AI Trust Scores: Canada Leads the U.S. in 2026

Key Finding

Only 2% of contractors across 58,000+ businesses tracked by VerifiedNode achieve a high AI Trust Score. That single figure captures the scale of the visibility gap shaping how AI-powered search surfaces home service providers in 2026.

The distribution is sharply skewed. The low score tier holds 482,361 businesses: 136% more contractors than the high tier's 2,557. The medium tier accounts for just 9,200 businesses, or 3% of the total. For the overwhelming majority of contractors, AI-driven discovery is effectively nonexistent.

Infrastructure explains much of the problem. Only 23% of contractors globally have a website. Only 2% have JSON-LD structured data, the markup format that allows AI systems and search engines to parse and surface business information reliably. Without it, even a contractor with strong reviews is largely invisible to the tools homeowners increasingly use to find and vet service providers.

The market-level data makes the gap concrete.

Alberta's average score of 41.7 is more than ten times Vancouver's 3.9. That is not a marginal difference in optimization quality. It reflects a structural gap in how contractors in those markets have built their digital presence.

The Canada-versus-U.S. tension is the defining pattern in this dataset. Three Canadian provinces hold the top three positions across 65 qualified markets. Five of the six lowest-ranked markets are major U.S. metros, including two tied at 3.9.

Vancouver, despite sharing a country with Alberta, ranks last. Calgary (avg score: 4.2, rank 60) sits five spots from the bottom. Canadian provinces outperform Canadian cities, which suggests that smaller, less saturated markets are driving provincial averages up while dense urban markets drag scores down on both sides of the border.

For contractors sitting at the median, the position is worse than average suggests. The median score across tracked markets is concentrated in the fair tier, meaning most businesses have a website but lack the structured data, review volume, and verification signals that AI systems weight most heavily.

Check where your business stands at /find, or review the full state-level breakdown at /resources/state-of-the-market/.

Data Analysis

The top-versus-bottom comparison becomes sharper when you add the infrastructure signals alongside the scores.

The top three provinces share a consistent profile: high website penetration, low JSON-LD adoption, moderate review counts. Alberta leads at 82% website coverage and 7% JSON-LD. British Columbia follows at 81% and 5%. Manitoba trails on websites at 71% and JSON-LD at 3%.

Those JSON-LD figures are low in absolute terms, but they are still multiples above the global average of 2%. That gap is enough to produce the score separation visible at the top of the rankings.

Canadian Provincial Dominance

Alberta's average score of 41.7 is built on a skewed tier distribution. Of its 3,600 tracked businesses, 2,100 sit in the fair tier (58.3%) and 598 in the poor tier. Only 366 qualify as excellent (10.2%) and 536 as good (14.9%). The provincial average looks strong not because most contractors are well-optimized, but because the floor is higher than in competing markets.

British Columbia shows an almost identical shape: 1,999 businesses in the fair tier (60.7%), 514 in poor, 277 excellent (8.4%), 505 good (15.3%). Manitoba's distribution mirrors the same pattern at smaller scale: 398 fair (55.8%), 137 poor, 79 excellent (11.1%), 99 good (13.9%).

The medians confirm this: Alberta and Manitoba both sit at a median score of 37. British Columbia's median is 38. These are fair-tier scores, not good or excellent. Provincial rankings reflect a situation where the top performers pull averages up while the majority remain underoptimized.

New York breaks the pattern in one key metric: its contractors average 104 reviews, the highest of any market in this dataset. Yet its average score is only 40.1 and its excellent rate is 0.6%, with 76.3% of businesses landing in the fair tier. Review volume alone is not sufficient for AI visibility without the structural signals that accompany it.

The Suburban Advantage

The city-level data within top provinces reveals a consistent pattern: smaller suburban markets outperform urban cores by a significant margin.

Priddis, Alberta averages 59.0 across just 5 businesses. Rocky View, Alberta averages 52.7 across 6 businesses. North Vancouver, British Columbia averages 56.3 across 13 businesses. Cochrane, Alberta averages 47.7 across 119 businesses.

Each of these markets sits well above their provincial averages of 41.7 and 41.4 respectively. The explanation is market density: in smaller markets, individual contractors face less competition for AI visibility signals, and the businesses operating there tend to be more established locally with stronger review profiles relative to their footprint.

Calgary, by contrast, averages 4.2 despite being located within Alberta's borders. Its 628 businesses compete in a saturated urban environment where undifferentiated digital presence produces scores indistinguishable from Chicago or Dallas.

