Section 1
How AI assistants actually read your website
Google's documentation describes its AI Overviews and AI Mode using a 'query fan-out' technique: one user question is decomposed into multiple sub-searches across topics and sources, and the system assembles an answer from pages it can retrieve and parse. Other assistants behave similarly, fetching pages in real time or drawing on indexed snapshots. The practical implication: your site is no longer read only top-to-bottom by a person; it is read in fragments by machines hunting for a specific fact, your pricing model, your service area, your proof. Pages that bury answers in marketing prose lose to pages that state them plainly. Fragments win or lose citations. Write each important page so that a machine grabbing any single section, your who, what, where, how much, gets a complete, accurate, quotable answer. If you are turning this into practice, [The Future of Storytelling in Business: Trends to Watch](/blog/the-future-of-storytelling-in-business-trends-to-watch) maps the adjacent system.
Section 2
The AEO tactic stack: what is proven, what is speculative
Not all AEO advice deserves equal budget, and the industry is currently selling plenty of speculation at proven-tactic prices. The table below ranks the common tactics by documented evidence as of June 2026. Two notes deserve emphasis. Structured data has the strongest documented track record; Google's own case studies include Rotten Tomatoes measuring a 25% higher click-through rate on pages with markup. And llms.txt, a proposed file telling AI crawlers what matters on your site, remains exactly that: a proposal. Google has stated its AI features need no special AI files or markup. Some non-Google crawlers may read it, and it costs twenty minutes, so we file it under cheap hedge, not strategy. Anyone selling llms.txt as the centerpiece of AI visibility is selling the cheapest item on the menu.
Section 3
What Google actually says, and what it means for you
Google's guidance on AI features is unusually plain: you do not need new machine-readable files, AI-specific text files, or special markup to appear in AI Overviews or AI Mode. The fundamentals carry over: allow crawling, make content findable through internal links, and publish genuinely useful material. Read that carefully, because it cuts both ways. It deflates vendors selling proprietary 'AI optimization' tricks. But it also confirms the bar that most service-business websites fail: being parseable, fast, internally linked, and substantive. In our audits, the typical 5-7 figure firm's site fails on basics, headings used decoratively, services described in vague prose, no schema, proof scattered across PDFs, long before exotic tactics matter. The future-proof move is unglamorous: make the site machine-legible and evidence-dense. That work is durable across every assistant, Google or otherwise. To see how this connects to the wider system, read [Image and Media Optimization: The Cheapest Website Speed Win Available](/blog/image-media-optimization).
Section 4
A practical AEO checklist for service businesses
Here is the sequence we implement inside ConvertOS, ordered by return on effort. One: verify crawlability, robots.txt, CDN settings, and indexation; you cannot be cited if you cannot be read. Two: rewrite key pages so the first 100 words answer the page's core question directly, with specifics. Three: add Organization, LocalBusiness, Service, and FAQPage structured data, validated in Google's tools. Four: make every claim extractable, prices or pricing logic, service areas, timelines, named results, in text rather than images. Five: build question-formatted content from real sales conversations, since those are the queries buyers ask assistants. Six: monitor Search Console's AI-feature reporting and your own lead-source data, asking new leads 'how did you find us' now that referral paths are opaque. If you want this audited against your actual site, that is a strategy call, not a guess. For the step that usually comes next, see [Scheduling and Calendar Management with AI Assistants](/blog/scheduling-and-calendar-management-with-ai-assistants).