# Surfaced — SEO & AI Search Advisory SEO and AI search advisory from Andrew Riker. Improve website and brand visibility with clear priorities, practical strategy, and guidance for your team. Founder: Andrew Riker (Founder, Surfaced). Contact: andrew@getsurfaced.co. Newsletter: https://newsletter.getsurfaced.co/ --- # Services ## Explorer — Find the gaps. Set the priorities. URL: https://www.getsurfaced.co/services/explorer For teams that need a clearer picture of their SEO and AI search opportunities. I assess your current visibility, website and content foundations, and competitive landscape, then recommend where to focus first. A useful starting point when: You need a clearer diagnosis before deciding what deserves attention and investment. Deliverables: - SEO and AI search visibility assessment - Customer questions and competitor research - Technical accessibility and content gap review - Prioritized recommendations with supporting evidence We start with what your business wants to achieve and how your customers look for information. I review the search experiences that matter in your category alongside your website, content, and available performance data. The assessment looks for meaningful gaps: where you’re missing relevant visibility, where content falls short of customer questions, and where technical foundations may be limiting discovery. You leave with a prioritized set of recommendations, the evidence behind them, and a clear explanation of the trade-offs. Outcomes: - A baseline for the agreed search surfaces and customer questions - An assessment of important visibility, content, and technical gaps - Recommendations ranked by likely value, effort, and confidence ## Architect — Build a plan your team can put to work. URL: https://www.getsurfaced.co/services/architect For teams ready to turn their findings into a coordinated search program. We connect business goals with content priorities, measurement, ownership, and a roadmap your team can realistically deliver. A useful starting point when: You understand the situation and need a coordinated strategy your team can realistically deliver. Deliverables: - SEO and AI search strategy - Content priorities and optimization guidance - Measurement framework and reporting recommendations - Team playbooks, workshops, and enablement We turn your business goals and available research into a search strategy. Existing audits and reporting help establish the starting point. Together, we define which opportunities deserve investment, what your content needs to accomplish, and how to measure progress. We also identify owners and dependencies so the plan fits how your organization works. Workshops and practical guidance help your team understand the decisions and apply them as new questions come up. Outcomes: - A roadmap tied to business priorities - A content and optimization plan - A measurement framework with clear limitations - Shared guidance for the people doing and approving the work ## Embedded — A forward-deployed search advisor inside your team. URL: https://www.getsurfaced.co/services/embedded For businesses that need ongoing guidance as priorities evolve. I work with your team to review performance, shape decisions, plan tests, and help stakeholders understand the work. A useful starting point when: You need experienced guidance as priorities, performance, and the search landscape change. Deliverables: - Regular strategy sessions and agreed support between meetings - Roadmap review and prioritization - Test planning and performance interpretation - Team coaching and leadership communication I join your planning rhythm with an agreed cadence for strategy sessions and support between meetings. We review results, work through decisions, and adjust priorities as your business and the search landscape change. That can include evaluating recommendations, planning tests, interpreting performance, or preparing the case for a new investment. The aim is to give your team experienced guidance and build its confidence in making search decisions over time. Outcomes: - Senior input into ongoing search decisions - Regular review of priorities, tests, and performance - Clearer communication between leadership and delivery teams - Stronger internal search knowledge ## The process - Understand: Assess your business goals, audience behavior, current search performance, and the team and processes responsible for the work. - Prioritize: Agree on what to tackle now, what needs testing, and what belongs further down the roadmap. - Measure: Establish a baseline and connect progress to the outcomes that matter to your business. - Align: Help the people approving and doing the work understand the priorities, evidence, and next steps. ## Common service questions Q: What do you mean by SEO and AI search visibility? A: How your website and brand appear when people look for relevant information through search engines and AI tools. We examine the search experiences that matter to your audience, including results, answers, brand mentions, and links to your content. Q: How does this relate to our existing SEO work? A: We build from your existing program. Technical accessibility, useful content, and understanding your audience remain important. We also examine how AI search presents your business and which additional research, content, or measurement your team needs. Q: How do you measure progress? A: We agree on measures that fit your goals and available data. These can include relevant search visibility, brand representation in a defined set of AI answers, qualified visits, and conversions. AI visibility tracking is directional, so we document what we’re measuring and where attribution is incomplete. Q: Do you work with our existing agency? A: Yes. I work alongside internal teams and agency partners to clarify priorities, evaluate opportunities, and connect the work to business goals. Q: Who handles implementation? A: Your internal team or delivery partners own implementation. My role covers assessment, strategy, prioritization, measurement guidance, and enablement, with responsibilities agreed in the scope. Q: Which engagement should we start with? A: That depends on what you already know and where you need support. Explorer helps establish the situation, Architect develops the plan, and Embedded provides ongoing advisory. We can tailor the scope around existing work. --- # Writing ## Your AI visibility score starts with the prompts you choose URL: https://www.getsurfaced.co/blog/prompt-composition-ai-visibility Published: 2026-10-26 · Topic: AI Search · ~9 min read Before a visibility score tells you anything useful, you need to know what the prompts measure. How prompt mix shapes AI search scores and what to choose instead. I came across a clip of Gaetano Nino DiNardi and Ross Simmonds talking about the difference between being visible and being recommended in AI search. It reminds me how I approach the question when clients ask me how I choose prompts. What went into the prompts? Are they branded or non-branded? Informational? Comparisons? Specific pain points? Who are they supposed to represent, and how are they weighted? It can all get pretty complicated and fuzzy. Because before a visibility score can tell you anything useful about your business, you need to understand what you've asked it to measure. You're setting the goalposts in this phase! My take: prompt composition is one of the most consequential strategy decisions in an AI search program (operations I'd argue is more important, though). It determines which parts of the buying process you're watching. And when that score becomes the number leadership cares about, it starts influencing what your team writes, fixes, funds, and reports. That's a lot of responsibility for a prompt list someone might have accepted during onboarding. Sometimes auto-generated prompts is OK for directional use, but if you want any sort of rigor, it requires a bit more. Gaetano has made this practical in his own writing about tracking a guide to non-human identity management. He described an informational question where none of the brands in the sample were being recommended. His point was that a dashboard could make the brand's absence look like a performance problem, even though the answer wasn't recommending vendors at all. I love his takes. If you're asking for an explanation, a useful answer may simply explain the thing. Judging that answer on whether it recommends your product needs a pretty good justification. Imagine asking everyone walking into a Target whether they're ready to buy a refrigerator. You'll get a different result if you ask the people already standing in the appliance department. Neither group represents everyone in the building, and that's the whole shooting match. Your prompt set makes a similar choice. Consider these illustrative questions for a CRM company: | Prompt | What I'd use it to assess | | How does lead routing work? | Educational presence and useful source citations | | How can a small sales team stop losing inbound leads during handoffs? | Presence around a problem the product addresses | | Which CRMs support lead routing for a 20-person sales team? | Inclusion and fit in a relevant shortlist | | How does Brand A compare with Brand B on routing and implementation? | Competitive positioning and stated tradeoffs | | Does Brand A support our existing integrations? | Accuracy when someone is already evaluating the brand | Those are different measurement jobs. Branded versus non-branded is also a separate dimension from intent: a comparison can name your brand or ask the system to discover alternatives for you. Roll everything into one score and you lose the distinction between being used as a source, being introduced to a buyer, and being described accurately after the buyer already knows your name. There's also a difference between a brand-unaware buyer and a brand-aware buyer. Someone can understand their problem without knowing your company exists. A non-branded prompt lets you see whether your brand enters that person's consideration set. A branded prompt helps you examine what happens once it's there. ![A buyer compares a new product card with an existing shortlist as new requirements come to mind.]() And that consideration set keeps changing as the person shopping finds out more and more information. A buyer starts with a problem, discovers a few logos, learns about features they hadn't considered, and asks a different question. A new competitor joins the shortlist. A product that looked ideal turns out to be missing something important. It's always changing! The same person can move from “What could help us do this?” to “Which of these three products supports our workflow?” within one conversation and with attribution being pretty horrible you will have no idea that has taken place. Your prompt library should reflect those changing decisions, including follow-up questions, rather than treating awareness as a permanent label attached to a person. ### The number can improve without performance improving Here's a hypothetical example using a simple mention rate, with equal weight per prompt and the same number of runs for each (note: this isn't a vendor benchmark and everyone does something differently). Suppose your brand appears in 80% of answers to branded questions and 20% of answers to non-branded questions. With a library that's 20% branded and 80% non-branded, the combined mention rate is 32%. Change the library to 80% branded and 20% non-branded, and the combined rate becomes 68%. Performance within each group hasn't changed but the mix totally has...and you now have a much happier dashboard and no evidence that more buyers are discovering you. Branded prompts still deserve a place. They're particularly useful for examining competitive share of mention or voice within a defined comparison set, and they're a necessary part of how I'd assess brand sentiment, accuracy, and reputation. You want to know whether AI gets your pricing, capabilities, positioning, and limitations right, and what concerns it associates with your name. Sentiment also