The Bottom Market Problem

The bottom six markets share a structural characteristic: all are large metros with contractor populations between 508 and 659 businesses. Size and competition compress scores downward.

Vancouver (3.9, 559 businesses) and Dallas (3.9, 598 businesses) are tied at the bottom despite being in different countries and economic contexts. Denver (4.0), Seattle (4.1), and Chicago (4.1) cluster at the same level. In each of these markets, the volume of contractors creates noise that suppresses individual scores when most businesses lack differentiated signals.

For contractors in these markets, the math is straightforward: a website puts you in the top 23% globally, and JSON-LD structured data puts you in the top 2%. Both steps are table stakes in markets where the average score is below 5.0.

Browse contractor profiles in your vertical at /contractors/, or review province-level reports for Alberta and British Columbia for deeper market context.

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Methodology note: All scores computed from VerifiedNode's dataset of 58,000+ contractors as of April 10, 2026. AI Trust Scores aggregate structured data presence, website signals, review count, rating, and verification status into a single composite score. Rankings cover 65 qualified markets with sufficient business volume for statistical comparison. Market-level n/a values indicate infrastructure data not available in the current dataset extract.

Breakdown by State and Market Size

The city-level data exposes what provincial averages obscure. Alberta ranks 1st among 65 markets with an average score of 41.7, but Calgary, the province's largest city, sits at rank 60 with an average score of 4.2. The same province. A ten-fold difference in score.

That gap is a direct product of business density. Calgary's 628 tracked contractors compete in a saturated urban market where the majority lack differentiated digital signals. The provincial average of 41.7 is driven not by Calgary, but by the smaller suburban markets surrounding it.

City-Level Leaders: Alberta and British Columbia

Priddis averages 59.0 across 5 businesses. Rocky View averages 52.7 across 6. North Vancouver reaches 56.3 with just 13 businesses. These are not flukes. In each case, a small business count means that individual reputation signals, review volume relative to competition, and structured data presence carry disproportionate weight. One well-optimized contractor in a five-business market moves the average materially.

The pattern holds at moderate scale too. Cochrane, Alberta reaches 47.7 with 119 businesses, sitting 6 full points above the provincial average. Victoria, BC reaches 45.3 with 146 businesses. Even at those sizes, local market concentration produces scores that large metros cannot match.

Top Markets vs. Bottom Markets

The bottom 10 markets all share a defining characteristic: business counts between 476 and 697, combined with scores that cluster between 3.9 and 5.0. San Diego, the largest market in the bottom 10 with 697 businesses, averages only 4.8. Chicago, the largest market in the bottom 5 with 659 businesses, averages 4.1.

Portland ranks 56th with a score of 5.0, the best of the bottom 10. That is still 36.7 points below Alberta's provincial average. The entire bottom tier is operating in a different category.

What Large-Metro Contractors Are Missing

The structural deficits in bottom markets are readable in the global infrastructure numbers. Only 23% of contractors globally have a website. Only 2% have JSON-LD structured data.

Alberta's 7% JSON-LD penetration is 3.5 times the global rate. British Columbia's 5% is 2.5 times the global rate. Those multiples are small in absolute terms, but they are enough to generate scores an order of magnitude above the competition.

In Chicago, Seattle, Dallas, and Vancouver, the density of contractor listings creates noise. When 600-plus businesses compete in a single market and the vast majority share the same thin digital footprint: no JSON-LD, no verified profile, review counts that don't stand out relative to peers, scores compress toward the floor. Individual contractors become functionally indistinguishable to AI systems parsing local search signals.

Three specific gaps explain most of the score deficit in large metros:

• JSON-LD absence: Without structured data markup, AI systems cannot reliably parse business type, service area, or category. A contractor with 200 reviews and no JSON-LD is harder to surface than a competitor with 40 reviews and proper markup.
• Review volume relative to competition: New York's contractors average 104 reviews, the highest in this dataset, yet the market averages only 40.1 and carries a 0.6% excellent rate. In dense markets, review volume only matters when it creates clear separation from competitors. Uniform review counts across hundreds of businesses neutralize the signal.
• Unverified profiles: Verification status is a direct input to AI Trust Scores. In markets where most contractors have not claimed or verified their business profiles, the signal is absent across the board rather than selectively disadvantageous.

