AI Trust Scores for Contractors: 2026 National Report

The Discoverability Gap Is Not What You Think

63% of contractors across VerifiedNode's dataset of 58,000+ records score in the low tier for AI visibility. That is 36,604 businesses that AI-driven search tools, recommendation engines, and large language model citations will largely bypass when a homeowner asks for a contractor referral.

The instinct is to frame this as a technology problem. It is not. It is a discoverability problem, and the numbers show exactly where the breakdown occurs.

89% of contractors globally have a website. That means the issue is not web presence. Contractors built the digital footprint. They simply did not structure it in a way that AI systems can read, verify, and cite.

Only 10% of contractors have JSON-LD structured data on their pages. JSON-LD is the primary markup format that allows search engines and AI tools to parse business identity: service type, location, credentials, hours, reviews. Without it, a contractor's website is text that AI systems cannot reliably interpret as a trusted source.

The claim rate tells the same story from a different angle. Only 17% of contractor profiles are claimed across the platform. Unclaimed profiles cannot be updated, cannot be verified, and cannot accumulate the trust signals that AI ranking systems weight most heavily.

The tier breakdown shows how concentrated the problem is at the bottom:

The high tier represents a functional ceiling that only 6% of contractors have cleared. These are businesses with verified profiles, structured data, and sufficient review volume to register as credible signals in AI-assisted search. The remaining 94% occupy a spectrum where most of the meaningful differentiation happens in the medium tier, not at the top.

The revenue consequence is direct. When a homeowner uses an AI assistant to find a licensed electrician or a vetted roofing contractor, the system draws from structured, verified sources. A contractor with a website but no JSON-LD, no claimed profile, and a low score is not considered. They are not ranked low. They are absent from the result set entirely.

For a methodology overview and state-level breakdowns, see the State of the Market reports. To check where your business scores, visit /find.

What the Rankings Actually Show

The top 5 markets in VerifiedNode's dataset are all Canadian provinces. The bottom 5 are all large US metros. That pattern is not coincidental.

The gap between Manitoba at 41.7 and Vancouver and Dallas at 3.9 is not a marginal difference. It represents a structural divide in how contractors across these markets have built their digital presence. Canadian provincial markets, where contractor density is lower and competition for AI-visible positioning is less saturated, show higher average scores despite having no particular advantage in structured data adoption.

Market Size Works Against You

The relationship between total businesses and average score runs consistently inverse across the dataset.

Ontario has 11,095 contractors and scores 38.5. The Northwest Territories has 97 contractors and scores 38.9. The difference is small in absolute terms, but the direction is consistent: larger markets produce lower average scores. Alberta, with 3,600 businesses, ties Ontario at 38.5 despite having far fewer competitors.

The pattern in the bottom markets reinforces this. Chicago has 659 businesses and scores 4.1. Dallas has 598 and scores 3.9. These are markets where contractor volume is high, but average AI visibility is effectively near zero on a 100-point scale. More contractors competing in the same geography, without structured data or verified profiles, does not raise the floor. It keeps it low.

The Manitoba Outlier Problem

Manitoba's average score of 41.7 looks like a top performer until you examine the median.

The median score in Manitoba is 37.0. The average is 41.7. That 4.7-point spread indicates a small cohort of high-scoring businesses pulling the mean upward. Most Manitoba contractors cluster near 37, which is a fair-tier score, not an excellent one. Only 11.4% of Manitoba contractors reach the excellent tier.

The tier distribution confirms this: 382 of Manitoba's 673 contractors sit in the fair tier. 118 are in the poor tier. 77 reach excellent. The province leads the national rankings because its high performers are pulling harder than anywhere else in the dataset, not because its baseline is stronger.

JSON-LD Adoption Is a Near-Universal Failure

Alberta leads Canadian provinces on JSON-LD adoption at 7%. Globally, the average is 10%. Both numbers describe an industry that has not yet implemented the single most actionable signal for AI visibility.

Alberta's 7% would rank at the bottom of any industry that had broadly adopted structured data. In contractor markets, it ranks first among Canadian provinces. That is not a competitive advantage. It is an indication of how low the baseline sits across the entire vertical.

Manitoba's 2% JSON-LD adoption is particularly striking given its top ranking. The province leads the dataset on average score while having the lowest structured data adoption among the top markets. Its scores are driven by review volume (average 60.0 reviews per business) and website presence (72%), not by technical markup. That means Manitoba's top performers have a ceiling they have not yet approached.

Website presence tells a similar story from the opposite direction. Alberta's contractors have 82% website coverage, the highest among the top markets, yet only 7% have made those sites readable to AI systems through structured data. The infrastructure is there. The signal layer is missing.

For state-level contractor directories and market-specific data, see the Alberta contractor directory and Ontario contractor directory. The full 65-market comparison is available in the State of the Market reports.

Breakdown by State and Market

The tier distributions within top-ranked states reveal a consistent pattern: even the best-performing markets are mostly fair-tier, not excellent.

In every top-ranked market, the fair tier is the dominant category. Manitoba, ranked first nationally with an average score of 41.7, places 56.8% of its 673 contractors in the fair tier. Only 77 businesses reach excellent. Ontario's scale amplifies this: 6,245 contractors out of 11,095 sit in fair, and just 909 reach excellent, an 8.2% excellent rate across the largest market in the dataset.

The Northwest Territories presents a different problem. With only 97 total businesses, 7 reaching excellent represents 7.2%, a rate lower than Manitoba's 11.4% despite having far fewer competitors. Small market size does not automatically produce strong tier distribution. It produces a compressed sample where individual businesses have an outsized effect on aggregate figures.