deserves attention when your brand appears in non-branded answers. Keep those measures distinct. An answer can sound positive and still get your product wrong. And if you've named three vendors in a comparison prompt, their appearance isn't evidence of spontaneous discovery. Use consistent prompts and a clear definition of share of voice to examine how each is represented, then read the actual recommendations and tradeoffs. ### Choose prompts around the decision you're trying to influence If the business priority is qualified demos for a particular product, I'd put more measurement attention on the problems, requirements, comparisons, and objections that come up while people evaluate that product. That gives you a more specific hypothesis to test. Improve the evidence available for those decisions, watch how the answers change, and look for corresponding signals in qualified inquiries and sales conversations. A library dominated by broad educational questions tells you less about that particular objective cause it's all fuzzy and general. Educational content deserves serious investment, especially when you're creating a category. My take is that the AI era creates a huge opportunity here. Buyers are trying to understand unfamiliar capabilities, figure out what problems they solve, and decide which features even matter. You have an opportunity to help shape those evaluation criteria before anyone has a vendor shortlist. Support documentation belongs in this conversation too. During my time at Intuit, support content was the largest cited area I witnessed. That's my observation from that work, not a universal benchmark. It informs why I see documentation becoming increasingly important to businesses: it contains concrete answers about what a product does, how it works, and where people get stuck. Those answers can serve existing customers and help prospective buyers assess fit. “How do I connect this to our existing system?” can be a support question or a buying question. The wording alone won't always tell you which. Demand generation includes much more than informational search, and education needs measures appropriate to its role. An answer that satisfies a question without a click isn't automatically worthless, and a vendor recommendation isn't automatically a sale. Longer prompts aren't automatically more commercial, either. A detailed research question can carry no buying intent and short pricing question can carry plenty. That also means knowing whose decision you're studying. After Similarweb's Future of Search event, I wrote about matching content to the platforms an audience uses and the problem that person has now. That same thinking belongs in prompt selection. A small business owner and an enterprise procurement lead can ask about the same product category and need very different answers. Team size, geography, integrations, budget, and implementation constraints can all be relevant. Include the context you have evidence for. Don't invent an elaborate buyer persona just to make the answer favor you ;) Aleyda Solís makes a related point in her post about the limits of individual prompt tracking: answers are dynamic, personalized, and context-dependent. She recommends looking across topics, use cases, and audience intent rather than obsessing over isolated prompt inclusions. She's an excellent follow. ### Your context layer is a treasure trove for prompt research ![Two colleagues uncover customer conversations, notebooks, and question cards in a treasure chest.]() This is where the context layer I've talked about becomes useful. In my writing about extracting expertise through voice, I described it as the knowledge you have that other people can't easily replicate. For prompt research, that means bringing together what you know about the business, its products, its differentiation, and its customers. Product marketing research. Customer interviews. Sales calls. Support tickets. Surveys. Community discussions. The questions people ask in a webinar after the prepared slides are over. It's a treasure trove of information for crafting prompts because it contains the language people use when they're confused, comparing options, dealing with a constraint, or trying to make something work. It also contains the reasons they chose you, rejected you, or started looking for an alternative (RIP). I'd mine that material for recurring questions and phrases, connect them to the buyer and the decision, and use site search and search data to investigate further. Then build prompt groups around those patterns. Use generated prompts to expand the research, and have someone who knows the customer review the result. The context layer gives the prompt list a reason to exist beyond whatever a tool inferred from your homepage. Those inputs still inform a sample; they don't establish how frequently each prompt is used inside an AI platform. ### Balanced needs a definition We're all figuring this out together. What I recommend will no doubt change as models change and we get better data...at least this is my current way of thinking about it. An equal split across funnel stages isn't automatically balanced for your business. Neither is a list containing every variation of “best” plus your category. I'd keep discovery, problem-solving, evaluation, and branded accuracy visible as separate groups. Then decide which groups deserve more attention based on the program's objective. If you weight them into an executive score, show the weights. A score weighted toward commercial priorities is a management choice. Calling it a measure of total market visibility would require evidence about actual audience demand that the weighting alone doesn't provide. Prompt counts create weighting even when nobody explicitly sets weights. Fifty versions of one comparison can overwhelm five questions about another product. Keep a stable core for trend reporting, and put exploratory prompts in a separate group. When the core changes, record the change and establish a comparable baseline. Apply the same discipline to platform, location, model where known, and testing conditions. Repeat the sampling; one answer isn't a durable result. A simulated persona or clean-session API test also won't reproduce every real user's conversation history. And if this feels overwhelming, I get it, and it's kind of because it is cause we're all figuring out this together in real time. You don't need to feel certain that you've assembled the perfectly curated prompt list. Your measurement will change as you learn. Know your units of measurement: mentions per sampled answer, citations, recommendations, or a defined competitive share. Know which prompts and surfaces those numbers cover, and what performance you're building toward. That's a useful place to start, with room to improve. ### Recommendations still need a business test Wil Reynolds asks the question that belongs alongside every visibility chart: what happened for the business? In his SEO Week interview, he calls for testing whether visibility on relevant prompts corresponds with branded search, service-page traffic, and leads, while checking for other explanations. That's where I'd take this next. Keep mentions, citations, and actual recommendations separate. Read whether the recommendation fits the buyer's stated need, including any caveats. Then examine identifiable AI referrals, qualified demos, purchases, and what buyers tell sales about how they found you. Branded search and direct traffic can add context, but movement there doesn't establish that AI caused it. Even a tracked AI referral doesn't reveal the full influence of earlier interactions. Answer-first content for a specific buying question gives you a focused intervention to test. Clear comparisons, credible evidence, accurate product details, and useful implementation guidance can address the uncertainty behind that question. The test still has to establish whether the answers changed and whether there is evidence of a commercial contribution. Publishing more pages and watching a score rise doesn't settle either question. For the next reporting meeting, bring the prompt list beside the chart. Show which buyer decisions it covers, how the categories are weighted, and what changed since the previous report. Before that score becomes your North Star, could you explain why those are the questions your business should be guided by? I hope so, and maybe I helped. Onwards! ## AI search measurement: readiness, visibility, and revenue URL: https://www.getsurfaced.co/blog/ai-search-measurement Published: 2026-10-15 · Topic: AI Search · ~12 min read Measure AI search across readiness, competitive visibility, and revenue evidence. A practical approach for marketing leaders deciding where to focus. A client recently asked me whether they should focus their AI search effort on the top, middle, or bottom of the funnel. My recommendation was to start with the middle and bottom. When revenue is the priority, I want to understand the questions people ask when they are evaluating an approach, comparing providers, or working out whether a purchase makes sense. I also want the problem-aware questions that connect closely to what the company actually sells. That question gets at something bigger than which prompts to track. Before we measure AI search, we need to agree on what we want the work to accomplish. A dashboard can help us see what is happening. It cannot choose our priorities, get a subject matter expert to review the content, or convince sales to capture what buyers are telling them. Measurement supports strategy and execution. There is still a lot of work between opening the report and improving performance. I break that measurement into three layers: AI readiness, competitive visibility, and revenue attribution and influence. Each gives us a different kind of evidence. Together, they help us decide where to put our effort and explain what we know with some honesty. ![Three questions, different evidence: readiness checks access and understanding; visibility checks sampled answers; business outcomes connect evidence to pipeline and revenue.]() ### Start with the buying conversations you want to influence Broad informational content has a tougher job when someone can get a sufficient answer without visiting a website. Pew Research Center's analysis of browsing activity from 900 U.S. adults in March 2025 found that users clicked traditional search results on 8% of visits with an AI summary, compared with 15% without one. That is evidence of a click-through gap in that Google sample, rather than a forecast for every business or platform. Pew also reconstructed search results in April, so the results it collected could differ from what participants originally saw. [1] For me, that reinforces the need to be selective. If the team has limited capacity and needs to support pipeline, I would prioritize questions with a clear connection to a buying decision. Take a hypothetical managed IT provider. "What are managed IT services?" is a different opportunity from "Should we outsource IT support or hire another internal team member?" The second person has a problem to solve, even if they have never heard of your company. Questions about transition risk, service coverage, cost, and provider fit move that conversation further along. Broad education still has a role in building familiarity and explaining unfamiliar categories. My recommendation is about sequencing the work. Cover the questions that help a relevant buyer move forward, then expand with purpose. ### Layer one: can they come through the front door? AI readiness starts with a simple question: can the relevant systems access your website and retrieve useful content? I want to know whether important pages are accessible, whether the main copy is readable, and whether security controls are getting in the way. I also want to understand which crawlers we are talking about. OpenAI, for example, distinguishes its search crawler, OAI-SearchBot, from GPTBot, which relates to model training. Those are separate controls, so "we allow AI bots" needs a little more