Most "AI search strategy" advice is written as if all queries were the same. They are not — and the differences between industries are not marginal adjustments. They span the entire strategy space: from sectors where AI answers now dominate nearly every research query, to a sector where Google has visibly decided to keep AI out of the transaction path.
BrightEdge's year of continuous AI Overview tracking — across industries, using its Generative Parser methodology — produced the single most strategy-defining dataset in the field, and it is under-cited. This piece walks through the vertical data and what it means for anyone whose playbook was written for "search in general."
The Trigger Rate Spread
Year-over-year AI Overview trigger rates by industry:
| Industry | Early 2025 | Early 2026 | Change |
| Healthcare | 72% | 88% | +16 pts |
| Education | 18% | 83% | +361% |
| B2B Tech | 36% | 82% | +46 pts |
| Restaurants | 10% | 78% | +68 pts |
| Insurance | 17% | 63% | +46 pts |
| E-commerce | 29% | 4% | -25 pts |
Read the two outliers carefully, because they are not noise — they are policy.
Healthcare at 88% means AI answers are now the default research surface for medical queries. Combined with the citation-source migration documented in our freshness piece — generic health portals collapsing while authoritative institutions surge — the picture is a sector where the answer layer has been rapidly professionalized: high prevalence, high-stakes sourcing, and (per the QRG's YMYL expansion covered earlier) the strictest quality regime Google operates.
E-commerce at 4% — down from 29% — is the tell. Product and transaction queries are where Google monetizes directly, and AI Overviews have retreated from them almost completely. Google is not unable to answer "best running shoes"; it is choosing not to insert a synthesized answer between the query and the shopping results. Any strategist projecting "AI answers will take over everything" needs to reconcile that with a sector where the engine is visibly protecting its transactional core.
The middle of the table carries its own lesson: Education's 18%→83% climb (+361%) happened in a single year. Verticals are not drifting — they are being re-sorted, fast, and the sorting is not uniform.
The Overlap Spread: Where Your Rankings Stop Helping
The second vertical dimension is subtler: how much do AI Overview citations overlap with classic organic rankings? BrightEdge measured overall overlap at roughly 17% — the decoupling result we built an entire earlier piece on. But the industry split is the operational number: Healthcare 24% versus Finance 11%.
In healthcare, about a quarter of cited content also ranks organically — rank-adjacent work still transfers partially. In finance, barely one in ten citations comes from page-one content. A finance brand investing exclusively in ranking improvements is buying an asset that transfers to the answer layer at an 11% exchange rate. The same budget spent on citable-format content — comparisons, structured explainers, primary data — operates in the pool where finance citations actually originate.
Who Dominates — and Who Doesn't
Amsive's category-level visibility research adds the third dimension: concentration. In classic YMYL-adjacent categories, single authorities dominate AI answers: Amazon at 57.3% visibility in its category, Bank of America at 32.2%, Harvard at 20.8%, Mayo Clinic at 14.1%. Where trust is existential, the answer layer concentrates on incumbent institutions — consistent with everything the quality-guidelines data implies.
But the counter-finding is the strategically interesting one: smaller brands are anomalously loud inside LLM-native answers. Brands like Navy Federal and Upstart punch far above their classic-search weight in ChatGPT-class engines — a pattern consistent with LLMs rewarding distinctive, well-documented, heavily-mentioned entities over classic domain authority. The concentration is a Google-surface phenomenon; the dispersion is an LLM phenomenon. Both are true simultaneously, and they imply opposite strategies for the same brand depending on which surface dominates its category.
A final vertical wrinkle from Amsive's tracking: geographic bleed-through. Non-local queries ("best companies for life insurance") increasingly return local business profiles in AI Mode — meaning even national brands now need their office-level Google Business Profiles in order, because the answer layer is quietly re-localizing certain verticals.
