The AI Visibility Gap Is Wider Than Most Plumbers Realize
63% of contractors across 58,000+ records in the VerifiedNode dataset score in the low AI visibility tier. That single figure defines the competitive landscape for plumbers in 2026: the majority are effectively invisible to AI-driven discovery tools, recommendation engines, and the growing share of consumers who rely on AI assistants to find local service providers.
The distribution leaves little room for optimism at the median level:
| Tier | Share of Contractors | Record Count |
|---|---|---|
| High Score | 6% | 3,562 |
| Medium Score | 31% | 17,697 |
| Low Score | 63% | 36,602 |
Only 6% of plumbing contractors in the dataset reach the high score tier. The medium tier captures 31%. Everyone else is operating without meaningful AI visibility.
The gap between top and bottom markets makes the stakes concrete. Manitoba leads all tracked markets with an average AI visibility score of 41.7 across 673 businesses. At the other end of the ranking, Vancouver and Dallas are tied at the bottom with average scores of 3.9 each, across 559 and 598 businesses respectively. That spread, from 3.9 to 41.7, represents roughly a 10x difference in average market visibility between the best and worst performing regions.
The underlying cause is largely structural. Across the full dataset, only 10% of contractors have JSON-LD structured data implemented on their websites. JSON-LD is one of the primary signals AI systems and search engines use to understand and surface local business information. Without it, even a plumber with strong reviews and a functional website presents an incomplete profile to automated discovery systems.
The website number tells a different story: 89% of contractors globally have a website. The infrastructure exists. The structured data layer does not.
For plumbers in markets like Vancouver or Dallas, this creates a specific and addressable problem. A competitor with a score in the medium or high tier, even with fewer reviews or a lower rating, will outperform them in AI-mediated referrals simply because their data is readable by the systems doing the routing.
You can check where your business currently stands at /find. The 2026 State of the Market report breaks down visibility trends by region across the full dataset.
Market-by-Market Breakdown: Top 5 vs. Bottom 5
The table below compares the top and bottom markets by average AI visibility score. JSON-LD adoption rates are included where available from the dataset.
| Market | Avg Score | Median Score | Total Businesses | JSON-LD Adoption | Website Adoption |
|---|---|---|---|---|---|
| Manitoba | 41.7 | 37.0 | 673 | 2% | 72% |
| Northwest Territories | 38.9 | 33.0 | 97 | 4% | 63% |
| Ontario | 38.5 | 35.0 | 11,095 | 5% | 75% |
| Alberta | 38.5 | 35.0 | 3,600 | 7% | 82% |
| Calgary | 4.2 | N/A | 628 | N/A | N/A |
| Chicago | 4.1 | N/A | 659 | N/A | N/A |
| Seattle | 4.1 | N/A | 551 | N/A | N/A |
| Denver | 4.0 | N/A | 593 | N/A | N/A |
| Dallas | 3.9 | N/A | 598 | N/A | N/A |
| Vancouver | 3.9 | N/A | 559 | N/A | N/A |
The contrast between top and bottom markets is not primarily a story about review volume or ratings. It reflects structural differences in how business data is formatted and published.
What the Median Scores Reveal
Average scores in top markets look relatively strong, but the median figures tell a more cautious story.
| Market | Avg Score | Median Score | Gap |
|---|---|---|---|
| Manitoba | 41.7 | 37.0 | 4.7 |
| Northwest Territories | 38.9 | 33.0 | 5.9 |
| Ontario | 38.5 | 35.0 | 3.5 |
| Alberta | 38.5 | 35.0 | 3.5 |
In every top-ranked market, the median score trails the average. Manitoba's median of 37.0 sits 4.7 points below its average of 41.7. Northwest Territories shows the largest gap: a median of 33.0 against an average of 38.9, a 5.9-point spread across just 97 businesses.
This pattern indicates that a small number of higher-scoring contractors are pulling the average upward, while the majority of businesses in each market cluster near or below the median. The typical plumber in Ontario is not scoring 38.5. They are scoring closer to 35.0, with a subset of well-optimized competitors raising the regional average.
That concentration matters for competitive positioning. In a market where most plumbers score in the low tier, a business that reaches the medium tier gains a disproportionate visibility advantage simply by moving above the regional median.
The Website-Without-Structure Problem
The 89% website adoption rate globally is not translating into AI visibility. Only 10% of contractors have JSON-LD structured data, and the top-ranked markets reflect this ceiling even at their best.