detail. [2] Then I look at the content itself. Is it clear what the company does, who it helps, and where its expertise sits? Does the site answer the questions buyers actually have? Does it provide evidence for its claims? Content and embedding analysis can help identify related topics, thin coverage, and gaps. I use that analysis as a diagnostic. It does not expose a secret topical authority score inside ChatGPT, and similarity between pages does not establish that the content is accurate or convincing. Someone still needs to read it. For our hypothetical IT provider, this could mean discovering that the site has plenty of general technology articles but very little about switching providers without disrupting operations. That is a useful finding because it gives the team something specific to investigate and improve. The readiness report should show the important barriers, the pages affected, and who owns the fix. A rising audit score is encouraging. The practical value is knowing which obstacles you have removed and which ones still need attention. #### Readiness metrics worth considering - Page accessibility: The number or percentage of priority pages the relevant AI search crawlers can access. - Content coverage: The number or percentage of important buyer questions your content answers usefully. - Information accuracy: The number or percentage of reviewed pages with accurate, current information. Define the priority pages and buyer questions first, and keep the review criteria consistent. Otherwise, a better score can simply mean you changed the checklist. Removing an access barrier gives the content a chance to be used; it does not establish demand. The three views below belong together. Each supports a different decision, and none guarantees the next outcome. ### Layer two: where do we appear in the conversation? Once we have looked at readiness, I want to understand our position against competitors in the answers buyers could encounter. We can track this while the team works on the site; there is no need to wait for a perfect audit score. This is where prompt measurement helps. Build a set of questions around the problems, comparisons, and decisions that matter to your audience. Sales calls, customer interviews, objections, and existing search data are useful inputs. A list generated entirely from what we wish buyers were asking deserves a second look. For the IT provider, I would include questions about outsourcing versus hiring, comparing service models, and choosing a provider with experience in a specific industry. I would separate branded questions from unbranded ones so existing awareness does not get mistaken for discovery. Within that sample, we can measure how often the brand appears, its share of mentions relative to competitors, and its share of citations. We should also read the answers. Are we described accurately? Are we recommended for work we actually want? Is the citation pointing to a page that helps someone evaluate us? A mention and a citation tell us different things. A brand can be named without its website being cited. A page can be cited without the company being recommended as a provider. The measurement needs consistent definitions, too. State the prompt set, platforms, dates, and denominator behind each percentage. Repeat observations and record changes to the sample. Call it share of voice within the tracked questions, rather than treating it as a census of the market. If a competitor repeatedly appears for transition-risk questions and we do not, I want to investigate the difference. The report gives us a question worth answering. It does not prescribe the fix on its own. #### Visibility metrics worth considering - Mention rate: The percentage of sampled answers that name your brand. - Recommendation rate: The percentage that suggest your brand as a relevant option. - Citation rate: The percentage that cite your website as a source. - Share of mentions: Your brand's share of mentions across the defined set of tracked brands. - Answer accuracy: The percentage of reviewed answers about your brand that describe it correctly. State how you count repeated mentions, missing answers and failed runs. Keep the questions and testing conditions consistent, and record observation dates. These numbers describe the tracked sample, not your overall market share. #### Read the answer, especially when it is about you A report can count your name and still miss the part a buyer reads. A branded answer might repeat an old complaint, outdated product information or an inaccurate claim. Those are risks to check, not reasons to assume every answer is negative. Review brand-only questions separately from reviews, comparisons and complaints. Open the cited sources and check whether they support the answer. A legitimate complaint and a false description need different responses. One may require work on the customer experience; the other needs an accuracy investigation. Give someone responsibility for that next action. ### Layer three: what connects to pipeline and revenue? Eventually, someone will ask what the AI search investment is doing for the business. Fair question. "Look, the line went up" needs some supporting material. I want identifiable AI referral traffic and its conversions in the reporting. I also want to know what buyers tell us and what happens to those opportunities afterward. That means working with sales and RevOps. Ask how someone first heard about the company. Where it fits the conversation, ask what helped them evaluate their options. An open response can surface something a channel dropdown misses: "We asked ChatGPT for a shortlist, looked at your case studies, and then a colleague recommended you." Keep that answer with the lead or account. Preserve the buyer's words alongside any standardized category, and connect the record to opportunity stage and eventual revenue. Track how often you collect an answer, too. Five responses out of ten conversations and five out of five hundred tell very different stories. I would report directly tracked AI conversions and buyer-reported AI influence separately. If an opportunity appears in both, account for the overlap before adding anything together. Pipeline also needs to stay distinct from closed-won revenue. #### Business metrics worth considering - Identifiable AI referral visits and leads: Visits and inquiries you can trace to an AI source. - AI referral conversion rate: The percentage of those visits that become a defined conversion, such as a qualified lead. - Buyer-reported influence: Buyers who say AI helped them discover or evaluate you, alongside the percentage of buyers who answered the question. - Associated pipeline and revenue: Opportunity value and closed-won revenue linked to those signals, reported separately and deduplicated. - Estimated incremental lift: Additional business a credible comparison test suggests the work produced. Direct traffic and branded search trends can help identify patterns worth investigating. They are supporting signals, not automatic evidence of AI-attributed revenue. Pick the measures that help you decide what to fix, where to focus or whether to keep investing. Will a form field reconstruct every influence on a buying decision? I wish. It gives us another piece of evidence. A buyer saying AI helped them find us supports an influence claim. It does not prove that the sale would have disappeared without AI. Establishing incremental impact requires a stronger test, such as a credible comparison group, with the other changes and limitations made explicit. ### What happened because of the work? Incrementality asks how much extra business the work produced compared with what would have happened without it. We do not get to watch the same buyer live both versions, so the comparison has to do some serious work. ![Conceptual comparison of an observed outcome with the estimated outcome without the work. The difference is the estimated incremental effect.]() For our hypothetical IT provider, start with a specific change: improve the evidence about switching providers without disrupting operations. Agree on the business outcome and what result would justify expanding the work before the test starts. Then look for a credible comparison that can remain unchanged. Randomize where feasible. Otherwise, explain why the groups are comparable, check their prior trends, and account for other campaigns and spillover. Allow enough time for the buying cycle and enough volume to learn something. Even a credible result may show that the content helped across search and sales. Separating the AI contribution is another claim that needs its own evidence. A page that also ranks in Google and gets used by the sales team should not have all its impact assigned to AI. If the business lacks enough volume or a credible comparison, report what changed, what buyers told you and what remains uncertain. An inconclusive result is allowed. Use it to choose the next useful test. ### What about ChatGPT visits showing up as direct? This is a fair concern to investigate. Google describes direct traffic as visits without a clear referral source and lists missing traffic information among the reasons it occurs. [3] An AI-originated visit that arrives without usable source information may therefore be difficult to identify. A buyer can also discover you in an AI answer and return later by typing your address or searching your brand. But a rise in direct traffic cannot tell us how much came from AI. I would not turn that increase into an AI revenue number. If referrals change suddenly, check the links, tagging, redirects, consent behavior, and device patterns before deciding what happened. The useful response is to combine the signals we can observe, document the gaps, and keep collecting buyer evidence. There is no universal percentage of hidden AI revenue I would apply to a client's business. #### Keep a change log beside the report Record content changes, campaigns, tagging changes, prompt-set revisions and known platform changes. When a chart moves abruptly, check whether the underlying behavior changed or the collection method changed. Compare platforms and inspect the actual answers before attributing a citation drop to your content. Keep a note of reporting breaks so a tracking problem does not become a performance story by accident. For the managed IT example, that means keeping the work and the evidence connected without assuming a sale will follow. ![Illustrative managed IT example connecting a buyer question with readiness work, repeated visibility checks, and buyer-reported CRM evidence.]() ### The team creates the performance ![A team working together around a table. Measurement helps choose the work; people turn the findings into something useful.]() Imagine the reporting identifies weak visibility around switching IT providers. SEO can investigate access and content gaps. Product marketing can clarify the offer. Subject matter experts can explain the transition process. Sales can contribute the objections that come up in real conversations. RevOps can help connect those conversations to opportunities. That is why measurement needs a working relationship across the team. Someone has to own the next action, and the people doing the work need time and support to deliver it. In the next review, I want to hear what changed, what evidence followed, and what remains uncertain. If access improved but visibility stayed flat, investigate that. If visibility improved but qualified conversations did not, revisit the audience, offer, and content. A report should help us choose the next useful piece of work. For agencies, agree with the client on those responsibilities early. Be specific about what your team can deliver and where you need their technical team, experts, salespeople, or leadership involved. It makes the reporting conversation much more productive. There is no silver bullet here. Better measurement can make the hard work more focused, and it can help an internal champion explain why that work deserves attention. It still takes people to turn the findings into something a buyer finds useful. If you are trying to decide which buying questions to prioritize or what your current reporting can tell you, bring me one question your team is stuck on. I am happy to think it through with you. ### Sources 1. Pew Research Center: Google users are less likely to click on links when an AI summary appears in the results, July 22, 2025. 