For contractors in bottom-tier markets, the path to visibility does not require outranking hundreds of competitors on every dimension. It requires exceeding the local baseline on the three signals most competitors have ignored entirely.

Review the full province-level data for Alberta and British Columbia, or check your own score at /find.

Methodology

VerifiedNode calculates AI Trust Scores across 58,000+ contractor records using a three-pillar framework. Each business receives a composite score built from verified data sources, then those individual scores are aggregated to produce city and state-level averages. The rankings in this report cover 65 qualified markets evaluated as of April 10, 2026.

Scoring Pillars

Readability carries the highest weight because it determines whether AI systems can parse and surface a business at all. A contractor with strong identity and legitimacy signals but no structured data is largely unreadable to the tools driving discovery in 2026.

Data Sources

Scores draw from four inputs: Google Business Profile (identity and review signals), public records (license and registration verification), website crawl (structured data presence, schema markup, page accessibility), and review aggregation (count, recency, rating distribution across platforms).

Each source feeds into one or more of the three pillars. No single signal determines a score. Missing inputs register as absent rather than neutral, which is why unverified profiles and websites without JSON-LD suppress scores rather than simply failing to help them.

Why JSON-LD Matters for AI Visibility

JSON-LD is the structured data format used by ChatGPT, Perplexity, Google SGE, and similar AI-powered discovery systems to identify business type, service category, and geographic coverage. Only 2% of contractors globally have it. That figure is the primary driver of AI invisibility across the dataset.

Website presence is the baseline gap: only 23% of contractors globally have one. JSON-LD is the layer above that. Alberta's 7% JSON-LD penetration, 3.5 times the global rate, is a meaningful factor in its rank 1 position. Without both signals in place, AI systems have no reliable mechanism to categorize a contractor and surface them in relevant queries.

Score Distribution and Tiers

Scores are computed per business and fall into four tiers: poor, fair, good, and excellent. The high tier contains 2,557 businesses globally, representing 1% of the dataset. The low tier contains 482,361 businesses, or 136 times the high tier count. The medium tier holds 9,200 businesses, or 3% of the total.

The median review count across Alberta, British Columbia, and Manitoba is 0.0. Provincial averages of 65, 50, and 58 reviews respectively are pulled by a small number of high-review businesses. Most tracked contractors in even the top-ranked provinces have no verified reviews on record.

Researchers and journalists can reference this methodology directly. Score inputs, tier thresholds, and market qualification criteria are consistent across all 65 ranked markets in this cycle. The full dataset methodology is documented at /resources/state-of-the-market/.

FAQ

Which province or state ranks highest for AI visibility in 2026?

Alberta ranks 1st out of 65 qualified markets with an average AI Trust Score of 41.7 across 3,600 tracked businesses. British Columbia (41.4) and Manitoba (41.1) hold the next two positions. All three are Canadian provinces, making Canada the dominant region at the top of the global rankings.

What percentage of contractors actually have a high AI Trust Score?

Only 1% of contractors globally, meaning 2,557 businesses out of the full dataset, reach the high score tier. The low score tier holds 482,361 businesses, 136 times more than the high tier. For the overwhelming majority of contractors, AI-driven discovery produces no meaningful visibility.

Why do Canadian provinces outrank major U.S. cities?

Infrastructure adoption explains most of the gap. Alberta has 82% website penetration and 7% JSON-LD adoption. Globally, only 23% of contractors have a website and only 2% have JSON-LD. Canadian provinces consistently exceed those global baselines, while large U.S. metros like Vancouver (3.9, rank 65) and Dallas (3.9, rank 64) cluster near the floor with thin digital footprints spread across hundreds of competing businesses.

Does having more reviews guarantee a higher AI Trust Score?

No. New York's contractors average 104 reviews, the highest of any market in this dataset, yet the market's excellent rate is only 0.6% and 76.3% of businesses sit in the fair tier. Alberta, with an average of 65 reviews per business, produces an excellent rate of 10.2%. Review volume contributes to score, but structured data presence and verification signals determine whether AI systems can read and surface a business in the first place.

How does my city's score compare to the national average?

The 65-market dataset shows scores ranging from 3.9 (Vancouver and Dallas, tied last) to 41.7 (Alberta). The median score across top-ranked provinces sits in the fair tier, around 37 to 38 points. If your business is in a large metro with 500-plus tracked competitors, your local baseline is likely below 5.0. Check your individual score at /find and review province or state-level context at /resources/state-of-the-market/.