Practical writing on AI search, AI marketing systems, and the parts of SEO that still matter. No hype, one idea per piece, always something you can use.
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What AI search changed, what it did not, and a four-layer framework for finding where your visibility is breaking, with the method to measure it.
You need an AI marketing consultant when the problem is deciding where AI belongs and how to rebuild the system around it, not when you need hands to run tools. A consultant diagnoses the constraint and designs the approach; an agency or your team executes it. If output is up and results are flat, the gap is usually strategy, not tools.
AI can automate the mechanical marketing work that used to need a person: drafting and personalising at scale, classifying and routing inbound, summarising research, triaging data. The gain is real, but automating without judgement scales mistakes as fast as output. Automate the repeatable steps and keep a person on anything that needs a decision or reaches a customer unchecked.
An AI marketing strategy is a plan for where AI improves specific marketing outcomes, not a list of tools to adopt. Start from the outcome you own, decide which tasks AI genuinely does better, and put a human review layer between the model and anything a customer sees. The teams that see a return treat AI as leverage on a system that already works, not a substitute for the strategy underneath it.
To use AI in marketing without losing your voice, keep the model on drafting and structure and keep a person on the final voice edit, because a model pulls copy toward the generic average it was trained on. Write down what your voice actually is, feed it real examples, and treat every draft as raw material a human finishes. The voice is a judgement call you cannot delegate to the tool.
SEO is not dead, but the claim is not pure hype. Being found now has two layers: ranking in the classic results, and being cited in the AI answers above them. The fundamentals that made you rankable still matter, because AI answers are built on top of them. What dies is treating a ranking as the finish line; the work now is being the source the answer is built from.
AI lead scoring ranks leads by likelihood to convert, learning the pattern from your historical data instead of points you assign by hand. That is more accurate when the data is good and misleading when it is not. It is worth adopting once you have clean, outcome-labelled history and sales and marketing agree on what a good lead is. Without both, it just automates a definition nobody trusts.
Multi-touch attribution splits credit for a conversion across the touchpoints that led to it, instead of giving it all to the last click. No model is objectively correct: attribution is a simplifying assumption, not a measurement, so the right model is the one that fits your decision. Treat the numbers as directional, and fix the first-party data underneath before trusting any of them.
Sales and marketing alignment means both teams share one definition of a good lead and one view of the funnel, so demand does not leak at the handoff. It is a systems problem, not a relationship one: the friction comes from misaligned definitions, data, and incentives, not personalities. Fix those and the metrics each team is measured on, and co-operation follows.
In the age of AI, SEO's fundamentals did not stop mattering; they became the precondition for being retrievable. Crawlability, structure, internal linking, and authority are what let an AI find, read, and trust your pages before it can cite them. What changed is the goal: not only ranking a page but being the source an answer is built from, which rewards extractable answers.
Technical SEO for AI crawlers is mostly the technical SEO you know, aimed at new visitors: the bots that build AI search indexes and fetch pages for assistants. They need what classic crawlers need, reachable URLs, fast responses, and content in the server-rendered HTML rather than painted in by JavaScript. Get your content into the initial HTML and let these crawlers in, or you are invisible.
Revenue operations unifies the systems and data behind marketing, sales, and success, so the revenue engine runs as one rather than three teams optimising separately. It closes the handoffs between them, where revenue quietly leaks. You need it when the handoffs break, when teams cannot agree on the numbers, or nobody owns the funnel end to end. It is an operating model, not a tool.
In content marketing, AI belongs on the mechanical work, research, first drafts, restructuring, and variation, not on the parts that make content worth reading: the angle, the judgement, the first-hand experience. Use it to clear the friction that stops good ideas getting written, and keep a person on what is worth saying. Used the other way round, it just makes more forgettable content.
Apply AI to the steps inside a workflow that are repetitive and mechanical, and route it through a person where a decision or customer-facing output happens. The unit is the step, not the whole process: most workflows mix both, so ask which steps to automate, not whether. Map the workflow, mark each step as toil or judgement, and automate the toil.
AI marketing produces more drafts, not more revenue, because output was rarely what limited revenue. Pipeline is constrained by positioning, distinctiveness, and the system that routes and follows up on demand, none of which a model improves by generating faster. Cheap output on an unchanged constraint adds cost, not results. Revenue moves only when you fix the real bottleneck first.
Answer engine optimization services help your brand get found, understood, and cited by AI assistants. They split into two offers: diagnosis, which measures your AI visibility and tells you what to fix and in what order, and delivery, which does the technical and content work. Most should start with diagnosis: paying for delivery before you know the cause spends budget on the wrong layer.
If AI describes your company vaguely, out of date, or as the wrong business, the problem is entity understanding: it has no clear picture of who you are. It comes from how consistently you are described across your site, structured data, and third-party sources. When those disagree, you become a fuzzy entity and a model prefers a clearer competitor. The fix is consistency, not more content.