2. OpenAI: Overview of OpenAI crawlers, accessed October 14, 2026. 3. Google Analytics Help: Understand (direct) / (none) traffic, accessed October 14, 2026. ## Building AI Search for Enterprise, Part 2: The Biggest Opportunity Isn't Another Tool. It's Getting Everyone Moving Together. URL: https://www.getsurfaced.co/blog/building-ai-search-for-enterprise-part-2 Published: 2026-10-12 · Topic: AI Search · ~10 min read The biggest opportunity in enterprise AI search isn't another tool. It's getting everyone moving together through shared resources, clear ownership, and practical experiments. Previously I shared how I approached a global AI search research challenge for a Fortune 50 company. We needed to understand which AI platforms mattered in different markets, who was using them, and how to turn a mountain of conflicting research into something leadership could use. The result was a living, interactive research resource that teams could revisit as the market changed. But that leads to another challenge, one I've seen repeatedly throughout my career: What happens after you have the research? You can have the best insights in the world, a beautiful dashboard, and a strategy that would make any consultant shed a single tear of joy ;) None of it matters much if the organization isn't equipped to act on it, which is often very much the case from working at enterprise companies like RBC and Intuit inside, and from the outside servicing as Head of SEO at BrightEdge and agency work. And at enterprise scale, getting people aligned is often harder than figuring out what needs to be done in the first place. Trust. ### Search has spent years fighting for a seat at the table I've worked in search for more than 15 years, recently at Intuit during the early rise of generative AI search. For much of that time in the biz, search teams have worked from the ground up. We tested hypotheses, chased algorithm changes, built forecasts, connected organic performance to revenue, and made the business case for investment. Sometimes we won. Sometimes we had to explain for the 47th time why organic traffic wasn't free just because we weren't paying for every click. That experience made search professionals resourceful. It also created a familiar organizational problem. SEO became something a specialized team understood deeply, while other functions often engaged only when something needed fixing. Content teams had their calendars, product teams had their roadmaps, regional marketing teams had their own priorities, analytics had reporting requirements, legal and compliance had perfectly reasonable reasons to say no to things. Everyone was “doing their job”. But the search team was often left trying to stitch the pieces together using data to inform strategy, and generative AI has made that arrangement increasingly difficult to sustain as the company looked towards them to find out what’s going on. A brand's visibility across AI-powered discovery experiences can depend on product information, editorial content, technical accessibility, public reputation, structured data, and information published across the broader web. No single SEO team owns all of those things. And increasingly, leadership is paying attention and sending those slack messages. That's an opportunity for search professionals to build something bigger. ### The conversation has moved upstairs. The work still happens downstairs. ![Leadership sharing direction with the team doing the work]() In my experience, enterprise leaders are increasingly asking questions about AI search that go well beyond rankings….mostly because fear of what they might not know. Are our brands appearing when customers ask AI platforms for recommendations? Which competitors are showing up? Are buyers getting accurate information about our products? Are we investing in the right places, particularly across regions? Those are business questions, and they deserve business-level answers. But executive interest alone doesn't magically create operational alignment. An executive can declare AI search a priority on Monday and, by Wednesday, five departments can have five different interpretations of what that means. Activity everywhere. Coordination, nope. This is where I think enterprise search programs have one of their largest opportunities. Leadership needs to establish the direction, while search practitioners help translate that direction into repeatable work across the organization. ### So, what can we actually build? This is the part I find particularly exciting. Historically, building internal resources could require months or even quarters of coordination. You needed a developer, a project brief, an approved budget, a place to host something, and probably three meetings to discuss when to have the next meeting. Now we can prototype surprisingly useful resources with AI-assisted development tools. That doesn't mean every search marketer should be building production software. It means we have another way to solve organizational problems that previously went unaddressed because they weren't worth dedicating an engineering team to. Think about the things that would make your team's work easier if they existed, like a shared resource where everyone can see what you're learning about AI search. Or a standardized way to compare visibility across brands. A searchable repository of experiments that keeps teams from repeating the same mistakes. All game changers! These are the types of assets that can make a center of excellence useful. #### 1. A shared AI search command center Imagine a single destination where stakeholders across the company can find the information they need about your AI search program. That might include market trends, current priorities, platform research, experiments, measurement definitions, competitive findings, and links to useful resources. The goal is straightforward: someone working on a regional content strategy shouldn't need to hunt down the SEO director to understand the organization's AI search priorities. A shared resource also makes it easier to onboard new people and maintain continuity when responsibilities change. And importantly, it can be updated as the program evolves instead of becoming another presentation that slowly fossilizes in a shared drive. #### 2. A prompt and testing library Enterprise brands often have multiple teams running similar AI search experiments without realizing it. One market is testing brand visibility for product comparisons, another is evaluating how AI describes its services, and a third is looking at recommendations for specific customer segments. A shared prompt library creates some consistency. Teams can see which questions are being tested, the intent behind them, which markets and products they apply to, and how results are collected. This is particularly valuable for global organizations, where a prompt that makes sense in Boston (hometown!) may be completely irrelevant in Beijing. I'd also include guidance about the limitations of prompt-based measurement. Responses can vary by platform, location, personalization, and time. A small set of prompts is useful for directional learning, but it shouldn't be presented as a definitive view of market share or performance. This comes up a lot in my convos lately about build vs. buy, margin of error, statsig, and everything else that goes into search measurement. #### 3. A reporting prototype everyone can understand There is no shortage of AI search dashboards, I’m even getting ads for them while watching HBO. What organizations often lack is agreement on what the numbers mean and what decisions they should influence. Search visibility, citations, mentions, referral traffic, and conversions all tell different parts of the story. A lightweight reporting prototype can help teams work through those definitions before committing to a complicated implementation. For example, you could bring together: - Visibility across a defined collection of high-priority buyer questions. - Competitor comparisons and how the answers change over time. - Identifiable AI referral traffic and downstream conversions. - Content experiments, their hypotheses, and observed outcomes. - Qualitative findings from sales teams about how buyers are researching. The point is to establish a common understanding of success, not to create a giant score that nobody trusts. Once the approach is validated, you have something much more useful to hand over for enterprise-grade implementation. #### 4. An operational playbook AI search touches teams with different responsibilities, workflows, and risk tolerances. An internal playbook can help answer questions like: Who owns evaluating new AI search platforms? How do we prioritize AI-related content updates? What does legal need to review? How do local teams submit experiments or requests? Who decides whether an approach is ready to scale? And perhaps most importantly, what do we do when the answer isn't clear? Not everything needs a forty-page governance document. Sometimes a straightforward workflow, a few clear owners, and a shared decision log can solve an enormous amount of friction. #### 5. Education and enablement resources One of the most overlooked parts of building an enterprise search program is helping people understand why they should care. Your product team doesn't necessarily need to become expert in retrieval systems. Your CFO DOES NOT WANT a presentation about robots.txt directives. They do need to understand where their responsibilities connect to the broader AI search strategy. AI can help build practical, role-specific learning materials, short explainers, FAQs, interactive examples, and internal training resources. The benefit is that teams become more capable of making good decisions without the search lead needing to personally attend every meeting. That's how I, and you, build champions. ### Pick a problem first. Then decide whether AI should help solve it. I want to be careful about something here in this rather long-winded dive into the org side of search. It's very easy to get excited about building tools simply because building tools has become easier. AI makes things exciting. I've certainly fallen into that rabbit hole. You start by trying to make a small internal dashboard, and two hours later you're wondering whether your company needs a custom authentication system and a mascot. Probably not. The right place to start is with a problem your teams repeatedly encounter. Maybe five regional teams are presenting AI search performance differently. Maybe your leadership team can't connect experimentation to business priorities. Maybe you've created excellent research, but nobody knows where to find it. Start there. Then ask whether a lightweight resource could improve how the organization works. A simple way to prioritize what to build: ![A simple way to prioritize what to build: recurring problems, shared benefit, quick tests, ownership, and outcomes]() I also think it's worth making a distinction between prototypes and production systems. AI-assisted coding can be great for an internal proof of concept, educational tool, analysis interface, or lightweight reporting resource. But once you're handling sensitive company information, introducing complex integrations, or serving large numbers of users, engineering review, security controls, accessibility, data governance, etc and ongoing maintenance become untenable. Build small to learn quickly. Bring in the right technical expertise when the tool needs to become a durable part of the organization. ### A center of excellence should make everyone better at their jobs I've seen the phrase center of excellence used to describe everything from a highly effective operating model to a recurring