Why Smaller Markets Outrank Large Metros

The bottom five markets by average score share one structural characteristic: high contractor density paired with low profile optimization.

Chicago's 659 contractors produce an average score of 4.1 on a 100-point scale. Dallas's 598 produce 3.9. These are not markets where contractors have optimized and still scored low. They are markets where a large share of profiles are unclaimed, unstructured, and unverified. Each additional unoptimized profile in the pool pulls the market average downward. More contractors, absent a corresponding increase in profile quality, does not raise the floor. It keeps it near zero.

Smaller markets have fewer profiles dragging the mean. When a market has 97 businesses (Northwest Territories) instead of 659 (Chicago), a single well-optimized contractor moves the average measurably. That is a mathematical effect, not a market quality signal.

Small-City Outliers Require Careful Interpretation

The top-performing cities within leading states illustrate this effect precisely.

Priddis, Alberta averages a score of 59.0 across 5 businesses. Ariss, Ontario averages 58.5 across 6 businesses. Navin, Manitoba averages 50.3 across 7 businesses. Each of these cities significantly outperforms its state average (Alberta: 38.5, Ontario: 38.5, Manitoba: 41.7).

Samples of 5, 6, and 7 businesses are not statistically stable. A single contractor with a score of 85 in a pool of 5 businesses shifts the city average by roughly 10 points. If that business reduces its review activity or loses verification status, the city ranking can move materially in the next reporting cycle.

For contractors comparing their own market performance, the relevant benchmark is the state median, not the top-city average. Manitoba's median score is 37.0. Ontario's is 35.0. Alberta's is 35.0. Those figures describe where most contractors in those markets actually sit. A small-city average of 59.0 reflects local outliers, not a replicable baseline.

This methodological point matters practically: if your city shows a high average score in a small market, your direct competitors may be fewer than five businesses. That concentration means capturing excellent-tier status is achievable with a targeted effort rather than a sustained campaign.

For market-specific hiring data, see the contractor hiring guides by state and city. Current directory data for leading markets is available in the Manitoba, Ontario, and Alberta directories.

Methodology

VerifiedNode calculates AI Trust Scores across 58,000+ contractor records using three weighted scoring categories that reflect how AI systems actually evaluate and cite businesses.

The three scoring categories:

Readability carries the heaviest weight because it most directly determines whether AI systems can parse and cite a contractor. A business can have a valid license and a consistent address and still be invisible to AI-generated recommendations if its website lacks structured data and its reviews are thin or outdated.

The four data sources:

• Google Business Profile: Review data, star ratings, and claimed status
• Public records databases: License and insurance verification
• Website crawl: JSON-LD presence, schema markup implementation, mobile readiness
• Review aggregation: Cross-platform review volume and recency signals

Scores are computed at the individual contractor level and then aggregated upward to city, state, and national averages. Each state record carries a computedAt timestamp, confirming that scores are recalculated on a recurring basis rather than captured as a one-time snapshot.

The four score tiers:

JSON-LD structured data sits inside the Readability category because its absence creates a specific and measurable exclusion mechanism. AI assistants and large language models use structured data to parse business identity: service type, location, credentials, hours, and reviews. A contractor without JSON-LD has a website that AI systems cannot reliably interpret as a verified, citable source. The consequence is not a lower ranking. It is removal from the result set.

The current dataset makes this gap visible. 89% of contractors globally have a website. Only 10% have JSON-LD. Only 17% have claimed their profile. The infrastructure exists. The signal layer that AI systems require to act on that infrastructure is missing for 90% of the market.

To check your own score, visit /find. State-level breakdowns are available in the State of the Market reports.

Frequently Asked Questions

What is a good AI visibility score for a contractor?

Based on VerifiedNode's scoring tiers across 58,000+ contractor records, a good score falls between 46 and 65, and an excellent score requires 66 or above. Only 6% of contractors globally reach the high tier. The top-ranked market in the dataset, Manitoba, averages 41.7, which sits in the fair tier. Most contractors, including those in leading markets, have not yet cleared the threshold where AI systems reliably cite them.

Which contractor market has the highest AI visibility score?

Manitoba ranks first among all 65 qualified markets with an average score of 41.7 across 673 businesses. The next closest markets are Northwest Territories at 38.9 (97 businesses) and Ontario at 38.5 (11,095 businesses). All five top-ranked markets are Canadian provinces, and none of them average above 42. The ceiling for the entire dataset reflects how early the industry is in structured digital optimization.

Why do large US cities score so low compared to Canadian markets?

Vancouver and Dallas both average 3.9, the lowest scores in the dataset, across 559 and 598 businesses respectively. Denver (4.0), Seattle (4.1), and Chicago (4.1) are similarly near zero on a 100-point scale. High contractor density paired with low profile optimization pulls market averages down: each unclaimed, unstructured profile in the pool reduces the mean. Canadian provincial markets have fewer total businesses, so a smaller number of optimized profiles carries more weight in the aggregate.

Do most contractors have websites?

89% of contractors globally have a website, so web presence is not the problem. The breakdown occurs at the signal layer: only 10% have JSON-LD structured data implemented on those sites. JSON-LD is the markup format AI systems use to parse business identity, services, and credentials. A contractor with a fully built website but no structured data is effectively unreadable to the AI tools that generate contractor recommendations.

How many contractor profiles are actually claimed?

Only 17% of contractor profiles across the VerifiedNode platform are claimed. Unclaimed profiles cannot be updated or verified, and they cannot accumulate the trust signals that AI ranking systems weight most heavily. Given that 63% of contractors already score in the low tier globally, the claim rate suggests that the majority of the market has not taken the first actionable step toward AI visibility.