatility is real. Personalization and fast model shifts make benchmarking and forecasting hard.
• Money + momentum. Tool costs and scarce resources stall action, especially for SMBs.
• Education first. Winning buy-in requires reframing KPIs and resetting expectations beyond “rankings.”
• Playbooks emerging. Teams want evidence-backed tactics that integrate SEO fundamentals with GEO specifics.

1) Measurement, Tracking & Attribution (most cited)

• No reliable way to measure AI visibility, traffic, or conversions; volatile/ephemeral citations (“citation drift”).
• Lack of standardized KPIs, benchmarks, or historical baselines; difficulty forecasting.
• Limited/refused data from AI platforms; incomplete referrers; tool output uncertainty/hallucination risk.
• GA4 setup gaps; server-log access issues; last-click models misalign with AI discovery.
• “Are we visible for the same prompts users see?” (personalization/variance breaks comparability).

2) Data Transparency & Platform Variability

• Black-box behavior of AIO/LLMs; no ranking analog; inconsistent results by user, login state, history, location.
• Rapid model changes; moving goalposts; low signal-to-noise; contradictory public guidance.
• Different engines (ChatGPT, Gemini, Perplexity) behave differently; “AI Mode” changed the baseline.

3) Budget, Tooling & Resourcing

• Cost of AI visibility/monitoring tools; uncertain ROI → procurement pushback.
• Limited headcount/time; competing priorities; approval bottlenecks.
• Small-business constraints; clients expect results without funding.

4) Stakeholder Education, Buy-in & Expectation Management

• Leadership confusion, hype fatigue, “shiny object syndrome,” or skepticism.
• Misaligned expectations (still think in keywords/rankings; want certainty the channel can’t provide yet).
• Need to reframe success (share of voice, citations, qualified leads) vs legacy pageview metrics.

5) Strategy Clarity & Know-How

• Unclear what truly moves the needle (content vs schema vs entities vs offsite).
• Lack of trusted playbooks, reliable case studies; heavy “test & learn.”
• Balancing SEO fundamentals with GEO/AIO specifics; integrating with PR/social/UGC.

6) Low/Unpredictable Business Impact Today

• Low AI referral traffic in many niches; hard to justify investment.
• Dominance of large brands/UGC (e.g., Reddit) crowding out smaller sites.
• YMYL/regulated categories need expensive experts/PR to build trust.

7) Org/Process Friction

• Prioritization challenges; slow implementation; cross-team coordination (PR, social, product).
• Scaling approvals; content ops not set up for chunking/FAQ/entity work.
• Offsite gaps (few directory/review/citation specialists).

8) Ethical/Brand & Market Risks

• Concerns about AI’s environmental impact and content homogenization.
• IP protection vs openness (how much to expose to bots); partial content restriction.
• Fear of undermining quality/human-centric content to chase machine retrievability.

9) Market/External Constraints

• Products too new to appear in training data; regional rollout gaps for AI features.
• EMD legacy issues; need to re-establish brand legitimacy.
• Competitive paid inclusion and “who to trust” in a noisy vendor landscape.

About these results, Chris Green, Technical Director and Senior Consultant at Torque, says: 

“The reporting challenge for AI search has two key gaps we have to bridge, the first – tools – is one which will develop quickly, but you need to be ready/open to the fact the tools we’re using now aren’t likely to be the tools we’re using tomorrow. This will be quite disruptive for many businesses, but we have to accept that this area is still in its infancy.

The second, and larger problem by far, is that of attribution. SEO has never been great at winning attributions from other channels, yet still most businesses still expect the reporting to be detailed and complete because it is digital. In the immediate short term, we need to be ready to re-educate around expectations as attribution to clicks (not even revenue) IS going to get worse. Part of this is how we put in a compelling case that fewer clicks does not mean that the channel is failing.”