calendar invitation with a fancy name. The version I like is much more practical. A small group establishes direction, maintains shared resources, helps teams prioritize opportunities, and brings lessons learned back into the broader program. The teams doing the work remain empowered to make decisions relevant to their products, audiences, and regions. For AI search, that might mean setting common measurement principles while allowing local teams to evaluate region-specific platforms. Or creating standardized experimentation guidelines without forcing every business unit to prioritize the same content. Centralizing knowledge shouldn't mean centralizing every decision. And ideally, the system becomes more valuable as more teams contribute to it. A successful experiment in one business unit becomes something another can learn from. A regional insight changes global priorities. A question from sales becomes the inspiration for a new content experiment. That creates a feedback loop instead of another silo. ![AI search center of excellence linking executive direction, shared resources, teams, and experiments]() The structure doesn't need to be particularly complicated. Executive leadership helps establish the priorities. A central group maintains resources, standards, and educational materials. Individual teams apply those capabilities to their own work and contribute their findings back into the program. ### What I'd do in the first 30 days If I were standing up this approach inside a large organization, I wouldn't start by building a sprawling AI search platform. I'd start with a handful of conversations and one useful asset. Week 1: Find the friction Talk to search, content, product, analytics, and a leadership sponsor. Identify where priorities, information, and ownership are breaking down. Week 2: Pick one shared problem Agree on a resource that solves something real. Define the users, the owner, and what a successful first version should accomplish. Week 3: Build and test Use existing documentation and AI-assisted tools to prototype the solution. Put it in front of actual users and see where it helps or falls short. Week 4: Improve and share Refine the resource, document ownership, and share what was learned. Decide whether to maintain it, expand it, or retire it. It's definitely not glamorous. But it's how you begin building something people will actually use. ### The opportunity is bigger than a search program One thing I find encouraging about AI search is that it's bringing familiar search challenges into conversations with people who may never have thought much about SEO before. There are a lot of new AI folks claiming a lot of BS. And suddenly, there's interest in how brands are described, how information is discovered, what influences recommendations, and how to measure visibility beyond traditional search results. Search professionals have spent years working on versions of those questions. We have an opportunity to help organizations navigate this shift, but that requires looking beyond our own channel and bringing more people into the process. The brands that make progress won't necessarily be the ones building the most sophisticated internal tools. They'll be the ones making it easier for their people to make better decisions, together. That's where I'd invest! ## Taste, artistry, and judgment URL: https://www.getsurfaced.co/blog/taste-artistry-and-judgment Published: 2026-09-07 · Topic: Point of view · ~7 min read Taste, artistry, and judgment are becoming the most valuable things you own as AI makes output effectively infinite. "AI is going to take my job," someone in tech tells their therapist. "I'm prompting myself out of a job," someone says to a colleague while pushing Claude as far as it goes, probably ending up on LinkedIn Lunatics. "Maybe I should just get out of tech." That's where a lot of smart people land after watching the cuts roll through 2026 like butter...."go to the blue collar jobs", says boomers. But, the anxiety is earned. TrueUp counted 783 tech layoff events in 2025, nearly a QUARTER MILLION PEOPLE, or about 674 a day. In 2026 the run rate is 760 a day. Big oof. You might have heard it, but taste, artistry, and judgment are about to be the most valuable things you own (along with becoming a systems thinker). Let's get into it. ### What people are reaching for As tools multiply and output goes effectively infinite, people reach for whatever still feels human. People connect with people. AI companions and Discord servers do a decent impression of connection, and they don't replace sitting across from someone you trust. In business, the marketer in me looks for the same thing in a different wrapper: - Primary customer research / Sales calls, support tickets, surveys - Information gain / Firsthand accounts. Things that actually happened to you - Proprietary data / Zero-party and first-party data that paints a picture only you can paint Search agents and LLMs are hunting for exactly this material, and it's one of the few durable ways a business stays differentiated and authoritative. But the more interesting stuff sits underneath (or within!). ### Taste Did you ever look up to someone because of their taste in movies, music, food, clothes? You took their word over a Reddit thread with 400 upvotes. You bought what they were selling. You were subscribed to what they were about. That's the influencer economy in one sentence. Parasocial bonds aren't always good for us, and they're real: people form one-sided attachments to figures they'll never meet, and those attachments get stronger when connection is thin everywhere else. The industry understood that early and built a $32 billion market on it (Influencer Marketing Hub). Underneath the commercial version, all of us run a private ledger of good and bad experiences that makes us individuals. It's close to impossible to replicate, which is why marketers settle for lookalike audiences and call it personalization. We are all running off of synthetic this or that or emulate what people are really doing (prompts, personas, etc.). That ledger is your fingerprint as a human, a user, a creator. What someone posts is their taste, dressed up a little, but still theirs and what they've lived, seen, or survived :) Taste is the collection of experiences you draw from. ### Artistry The pull to create can't be manufactured...it feels almost supernatural. It shows up as an unhinged dinner party, an unusual angle on a blog post, when people say "the quiet part out loud". Call it creativity, ye ole' left brain or right brain... it's powerful when it hits. Sticking to business: artistry colors outside the lines sometimes. Brand guidelines, voice and tone docs, design systems, those are guardrails so nobody drives into a ditch. That makes sense for companies that need control over what goes into the world. The version I keep running into looks like this. I took a discovery call this summer with a company about to launch a marketplace in a deeply unglamorous industry. No traffic, backlinks, no brand. Every playbook, human or machine, says lead with the gap analysis and sell them the climb. I led with the thing they were embarrassed about instead. Being pre-launch is an architectural advantage. Nobody has to unwind ten years of a CMS someone else built, or renegotiate a taxonomy with four internal owners, or explain to a VP why the category page they love has to go. They get to build the machine-readable version first, while their entrenched competitors pay down debt they can't see. That angle isn't in a prompt. It came from watching enterprise teams spend eighteen months undoing decisions made before they got there. You fall on your face, or see missteps, and you bake it into how you approach things in the future. I still use lessons from 2008 when I got into marketing. The decisions you make are your artistry. They come out of experience, ideas, and the willingness to break from average. It is kinda about risk, too. Research led me to find a quote from Wolfgang Messner at the University of South Carolina, where he made the point well in his piece on AI and the cognitive revolution: real creativity takes conceptual leaps, cross-disciplinary thinking, and lived experience: "AI cannot invent the future. It can only remix the past." Deep, Wolfgang. deep. AI slop is the antithesis of artistry! ### Judgment Who decides what's "best"? A model can run across most of what humans have written down and tell you, against a defined set of criteria, what the best option probably is. But do most people give it the criteria that matter? Nope. When I use AI for anything I'm creating, I make it ask me at least 10 questions first (something I find very valuable when working with AI). "What are the best running shoes?" is a different question from "what are the best trail running shoes for British Columbia in April, packed in a carry-on?" You won't get the same answer, and that's the point. AI answers what you asked with what it has. Now ask a friend whether you look better in jeans or shorts. You get access to an emotional being's bias, perception, lived experience, relationship with you, and personal sense of style. You get a mirror back. If they're good at empathy, maybe they set themselves aside and give you something closer to objective. Let me give you a live example of judgment as a rule rather than a vibe. I built a chained research pipeline out of Claude skills that turns a full day of market monitoring into about an hour of editing. The rule I hard-coded into every one of them: every finding carries a dated source link. Not because the model lies exactly, but because it will hand you a citation that looks right, reads well, and points to a journal that doesn't exist or it comes. I don't want that. I know because it happened in the first draft of this post. A confident, tidy citation to a publication nobody has ever published in. The pipeline is fast. The rule is judgment. And the rule only exists because clean-looking data has burned me before. In business, that's what judgment is, the thing someone builds through history and pedigree. I was Head of SEO at BrightEdge, led AI and Search at Intuit where LLM referral traffic grew 769% and converted about 4% better than other channels, and ran Market Insights at Brandlight, an AI visibility platform. When I say a product has bad UX, that statement has two enterprise search platforms standing behind it :) Your message should transmit that trust. It's how the top advisors and consultants got where they are. They have the wisdom, and they use it to call something good or bad, worth it or not, from a body of experience that took years to build. Pascal Bornet argues the same thing in Irreplaceable: AI can simulate human abilities, but those abilities rest on " our individual life experiences, our human emotions" and personalities that technology doesn't have. He's a great follow. ### Hard to write into a .skill file Taste is the closest to replicable, given how much it resembles building a knowledge graph of familiar entities. Artistry isn't, no matter how clever the prompt. It's more...poetic and can't be systemized. Judgment needs so much context that it's difficult to build at scale, especially when most of the variables never make it into the system in the first place. I say this as someone who has automated a lot of his own work. I've built a stack of custom skills that handle research, monitoring, audits, and reporting, and I hand the prototypes to internal teams so they can build the durable version themselves. I have no interest in being the guy who hoards the expertise...I want to build champion and championship programs with Surfaced. I also built a search maturity assessment and I refuse to turn it into a gated download. Everyone in growth marketing will tell you to make it a lead magnet, capture the email, run the nurture sequence. I'd rather people talk to me to find out where they land, because the interesting part was never the score. It's the ten minutes after, when someone says "yeah, but you don't know how our budget cycle works," and they're