A generative engine optimization checklist works best ordered by cause: the fixes are prerequisites for each other. Work in this order: make your content retrievable, make your identity consistent so a model knows who you are, make your key answers extractable, earn corroboration, and measure on a fixed schedule. Doing these out of order wastes effort on layers that cannot yet pay off.
GEO and SEO share most of their technical foundation, so you rarely need a separate agency or team for AI search. What you need is a provider who measures the right outcome and has reprioritised for it. Bring in a specialist only when your current provider cannot show a method for measuring AI visibility, or treats AI search as a product to upsell rather than a shift in the work they already do.
To check whether your brand appears in AI answers, run a fixed set of buyer questions through each assistant they use, in a clean session with personalisation off, and record whether you are cited, mentioned, or absent, and how you are described. Check each surface separately: ChatGPT, Gemini, Perplexity, and Google AI Overviews retrieve and answer differently, and one does not predict another.
You do not rank in an AI Overview directly. Google builds it from pages already ranking for the query, so the route in is to earn strong conventional visibility for the query and its sub-questions, then make your answer easy to extract: a direct response near the top, self-contained passages, and coverage of the sub-questions the Overview draws on. There is no separate Overview ranking to buy.
To show up in ChatGPT search, your pages must be reachable by its crawler, present in the index it queries, and clear enough to extract. Four things do it: let OpenAI's crawlers fetch your site, state your answers plainly near the top of each page, keep your name and category consistent everywhere, and earn mentions from sources ChatGPT trusts. There is no submission form and no paid placement.
An AI visibility audit measures how AI assistants find, describe, and cite your brand, and diagnoses why. A thorough one checks four things: whether your pages can be retrieved and rendered, whether your identity is consistent enough to understand, whether your content is extractable, and how you are described and cited across the assistants buyers use. It is a diagnosis, not a score.
AI answers cite the source that adds something others do not. When every page in your category repeats the same advice, a model has no reason to prefer yours, so it draws on whoever supplied the original material: first-hand data, a named method, the primary source. To be citable, stop restating the consensus, publish something only you could have written, and structure it to be extracted.
An assistant recommends the company it can most confidently connect to a category. A competitor gets named instead of you when the model has stronger evidence for them: they are retrieved more often, their pages answer the question directly, their identity is consistent, and other sources corroborate them. It is an evidence gap, not a quality gap, and each cause is diagnosable.
Publishing more content usually lowers AI visibility, because volume works against what these systems reward. A flood of thin, similar pages dilutes your entity, so a model is less sure what you are known for; it repeats the consensus, so none of it is worth citing; and it buries your genuinely strong pages. The fix is fewer, more original pages, and pruning the rest, not publishing faster.
Schema markup helps AI parse a page and connect it to a known entity, but it does not force a citation, override a confused identity, or lift a page with no clear answer in it. It is a comprehension aid, not a ranking lever. If schema is not moving your AI visibility, the problem is usually upstream: retrieval, entity consistency, or extractable content the markup describes but cannot replace.
SEO is optimising to appear in search engine results. AEO, or answer engine optimisation, is optimising to be the source an AI-generated answer draws on. GEO, or generative engine optimisation, means substantially the same thing as AEO and the two are used interchangeably. In practice these are not three separate disciplines but one discipline with a broader definition of where visibility happens.
Check your robots.txt for directives targeting GPTBot, ClaudeBot, PerplexityBot, Google-Extended and similar agents. Blocking them keeps your content out of AI answers that would otherwise cite you. Some agents control training use and others live retrieval: blocking training does not affect whether you are cited today, while blocking retrieval removes you entirely.
AI assistants that browse the web run a search, retrieve candidate pages, extract passages that address the question, and compose an answer from those they judge most relevant and reliable. Being citable therefore depends on three things in sequence: being retrieved at all, containing a passage that directly answers the question, and carrying signals that the source is trustworthy.
Build a fixed set of prompts covering how people describe your category, your brand, and the topics you claim expertise in. Run each in a clean session with memory and personalisation off, several times, across the assistants your buyers use. Score every result as cited, mentioned, or absent, and record which competitors appeared instead. Repeat on a fixed schedule.
If your positions are steady but your clicks are falling, the likely cause is that AI-generated answers are satisfying searches that used to send you a visit. The query still happens, your page still ranks, but the click never occurs. This is measurable in Search Console: find pages where impressions hold or rise while clicks fall, showing as a falling click-through rate at a stable position.
A migration protects search rankings through redirects, but redirects do not transfer citations in AI answers. Assistants cite URLs they have seen, and third-party pages still point at your old ones. Baseline your AI visibility before you migrate, keep every redirect a single hop to a page that answers the old page's question, and re-run the same prompts for 90 days after launch.
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Written by Viken Patel. No ghostwriters, no AI-generated filler. Unsubscribe any time.