AI Visibility by Industry: Playbooks That Actually Fit
Why AI visibility isn't one playbook
Most GEO advice is written as if every business were the same business: publish structured content, earn citations, get reviewed, measure your share of voice. None of that is wrong. But apply it uniformly across a Shopify store, a B2B SaaS company, and a medical clinic, and you'll waste most of your effort — because the thing that decides whether an AI engine names you differs sharply by vertical.
We've spent the past year writing industry-specific playbooks precisely because the generic advice kept failing in predictable, industry-shaped ways. This page is the hub: first the argument for why industry context changes the work, then a short honest summary of each playbook so you can jump to the one that fits.
Query patterns change what "visibility" even means
Start with what people actually ask. An e-commerce brand lives or dies on comparison and recommendation queries — "best running shoes for flat feet," "is brand X worth it." A SaaS company faces those too, but layered with integration, pricing, and migration questions that get asked deep into a sales cycle. A dentist's queries are local and trust-loaded. A bank's are cautious and heavily regulated on both ends of the conversation.
These aren't cosmetic differences. Query patterns determine which pages can win (product roundups vs. service pages vs. documentation), how long the answer's shortlist is (consumer answers name many brands; health answers often name none), and whether "visibility" means being recommended, being cited, or merely being described accurately. If you haven't mapped the query patterns of your vertical, you're optimizing for someone else's customers. Our guide on how to design a prompt set covers the general method; the playbooks below apply it per industry.
Every industry has its own source gatekeepers
The second axis is who the engines trust when they assemble an answer — and this varies more by industry than anything else we track. E-commerce answers lean on affiliate roundups, Reddit threads, and review platforms. SaaS answers run through G2, comparison articles, and developer communities. Healthcare answers defer overwhelmingly to institutional sources, with a directory layer (Healthgrades, Zocdoc) deciding provider-level questions. Local answers are built from business profiles and map data. Finance answers cite regulators, established financial media, and not much else.
The practical upshot: the same tactic — say, pitching listicle placements — is close to the entire game in one vertical and nearly useless in another. Knowing your vertical's source ecosystem tells you where earned coverage actually converts into AI visibility, and where it just feels productive.
Model caution scales with the stakes
The third axis is how nervous the model is. AI engines will cheerfully recommend a specific espresso grinder on thin evidence. Ask them about a medication, a mortgage, or a surgeon, and they hedge, defer to institutions, and often refuse to name brands at all. This caution — inherited from YMYL-style content policies and reinforced in training — means regulated and health-adjacent industries face a structurally higher bar for being named, and their playbooks are as much about satisfying a skeptical fact-checker as about marketing.
If your industry sits on the cautious end, generic GEO advice will overpromise. If it sits on the relaxed end, you're in a knife fight for recommendation slots that the cautious industries don't even have. Either way, the constraint shapes the strategy.
The industry playbooks
Here's each playbook in a paragraph, with an honest note on who it's for.
E-commerce AI visibility. The recommendation-query vertical: your customers ask engines what to buy, and the answers are assembled from affiliate roundups, Reddit, review corpora, and product data. The playbook covers product feed and schema hygiene, earning roundup placements, and why your product pages need to answer comparison questions your category pages ignore. If shoppers can ask "which one should I get" about your product, start here.
SaaS AI visibility. B2B software queries run deeper than consumer ones — buyers ask engines about integrations, pricing tiers, migration paths, and alternatives, often by name. The playbook covers the G2-and-comparison-site layer, documentation as a citation asset, and competing on "X vs Y" and "alternatives to X" queries where AI answers now do the analyst's job. Longer sales cycles mean more queries per deal, which makes this vertical unusually measurable.
Shopify store AI search visibility. A practical, platform-specific companion to the e-commerce guide: what Shopify gives you out of the box (and what it quietly gets wrong), theme-level schema fixes, and how a small store without a content team competes for AI recommendations. If you run on Shopify, read this alongside the e-commerce playbook rather than instead of it.
Healthcare AI visibility. The most constrained vertical we cover. Models hedge on health queries, defer to institutional sources, and demand corroboration before naming a provider. The playbook is honest about what a clinic can't win (condition-level content owned by major institutions) and specific about what it can: the directory and review layer, provider-level pages with schema, and locally scoped questions the institutions never answer.
Local business AI search. For single-location and service-area businesses, AI visibility runs through Google Business Profile, review count and recency, and directory consistency far more than through website content. The playbook is refreshingly cheap to execute — mostly hygiene plus a durable review habit — and explains why AI Overviews treats "near me" queries as its own category.
Finance AI visibility and YMYL. The other high-caution vertical, with a regulatory layer healthcare doesn't have: what you can even say is constrained, and engines cite a narrow set of established sources. The playbook covers building the kind of evidentiary trail cautious models accept, where challenger financial brands realistically get named, and compliance-safe ways to earn coverage.
See the live numbers for your industry
Playbooks describe the mechanics; they can't tell you where your industry stands this month. Our industry index tracks AI visibility data across verticals on live engine responses — which categories get brand recommendations, how long the shortlists run, and how that shifts over time. If you want to know whether your vertical behaves like the relaxed end or the cautious end before committing to a strategy, check your industry there first, then pick the playbook that matches what you see.
One last opinion, since this is a hub page and hub pages tend toward diplomacy: the biggest mistake we see isn't picking the wrong tactics. It's running a competitor's playbook because it was written up somewhere impressive. A SaaS team copying e-commerce listicle tactics, a clinic buying content marketing built for retail — the work gets done, the needle doesn't move, and everyone concludes GEO doesn't work. It works. It just doesn't transfer. Pick the playbook for the industry you're actually in.
For a YMYL example, see AI visibility for law firms.