right, and that changes the answer. That's the part I won't automate. Not because I can't, but because the conversation is the product. As AI expands the space of possible outputs, the hard part becomes choosing well: what's valuable, what's feasible, what's worth doing at all. AI will make you feel like you're perpetually behind. It won't take your job if you remember how much of creating, building, and marketing happens outside the walls of a data center. ![]() Hope you enjoyed. ## The Value of Voice: A 2026 Bet for AI Search URL: https://www.getsurfaced.co/blog/the-value-of-voice-a-2026-bet-for-ai-search Published: 2026-08-13 · Topic: AI search · ~9 min read Audio might be the next big thing for AI search—and a durable way for brands to create original, retrievable information. I consulted my astrologist last week. She said audio is the next big play. I've been making this bet in conversations for a few months now and it's time to write it down, including the parts where the data doesn't fully cooperate (you'll see below). The short tldr; version: - most brands are pouring budget into the one format that got commoditized to zero - most treat the formats that are still expensive to fake as a brand awareness play measured in downloads. - Finding your context layer through audio and video will help you craft that moat you want That's backwards, and the citation data I’ve been seeing is starting to show it. ### The slop math is more interesting than the slop panic You already know the headline number. Graphite sampled 65,000 English-language articles and found AI-generated share went from roughly 10% in late 2022 to 52% by October 2025, then plateaued around a 50/50 split. Every LinkedIn post about the death of the internet cites it. The second Graphite finding is the one nobody quotes, and it's better. When they looked at what Google actually surfaces, 86% of the articles in search results were human-written. Only 14% were AI. This was data from October, so grain of salt now. So the slop is real, it just isn't winning. What it did was reset the floor. Machine content made publishing cost roughly nothing, and your content now has to clear a bar that used to be cleared by simply existing. Fifty pages a month used to be a moat. Now it's the price of entry, and it's an entry fee anyone can pay with a credit card and a prompt! This is also why I keep seeing the same shape in data I see. Visibility spikes for six or eight weeks, then flattens, then slides. The best folks like Lily Ray, Cyrus Shepard and others are finding this all the time. ### The closed loop does not break through I say this with love, because I've watched genuinely smart teams do it. They ask an AI tool what topics to cover. They ask an AI tool to draft the coverage. They ask an AI tool to optimize the draft for AI retrieval. Then they wonder why the output has the same density of original information as everything else in the category. No new information entered the system at any point. It's a closed loop. You put the model's understanding of the world in, and you got the model's understanding of the world back out, formatted nicely. Google has been telling us the answer to this for years and we've been treating it as a compliance checkbox. Information gain. First-hand experience. E-E-A-T. Proprietary data. All of it is one instruction wearing four outfits: publish something that wasn't already in the index. And in July, a survey of 45 GEO studies published between November 2023 and July 2026 landed on arXiv with a conclusion that should have been a bigger deal than it was: no evaluated technique consistently improved discoverability, traffic, or business outcomes across platforms. I covered it in the newsletter. Some rewrites actually REDUCED retrieval. Structure isn't the differentiator anymore, cause, well, everyone can have structure. Schema is a solved problem you can buy with your dev team cycles. What's left is whether you know something that your competitors don’t. ### Your brain is the last proprietary database Every team I work with has a first-party data problem, and almost none of them have a first-party data shortage. They just are not using it, most likely. They have sales calls where the same objection surfaces for the eleventh time. Support tickets nobody has read as a corpus. A VP who can explain exactly why the standard approach in their category fails at scale, and has never written it down because writing it down would take four hours and she doesn't have four hours. Try to get that out of her as a blog post and you'll get a calendar invite that slips three times. Put her on a mic for forty minutes with someone asking good questions and you'll get more original thinking than her last six months of content calendar produced. Talking is the cheapest extraction method we have for the stuff only you know. It's not a new observation, journalists have run on it forever. What's new is that the extraction now compounds into a machine-readable asset instead of dying in a Google Doc. ### The transcript is the asset This is the part people miss when they hear "invest in video." AI engines aren't watching your webinar. With the partial exception of Google's own ecosystem, they're reading whatever text sits on the page: transcript, show notes, metadata, schema. Audio and video are the capture method. Text is what gets retrieved. The Podcast Citation Index study out of Everything-PR made this brutally clear, too. They tracked 5,214 citations across 187 shows from December 2025 through May 2026, and the finding buried in their methodology matters more than the rankings did. Most shows that missed the top ten missed on transcript access, not content quality. If the engines can't crawl the words, the show doesn't exist to them. Two decades of SEO people have been saying transcripts matter. They were right for the wrong reason, and now they're right for a much better one: a one-hour conversation transcribed and published on a page you own becomes a permanent retrieval anchor. Which also means the podcast you host on Spotify and Apple only, with no episode page and no transcript, is invisible. You built the asset and then rented it to platforms that don't share it to help build that a sweet, sweet search equity. ### What the citation data says, and where it argues with itself Now the fun part, because the numbers here are genuinely messy. ![YouTube leads in Google AI Overview mentions in June 2026, securing 20.9% of the share out of nearly 3 million prompts analyzed in the U.S.]() YouTube leads in Google AI Overview mentions in June 2026, securing 20.9% of the share out of nearly 3 million prompts analyzed in the U.S. The bullish case: Ahrefs' Brand Radar looked at 1.9 million citations across a million AI Overviews in July 2026 and found youtube.com as the single most-cited domain at 21.1% mention share. Other studies put it between 20.9% and 29.5% depending on the window and sample. BrightEdge has it at 16.6% in AI Mode. Adweek's January investigation across 6.1 million citations found YouTube had overtaken Reddit as the most-cited social platform, appearing in around 16% of LLM answers, generating 18x the citations of Instagram and 50x TikTok. The bearish case: from the same body of research: ChatGPT cites YouTube at roughly 0.2%. Otterly's analysis of 100 million-plus citations found only 5.54% of all citations came from social and video platforms at all, with YouTube taking about a third of that slice. And Otterly reports Gemini's chat surface barely touches YouTube, even as Google's search-integrated surfaces lean on it heavily. ![Otterly AI Looking at Gemini's YouTube Citation Share]() Otterly AI Looking at Gemini's YouTube Citation Share Both things are true because they're measuring different denominators on different products. "YouTube is the most-cited domain" and "video is 5% of citations" are not in conflict. One is share within AI Overviews, the other is share of everything. So be precise about what you're buying. This is a Google-surface bet first, a Perplexity bet second, and close to a non-bet on ChatGPT today. If your buyers live in ChatGPT (it’s hard to even know that unless you’re doing self-reported attribution cause referral is so low, video is a slower play. I'd still make it. Google is where the volume is, Google owns the video platform, and Google's models process the visual, the spoken audio, and the metadata as one input. Nobody else has that integration and nobody else can buy what they’ve curated over a couple decades. ### You need an audience AND you need to be retrievable The stat that changed how I talk about this: in Otterly's 2026 study, 40.8% of AI-cited videos had fewer than 1,000 views at the time they were cited. Read that again if you run a brand channel with 400 subscribers and have been told it isn't worth the effort. Citation is uncorrelated with popularity. The engine isn't looking for the video everyone watched, it's looking for the passage that answers the question cleanly, from a source it has reason to trust. Your 300-view customer-education video with a clean transcript and a real answer in it can beat a creator with a million subscribers who never published theirs. So, it’s about the retrieval, but also distribution is the moat that some companies have over others, and having an engaged audience is obviously very important. ### The attention argument, which is the one I actually believe most Set the citation data aside for a second. Audio is the only content format people consume while doing something else. Driving, running, weeding the garden, walking the dog (My Scout & Tilly are good girls). ![Scout likes being slept on.]() Scout likes being slept on. Nothing else in your mix gets to borrow time from an activity that isn't content consumption. Every article you publish is competing for the scarcest resource on earth, time. And the supply side is asymmetric in a way that I don't think is priced in yet. An affiliate operator can push 100 pages a week without breaking a sweat. Nobody has figured out how to fake being in a room with someone for an hour, taking hard questions, unscripted, with a recognizable voice and a name attached. If provenance and authorship signals get any weight in future models, and I'd put decent odds on it given where the publisher fights are heading, the archive of you talking is worth more than the archive of you publishing. ### Where this breaks In 4 areas: You make video slop. Same closed loop, new format. AI-scripted talking head reading an AI-generated outline into a webcam is not first-hand experience, it's the same commodity content with worse production values and a longer production cycle. If the person on camera isn't saying something they'd defend in an argument, don't record it. You publish to platforms you don't own. Spotify, Apple, and YouTube are distribution. The episode page on your domain, with the full transcript, speaker attribution, headings that mirror how buyers actually phrase questions, and PodcastEpisode plus AudioObject schema, that's the asset. Platform-only is a bet on the platform's continued cooperation with each AI surface, and that cooperation is being renegotiated constantly. Ask Reddit and Google how their deal is going. You measure it with the wrong scoreboard. Downloads and watch time are attention metrics. Fine for the social team, useless for this. The scoreboard is whether the transcript gets retrieved and cited, and whether you show up when your buyer asks the question. Nobody owns it. This is the one that actually kinda kills it. It lands between brand, content, social, and demand gen, which means it lands nowhere. 