Manitoba leads nationally with a 41.7 average score, yet only 2% of its 673 plumbing businesses have JSON-LD implemented. Alberta, ranked 4th with an average of 38.5, has the highest structured data adoption among top markets at 7%, and also carries the highest website adoption rate at 82%. That correlation is meaningful: markets where contractors invest more in digital infrastructure tend to score higher across the board, even when absolute adoption numbers remain low.
The bottom markets are not failing because they lack websites. They are failing because their digital presence is unreadable to the automated systems that route service inquiries. A plumber in Dallas or Denver with a well-designed website, 50 Google reviews, and a 4.8 rating is still effectively invisible to AI discovery if their business data lacks structured markup.
This is the core of the AI visibility gap: the infrastructure investment has been made, but the translation layer between that infrastructure and AI systems has been skipped by the overwhelming majority of contractors.
Claimed Profiles and the Baseline Problem
Only 17% of contractors globally have claimed their profiles. That figure sits upstream of every other optimization metric. A contractor who has not claimed their profile cannot systematically manage the data signals that feed AI visibility scores, regardless of how good their reviews are or how functional their website is.
In bottom markets like Vancouver (average score 3.9, 559 businesses) and Chicago (average score 4.1, 659 businesses), low claim rates compound the structured data problem. Unclaimed profiles provide minimal structured signals, incomplete business information, and no pathway for ongoing optimization.
The plumber directory for Ontario and the Alberta plumber listings show how markets with higher average scores distribute across cities, and where the concentration of higher-performing contractors tends to cluster in smaller suburban markets rather than major urban centers.
Methodology
Data sourced from the VerifiedNode platform as of March 27, 2026. The dataset covers 58,000+ contractor records across the United States and Canada. AI visibility scores are computed from a combination of structured data signals, profile completeness, review volume, rating consistency, and digital infrastructure markers including JSON-LD and website presence. Tier classifications: high score (top tier), medium score (mid tier), low score (bottom tier). Rankings reflect average scores across all tracked businesses in each market at the time of computation.
Breakdown by State: Top Markets vs. Bottom Markets
The score gap between top and bottom markets is not explained by market size alone. Ontario has 11,095 tracked plumbing businesses and still ranks 3rd nationally with an average score of 38.5. Chicago has only 659 businesses and ranks 61st with an average score of 4.1. More contractors does not mean lower scores. The distribution of optimization practices within a market determines the outcome.
| Market | Rank | Avg Score | Total Businesses | Excellent % | Fair % | Poor % |
|---|---|---|---|---|---|---|
| Manitoba | 1 | 41.7 | 673 | 11.4% | 56.8% | 17.5% |
| Northwest Territories | 2 | 38.9 | 97 | 7.2% | 42.3% | N/A |
| Ontario | 3 | 38.5 | 11,095 | 8.2% | 56.3% | N/A |
| Alberta | 4 | 38.5 | 3,600 | 10.2% | 49.6% | N/A |
| Vancouver | 65 | 3.9 | 559 | N/A | N/A | N/A |
| Dallas | 64 | 3.9 | 598 | N/A | N/A | N/A |
| Denver | 63 | 4.0 | 593 | N/A | N/A | N/A |
| Seattle | 62 | 4.1 | 551 | N/A | N/A | N/A |
| Chicago | 61 | 4.1 | 659 | N/A | N/A | N/A |
Manitoba's excellent tier sits at 11.4%, the highest among all ranked markets. That means roughly 1 in 9 plumbing businesses in the province has achieved a score threshold that fewer than 6% of contractors reach globally. The province's 118 poor-tier businesses represent a drag on the average, but the concentration of better-optimized contractors in the excellent and good tiers lifts the regional mean above every other tracked market.
Why Smaller Canadian Markets Score Higher
The conventional assumption is that larger markets produce better-optimized contractors because competition is more intense. The data does not support this. Smaller Canadian markets benefit from a different dynamic: the baseline of local competitors is low enough that modest optimization efforts produce outsized relative scores.
Northwest Territories illustrates this directly. With only 97 tracked businesses, its average score of 38.9 ranks 2nd nationally. Its average review count is 11.0, compared to Manitoba's 60.0. Manitoba's contractors average more than five times as many reviews per business, yet Northwest Territories trails Manitoba by only 3.8 points. Review volume does not solely determine rank. Structural data quality and profile completeness are doing more work than review accumulation.