What This Means: Four Playbooks, Not One
Collapsing the data into operating guidance:
1. YMYL research verticals (health, education, finance): assume the answer layer is the primary research surface (88%/83%/63% trigger rates). The game is citation-pool entry through authority: institutional sourcing, maintained content, earned media, and — per the finance overlap number — explicit investment in citable formats rather than rank-chasing. Quality-regime compliance is not optional; these are the verticals where the QRG bites hardest.
2. B2B and consideration verticals (tech, professional services): trigger rates above 80% make AI answers the top of every funnel. The exporter piece in this series covers the mechanics; the vertical addition here is that B2B's own-content advantage in the consideration stage (where brand-owned content earns 42–79% of citations, per BrightEdge's funnel research) makes owned comparison assets unusually high-leverage.
3. Local-influenced verticals (restaurants, services): 10%→78% trigger growth means the answer layer arrived suddenly in a sector that had barely optimized for it. Local entity hygiene — GBP completeness, review velocity, consistent hours and categories — is the immediate play, because the answers are increasingly assembling from local inventory.
4. Transactional e-commerce: the 4% trigger rate says AI Overviews are largely absent from the buying surface — but this is the vertical where the "portfolio, not replacement" framing matters most. AI-assisted research still happens upstream (comparison, sizing, "best for" questions), LLM-referred traffic converts at higher rates, and agentic shopping is the stated direction of every major platform. E-commerce teams should optimize the research layer while recognizing the transaction layer remains classic-search territory for now.
The Honest Caveats
Trigger rates are a surface metric. High prevalence does not equal high influence; a category with 88% trigger rates but low buyer reliance on answers behaves differently than the raw number suggests. Pair prevalence data with the downstream conversion evidence before reallocating budget.
Category concentration numbers come from one team's methodology. Amsive's visibility shares are rigorous but one measurement of a fast-moving target; treat the incumbent-dominance pattern as directional.
The e-commerce retreat is a policy choice that could reverse. Google's transaction-layer protection is a business decision, not a technical limit. Agentic commerce initiatives could bring AI answers back into the purchase path quickly — e-commerce teams should watch this surface, not write it off.
The Bottom Line
The most expensive strategic error in AI search right now is importing a playbook across verticals. The data shows industries spanning from 88% to 4% AI answer prevalence, from 24% to 11% rank-citation overlap, from incumbent-dominated to challenger-friendly answer layers — within the same search engine, in the same quarter. Healthcare and e-commerce are not two points on one curve; they are two different games played on one platform.
The teams winning the answer layer are the ones that started with their vertical's numbers: trigger rate first, overlap second, concentration third, local bleed-through fourth. "AI search strategy" is not a discipline. Four or five of them are hiding inside it.
Sources
BrightEdge AI Overviews one-year tracking (Feb 2025–Feb 2026, Generative Parser methodology): industry trigger rates (Healthcare 72%→88%, Education 18%→83%, B2B Tech 36%→82%, Restaurants 10%→78%, Insurance 17%→63%, E-commerce 29%→4%); citation-overlap-by-industry (Healthcare 24% vs Finance 11%); overall ~17% overlap
BrightEdge citation-source migration data (2025–2026): generic health portals down 77.9%/95.6%; authoritative institutions up 32.4%/83.2%/266.7%
BrightEdge funnel-stage research (8 industries): brand-owned content earns 42–79% of consideration-stage citations
Amsive / Lily Ray category visibility research (2025–2026): Amazon 57.3%, Bank of America 32.2%, Harvard 20.8%, Mayo Clinic 14.1%; challenger-brand overperformance in LLM-native answers (Navy Federal, Upstart); geographic bleed-through in AI Mode
Cross-context: this series' The Great Decoupling (overlap mechanics), The Anxiety Ledger (QRG/YMYL regime), The Invisible Supplier (B2B mechanics), The Vanishing Click (conversion evidence)
All figures are as of the tracking dates above; trigger rates move month to month — re-verify before locking annual plans.