61% of companies still haven't assigned AI search ownership to anyone. Adding an unowned podcast to that is just adding another orphan. ### If I were standing this up on Monday It would not be just a content project…more like a new system. Pick one recurring internal conversation that already happens and already contains original thinking. The monthly customer call review. The post-mortem. Whatever. The thing your sharpest SME explains to every new hire. Record it. You're not adding a meeting, you want to record the whole damn thing. Then build the pipeline once and stop thinking about it: record, transcribe, publish to an owned page with the transcript in HTML rather than a PDF, mark it up, cut the clips for distribution afterward. Clips are the byproduct that you share on the socials. The page is the point you need for search. Two mediocre conversations a month beat one beautifully produced quarterly thing, because you're building a corpus. And give it a name on someone's goals. Their personal goals. Give them a bonus if they do it! The diagnostic question I'd ask your team this week: what did we publish last quarter that literally no other company in our category could have published? If it takes more than ten seconds to answer, you already have your content strategy for the rest of the year, and it starts with turning a microphone on, and understanding the VALUE of VOICE. ## When technology allows creation to outpace distribution URL: https://www.getsurfaced.co/blog/when-technology-allows-creation-to-outpace-distribution Published: 2026-07-09 · Topic: Point of view · ~4 min read When creation becomes frictionless, distribution, community, and first-party relationships become the durable advantage. I've been chewing on something Greg Isenberg wrote, something he called "The Great Flip." It's been rattling around in my head for weeks, and I want to build on it with what I'm seeing in AI search. A decade ago, developers were the kings. Tech was booming, products were moats, and the best engineering minds got golden handcuffs at companies building the next big platform. Engineers ran the show. Product was just whatever those engineers could pull off. Marketing and distribution were an afterthought, because the thing itself was rare enough to sell on its own (Isenberg, "Marketers are the New Engineers"). ![]() I remember trying to figure out app development back in 2012. It felt like witchcraft. Not something you picked up in a weekend, the platforms were arcane, and the whole thing sat comfortably outside anything I understood. I was a search and data guy at the time, watching keyword traffic and conversion data tell me exactly how people found what they needed. Attribution felt solved back then in a way it doesn't now. Now it's 2026, and the buyer journey looks nothing like that. Google's Zero Moment of Truth research puts the average buyer at 7 to 11 touchpoints across at least four channels before they decide anything. That number keeps climbing every year brands find a new surface to show up on. B2B deals now run 25-plus touchpoints over ten months with 11-plus stakeholders weighing in, according to Gartner. A lot of that research happens where you can't see it: word of mouth, dark social, gated communities, consultants people trust. When someone's about to spend real money on software, they want a person to vouch for it, because comparison content gets manufactured constantly and buyers have caught on. GrowthSpree found buyers work through 20 to 30 pieces of content, review sites, peer groups, podcasts, analyst reports, before they ever talk to a vendor, and almost none of it happens where you can track it. Now ChatGPT recommends the vendor because it trained on that same content. Is that actually different, or just a new coat of paint on the same problem? That's been the arc of the last fifteen years. Now look at where we actually are. Tools like Canva and Lovable handed creation to people who were never trained for it. Canva's whole pitch was pulling the entire design stack into one page anyone could use.. The skills that used to gatekeep an entire industry got commoditized because the tools got that intuitive. Hand my 70-year-old mother a copy of Lovable or Wix's MCP tools and she could build something real. If she wanted to sell off everything sitting in her basement, I could snap photos with Google Lens, catalog the lot, and she'd have a working storefront by dinner. A plumber can photograph every job he does this week and turn it into how-to content for local search. None of that required an engineering degree five years ago. The moat around building digital products, sites, apps, tools, is gone. So what happens when the thing that made Silicon Valley powerful gets handed to everyone? The people who used to be untouchable get a little less untouchable. What replaces them? Distribution does. You've heard the line, it's not what you know, it's who you know. When making content is basically frictionless thanks to decent phones and generative AI, the audience holds the power now, not the creator. Community is the one advantage that compounds and can't be copied (AdAge, "Brand Community Strategy"). ### What's next? I see three things happening because of it: Gatekeepers are losing their grip. Platforms and studios used to decide what got seen. Now, audiences decide through merit and algorithmic sharing. If you're not building directly with your audience, you're not staying relevant. Niche beats demographic. Age and gender bands don't predict behavior the way they used to. People organize around shared passions and values now, and reaching them means finding where your specific tribe actually hangs out, not blasting a generic message at a broad segment. First-party data is the whole game. Renting attention from someone else's platform is a losing bet long-term. Watch how influence actually moves now: a colleague mentions a brand in Slack, someone asks ChatGPT about it, the AI backs it up, they Google the brand name, and land on the site. Gartner says 75% of B2B marketers are already shifting to first-party data strategies to protect their pipeline. The companies getting this right are building owned channels so they control the message and own the data, instead of borrowing both. Nobody's talking about what this actually means for marketers. While people panic about AI taking their jobs, the job itself is about to matter more, not less. What's actually scarce now is artistry, storytelling, judgment, and taste. The person who can look at a human being and know exactly what they need to hear before they click buy or tell a friend. Cowork automating your workflow or Codex shipping your code, that's table stakes soon. The marketer becomes the orchestrator, the one person stitching together zero, first, and third-party data into copy and campaigns that actually move people. So if the product isn't the moat anymore, ask yourself who's actually vouching for you right now, in the group chats and Slack threads and ChatGPT answers you'll never see. If you don't know, that's the whole game you're not playing. The Great Flip. Spot on, Greg. ## David vs. Goliath: why Google isn't going anywhere URL: https://www.getsurfaced.co/blog/david-vs-goliath-why-google-isn-t-going-anywhere Published: 2026-06-16 · Topic: AI search · ~6 min read Google's search data, Gmail, commerce infrastructure, business profiles, and YouTube create an advantage AI-native challengers cannot easily copy. Lots of companies are cashing in on this AI wave, and we'll see which platforms are left standing when the dust settles. You'd be led to believe OpenAI and Anthropic are accelerating faster than Google (true) and are destined to dominate (are they, or is there a glass ceiling?). Acceleration tells one story. But rather than get misdirected by hype, let's look at what Google has that the others don't: - 20+ years of search data - Gmail - Universal Cart and shopping - Business profiles - YouTube ### SEARCH First, those 20+ years of search data. I remember when Google Webmaster Tools gave marketers insight into how people were searching. Fast forward 20 years, and it hasn't changed all that much. We recently got AI Search impressions added - cool- but attribution to what actually drives revenue still feels like a Rube Goldberg machine at times. Google likely has more personal data than any company on earth, down to a profile of who people are and what they're interested in. They literally know if someone Googles "how to hide a body." Despite the acceleration of other channels over the last two years, none of this can be built without users filtering through your platform first. Claude and Anthropic have an identity crisis, positioning themselves as tools for businesses while also being conversational search engines for consumers. It took Google decades to build its ecosystem, and the acceleration of these new platforms is rapid, but time will tell how they manage explosive growth and turn it into real momentum. ### GMAIL Now, your inbox. The thing you hold sacred and meticulously curate, the one that's representative of your interests, your shopping, your life. 22% of the global population uses it, about 1.8B monthly users. Google knows what kind of coffee I like because of my Trade subscription. It knows what music I like from the tickets I buy. It knows I'm purchasing video equipment. It knows which newsletters I'm subscribed to, and it reminds me of the meetings I have with my family. This becomes massive with Personal Intelligence, which Google announced earlier this year and again at I/O. It uses your Google apps to provide suggestions and context in results. ChatGPT has integrations in its 5.5 release to leverage your inbox, but it's not native the way it is with Google. This is a huge growth lever, and it's what will make Google feel more personal. Garrett Sussman from iPullRank ran a whole study on Personal Intelligence faster and better than I ever could. Props to this gentleman. The TL;DR: brand appearance rose from 23.9% to 66.8% in the Personal Intelligence account, and brands introduced through email appeared in 53.6% of responses, making inbox content a major AI Search variable. ### COMMERCE Now shopping. With a massive amount of conversion data from Google Ads and Google Pay, they're on the front foot in the agentic commerce era. Between the Universal Commerce Protocol (UCP) and the Agent Payments Protocol (AP2), they're outfitting the largest retailers in the world with the technology to integrate with Google and be considered in this new era. Google came for your wallet in 2018 when Google Pay was introduced, because they wanted to own the transaction layer. The pieces are starting to fall into place: find what you're looking for using our search, organize your life using our apps, discover products through clean integrations, and buy using our rails. AI commerce is absolutely a thing and will continue to grow. People have used Google and trust them, so that's why they are throwing their credit card into Google Pay. Are they confident enough in OpenAI yet? Time will tell whether the adoption of commerce will fully make it's way to the AI-native search engines. Just over Q1 2026 Walmart and Amazon tested it, then sort of walked it back. The trust and confidence aren't there yet for consumers, but it will eventually get there with more familiarity. ### BUSINESS PROFILES Google is KING of local businesses and has been the leader in taking brick-and-mortar awareness digital. There are over 200M business profiles globally, and 76% of those are verified. Google Business Profiles was a huge infrastructure play that made sense for a consumer economy, another layer in Google's brilliance of owning the entire digital ecosystem for search and discovery. Google Maps ties it all together, giving consumers the tools to find businesses locally as they navigate from A to B. When people look locally, 87% of them use Google to find a business, smashing alternatives like Yelp, Facebook, or Apple Maps. Google even empowers businesses to add more and more information to their profiles, furthering its user-generated-content engine. ### YOUTUBE Lastly, YouTube has about 2.72B monthly users, with 1B hours of content watched every single day. Google I/O this year announced Ask YouTube, which basically turns the consumer platform into a new discovery channel by using transcripts to build a closed-loop ecosystem of information. Reddit is big, yeah, but YouTube is where content creators go. It's where influencers go. It's where people discover