This post explains that only 7.2% of domains appear in both Google AI Overviews and general LLM results, indicating a fragmented discovery landscape. For local HVAC, plumbing, and electrical businesses, traditional SEO will deliver fewer clicks as AI increases zero-click answers, so the focus should shift to being cited by AI systems and using paid channels to convert service intent. It outlines how AI engines value different sources and structured signals than classic SEO, emphasizing schema, authority markers, and concise, quotable content. The playbook recommends question-first pages with local context and pricing, robust schema and service area clarity, and securing trustworthy citations via “AI media” partnerships to build machine trust and drive booked jobs.

AI Overviews Won’t Feed Your Phones: How to Turn GenAI Visibility into Booked Jobs

TL;DR Only 7.2% of domains show up in both Google AI Overviews and LLM answers. Translation for HVAC/plumbing/electrical: the web is fragmenting. Don’t chase clicks—optimize content and listings for machines, secure citations from trusted “AI media,” and keep paid channels tuned to drive calls and jobs while AI eats more organic real estate.

What the 7.2% Overlap Really Means for Local Service Pros

When just 7.2% of domains appear in both Google’s AI Overviews and large language model (LLM) results, that’s a visibility gap. AI answer engines aren’t mirroring classic SEO. They crawl differently, cite differently, and reward different signals. If you’re banking on “ranking #1” to fill the board, expect more zero-click answers that siphon away traffic. The play isn’t to panic—it’s to adapt the content and distribution so machines can confidently quote you, and to use PPC to capture the intent AI creates.

How AI Engines “Decide” (And Why It’s Different from SEO)

Different indexes, different citations

LLMs pull from a mix of sources—Common Crawl, news, standards bodies, manufacturer docs, high-authority how-to publishers, and structured data. Google AI Overviews lean on Google’s corpus plus E-E-A-T signals. Your site might be fine for classic SEO but invisible to answer engines if you’re thin on structured data, real-world authority markers, and succinct, quotable explanations.

The zero-click squeeze

AI answers reduce clicks, especially for “what, why, how” queries like “furnace short cycling causes” or “GFCI keeps tripping.” If the answer is delivered in-SERP, your traffic drops. But people still need service. The win is to be the source AI cites and to make paid channels capture the service intent that survives the answer.

A Practical Playbook: Be Machine-Friendly, Locally Credible, and Conversion-First

1) Content that answers fast (for humans and machines)

• Question-first pages: “Why is my AC freezing up?” “Water heater popping noise?” Each page starts with a 2–4 sentence plain-English answer, then detail.
• Local intent baked in: climate references, code notes, common model families in your market, and when DIY becomes unsafe or code-violating.
• Pricing ranges with constraints: “Typical diagnostic $89–$129; most capacitor swaps $165–$325 in our area.” Ranges help AI summarize and build trust.
• Mini SOPs and checklists: step-by-step bullets that LLMs love to quote.
• Real authority markers: license numbers, tech bios, years in trade, photos of actual work, and manufacturer certifications.

2) Structure it so machines can digest it

• Schema: LocalBusiness + HVACBusiness/Plumber/Electrician, Service, FAQPage, HowTo, Product (for common parts/models), and Review.
• Service area clarity: city pages with unique, non-boilerplate content; NAP consistency; embedded map; hours; emergency flag.
• Answer boxes: use H2/H3 with the exact question, followed by a tight 40–60 word answer.
• Media with alt text: photos labeled with problem, part, and fix (“replacing 45/5 capacitor on Carrier unit”).
• Fast, crawlable pages: no heavy popups blocking content; keep core answers above the fold.

3) “AI media partnerships” that actually move the needle

• Be quotable on trusted sources: contribute explainers to local news, utility blogs, or trade orgs; reference code sections and safety standards

  User-provided content