hobbies or products that are physical, or talk about ideas where people like to learn through media and not through text. The addition of YouTube content fueling Gemini is such a game-changer and something that the new players can't compete with. Nobody is creating content in the Anthropic ecosystem; OpenAI is not adding user-generated content. It's such a stark difference, you wonder how they will keep up with Google beyond leaning on partnership deals, crawling, and scraping to keep afloat. Google is in the driver's seat. ### WHAT DOES IT ALL MEAN? The reason it's not just the sum of its parts, it's more, is that it's forming habitual dominance. YouTube's daily active user base combined with Gmail's daily usage creates a "habit loop." Users aren't just visiting Google for specific, one-off searches; they're living inside the environment. The AI multiplier: the rapid adoption of AI Mode (reaching 1 billion users in its first year, if you trust that) shows Google is successfully transitioning its massive existing user base into conversational commerce. Because these users are already logged into Gmail and YouTube, the AI has a 360-degree view of their preferences, enabling the kind of highly personalized "agentic" shopping assistance that a standalone search engine or chatbot can't match. And lastly, monetization efficiency. Because Google captures the user from the inspiration phase (YouTube) through the research phase (Search) to the transaction phase (Gmail and Google Pay), it can offer advertisers a more complete view of the customer journey than any other platform. It's not that they own one part of what people do online, they own practically all of it. Not using a Google product as part of your daily online activity feels crazy. Whether it's paying for something with GPay, checking your email, or searching for something, they're part of modern world infrastructure. So when entrants like the headline-grabbing OpenAIs and Anthropics come onto the scene without these decades of data and experience, you start to wonder what the underlying value is beyond the models themselves. ChatGPT used Google search to fuel its training data. Claude reportedly leans on Brave for search. When you see headlines about "AI exploding," think about the underlying economy these platforms sit on, and you'll quickly realize it's David vs. Goliath. I've mapped out the advantage Google has over today's headliners, hopefully painted a picture of why Google isn't going anywhere (short of getting broken up), and made you a little smarter the next time you hear about exploding numbers in the grand scheme of things. ## Enterprise AI Search Challenges URL: https://www.getsurfaced.co/blog/enterprise-ai-search-challenges Published: 2026-05-27 · Topic: Enterprise strategy · ~5 min read Five challenges enterprises face in AI search: executive buy-in, performance, budgets, strategy, and organizational friction. I have worked with agencies for 7 years, seeing all sorts of organizations from MIT to Pew Trusts, and worked at organizations like Intuit, the Royal Bank of Canada, and been in SEO/AEO platforms that cater to enterprise like BrightEdge and Brandlight. I’ve seen a lot of scenarios play out, and I want to share some learnings. And not all of this is purely a search problem. Let’s jump into it. First, enterprise organizations have a ton of challenges moving with technology. From procurement to enablement to execution, large organizations generally move slower because of red tape. Legal. Compliance. Sign-off. So much. Here is how I would bucket the (non-exhaustive) list: - ###### Executive buy-in — Internal positioning - ###### Proving performance — Attribution, measurement - ###### Securing budget (tech) — To help with efficiency & scale - ###### Strategy — Opportunity x goals - ###### Organizational friction — Siloed, horizontal alignment To win, it's about integration and human alignment. Let's break down these challenges I've witnessed. ### Executive buy-in How far up (to the board) and down the ladder do people understand the shift that's happening? How prepared are any of them to talk about the titanic shifts happening in search? Do they think this is just SEO 2.0, or do they understand the speed at which this is happening? Ask yourselves questions like this, and then think about how it ladders to organizational goals, and if it's even a priority this week, this quarter, this year. Disruption is happening all around us, but it's time we put a price tag on inaction. Education is extremely important, as executives have far too many things on their plate and conciseness is key, so you learn to be extremely succinct when creating plans, documentations, or strategy for senior leadership. ### Performance The attribution of AI is difficult; there's no sugarcoating it. There are proxies you can use, like Google's Monthly Search Volume for your brand, but there are a lot of factors that influence that. There's referral traffic and revenue associated, but that's only a small piece of the puzzle because the value of AI search is visibility to be in the consideration set, not the subset that transacted during that exact interaction. Due to the constant evolution of the space, the best we can get is directional at the moment. You can find your share of voice and share of citation — how often you are mentioned in the answers — and see how that changes over time and how much of certain markets you represent. There are new metrics that are important to AI search, and they are absolutely different from traditional search. Some have a big price tag, which brings us to... ### Budgets Most organizations will evolve their current SEO strategy. They will use the same technologies that have stapled on new "GEO features." Some will chase new solutions that crawl and query what's happening for their brand using prompts, a big market (that I was part of). Some might look for headcount, hiring people who are AI-fluent and can help build skills and infrastructure to produce content at scale or implement SEO recommendations for better eligibility and accessibility. Budgets should be looked at as scaler or optimization investments. Does this help from 0 to 1, or 1 to many? Does it help unburden the team and free them up to do things they want to do, or is it a build vs. buy conversation? Is it better to hire in-house or get an agency? Soon, AI surfaces will do what SEOs have been doing for a decade: integrate paid and organic into a holistic approach, because the channels and surfaces are the same and they are competing for attention. Paid shores up weak organic, and good organic reduces the need for paid. As ad placements are introduced, teams that have become "holistic search" will be advantaged. ### Strategy Strategy should not be a list of tactics. It should incorporate your organizational and digital goals. You need to understand how you are going to win in the context of your industry, your market, your product, or your business strategy. Slapping together a list of best practices based on a ChatGPT ask is not a strategy. Putting together a list of "stuff to do" is not moving toward a goal; it's actually fracturing your focus. I've seen 100-slide decks from agencies, and I've seen a tight 5 slides that cover strategy. It's not about the count; it's about the substance. Ultimately, strategy does not live in a PowerPoint — that's just for stakeholders — it lives in the micro-decisions you made to get there and the ones you'll be making after: - ###### WHO are the people or brands we should connect with to create value-added content? Saying "content marketing opportunities" is vague. - WHAT are the websites that our consumers are using to discover us? Just saying sites like Reddit or YouTube isn't enough. - WHEN are we measuring success? What does the timetable look like for understanding incremental gains? Saying "traffic" isn't the right metric. - WHERE are we strong or weak, and what is our advantage from a business perspective? Doing "playbooks" isn't one-size-fits-all. - HOW are we aligning across teams to get this done? Usually there's a driver and a RACI; ownership is flattened with AI. ### Industry and organizational friction I've worked at Intuit, I've worked at RBC, I've worked at BrightEdge serving enterprise customers, and I've led accounts at agencies with Fortune 500 clients. Friction is everywhere; you can't ignore it, and time-to-action is often one of the biggest hurdles. Different industries have different issues they need to face: some can't scale content, some have complex supply chains, and others have nothing stopping them but a plan. A few frictions I've seen: - ###### Financial services? Compliance and regulation. - Ecommerce? Scale and funnel optimization. - NGO? Internal politics and membership. Beyond even the industry-specific challenges, there's organizational friction. Getting an at-bat for a project, getting alignment for your annual OKRs, getting in front of ELTs to propose new headcount or budget — this stuff takes time, and ultimately it's about building trust. Navigating these waters can be challenging, and hopefully some of the wisdom and headaches I've endured can help you in your journey. --- # Newsletter issues - AI content plateaued, and Google still prefers humans (2026-10-01) — The Graphite data everyone cites, the finding nobody quotes, and what it means for your content. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-october-1st-2026 - Google's fundamentals still matter (2026-09-16) — Some AI search growing pains, and what to do with both. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-september-16-2026 - Google plays defense, Reddit takes a tumble (2026-09-01) — Goto redirects, an 86% citation collapse, and where the maturity matrix stops short. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-september-1st - Cloudflare enters the arena, and who owns AEO (2026-08-17) — The company that can see the traffic is now scoring the visibility everyone else infers. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-august-17th - Publisher deals unravel, 45 studies say nothing works (2026-08-03) — Reddit's Google deal winds down, and whether you should hire a Head of AEO. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-august-3rd - Exposure up, clicks down, trust wobbly (2026-07-15) — AI Overviews in 48% of searches, referrals converting at 5x, and trust down 28 points. https://www.getsurfaced.co/blog/surfaced-ai-search-intelligence-july-15th - Last-touch is hiding most of your AI conversions (2026-07-01) — Last-touch showed 0.9% of conversions from AEO. Self-reported showed 9%. https://www.getsurfaced.co/blog/surfaced-search-intelligence-july-1st - Surfaced - Search Intelligence - June 15th (2026-06-15) — 100% human-curated search insights https://www.getsurfaced.co/blog/surfaced-search-intelligence-june-15th - Surfaced - Search Intelligence - June (2026-06-02) — 100% human-curated search insights https://www.getsurfaced.co/blog/surfaced-search-intelligence-june - Surfaced - A Search Newsletter (2026-04-20) — Finding the signal through the slop https://www.getsurfaced.co/blog/test-newsletter-420 # Apps and artifacts - SEO + AEO reporting dashboard — Search and AI performance tied to business outcomes, with goals, input metrics, and owners in one view. https://claude.ai/code/artifact/c50f695a-e430-40d1-aabc-33d5cb3f202a - AI search intelligence agent — An automated research workflow that crawls industry news, research, and social threads through Andrew's context layer. https://d9e457cc-f179-4944-9923-b5a737f40096.usrfiles.com/ugd/d9e457_6963237597014420992af3b9ad7eecc1.pdf - Lightweight AI search audit — A prototype that reads a page like a crawler and answer engine, then returns ranked fixes. https://d9e457cc-f179-4944-9923-b5a737f40096.usrfiles.com/ugd/d9e457_786458993ad541b1ac34a96cc8fc9919.pdf - Global LLM Usage Atlas — How LLM usage breaks down across 25 countries and eight platforms using first- and third-party sources. https://surfaced-global-llm-usage.lovable.app/ Total articles: 8. 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