Alberta's average rating of 4.65 is the highest among the top four markets. Manitoba's average rating is 4.45. Despite the 0.20 rating gap, Manitoba outscores Alberta by 3.2 points on average AI visibility. Ratings matter, but they are one signal among several. Structured markup and profile completeness carry independent weight in the scoring model.
City-Level Bright Spots
Within the top-ranked states, the highest scores cluster in smaller cities, not provincial capitals or major urban centers.
| City | State | Avg Score | Total Businesses |
|---|---|---|---|
| Ariss | Ontario | 58.5 | 6 |
| Carp | Ontario | 54.3 | 20 |
| Navin | Manitoba | 50.3 | 7 |
| Sunnyside | Manitoba | 49.9 | 20 |
| Niverville | Manitoba | 47.6 | 7 |
Ariss, Ontario leads all tracked cities in the dataset with an average score of 58.5 across 6 businesses. Carp, Ontario follows at 54.3 across 20 businesses. Both are small communities outside major urban centers where a handful of well-optimized contractors are setting the local benchmark.
The same pattern holds in Manitoba. Navin's average of 50.3 comes from 7 businesses. Sunnyside reaches 49.9 across 20 businesses. These are not flukes of small sample sizes producing inflated averages: Sunnyside's 20-business sample is large enough to reflect genuine market-level optimization behavior.
The implication for contractors in Ontario and Manitoba is direct. In smaller markets, reaching the medium or excellent tier does not require outperforming thousands of competitors. It requires outperforming a handful of local businesses that, in most cases, have not implemented structured data at all.
For plumbers in bottom-ranked markets like Dallas or Seattle, the same logic applies in reverse: a dense competitive field where almost no one has implemented JSON-LD means that the first contractor to do so captures a disproportionate share of AI-mediated referrals.
How VerifiedNode Calculates AI Visibility Scores
VerifiedNode's AI Trust Score is built from 58,000+ contractor records across the United States and Canada, computed as of March 2026. Each score runs from 0 to 100 and is assigned to one of four tiers: poor, fair, good, or excellent.
The score draws from four primary data sources: Google Business Profile, public records databases, direct website crawls, and review aggregation across platforms. No single source dominates the calculation. The model weights signals across three categories.
Identity: 25 points
This category measures whether a contractor's core business information is consistent across sources. Scoring factors include business name consistency, address verification, and phone number matching. A plumber listed under three different name variations across directories will lose points here regardless of how strong their reviews are.
Legitimacy: 35 points
The largest single-category weight. Legitimacy scoring covers license status, insurance verification, Google Business Profile claim status, and public records cross-referencing. The 17% global claim rate is the most consequential figure in this category: 83% of contractors in the dataset have not claimed their profile, which limits what the model can verify and scores accordingly.
Readability: 40 points
The highest-weighted category, and the one with the widest optimization gap. Readability measures website crawlability, JSON-LD structured data presence, schema markup completeness, and review aggregation signals. This is where the 89% website adoption figure collides with the 10% JSON-LD adoption figure to produce the visibility gap that defines the 2026 landscape.
The table below summarizes the scoring structure:
| Category | Max Points | Primary Signals |
|---|---|---|
| Identity | 25 | Name consistency, address match, phone verification |
| Legitimacy | 35 | License, insurance, GBP claim status, public records |
| Readability | 40 | JSON-LD, schema markup, crawlability, review signals |
JSON-LD is the single largest gap between contractors who appear in AI-generated responses and those who do not. AI systems parse structured data to understand what a business does, where it operates, and how it is rated. Without JSON-LD, a website is readable to humans but largely opaque to the automated systems routing service inquiries. 89% of contractors have built the website. Only 10% have added the layer that makes it machine-readable.
That 79-point gap between website adoption and structured data adoption is the core technical explanation for why 63% of contractors score in the low tier despite having an established web presence.
Frequently Asked Questions
What percentage of plumbers have a low AI visibility score nationally?
63% of contractors across the VerifiedNode dataset of 58,000+ records score in the low tier. Only 6% reach the high score tier, representing 3,562 businesses out of more than 57,000 tracked. If you want to see where your business falls, check your score at /find.
Which market has the highest average AI visibility score for plumbers?
Manitoba ranks first among all tracked markets with an average score of 41.7 across 673 businesses. The next closest markets are Northwest Territories at 38.9 and Ontario at 38.5. The full regional breakdown is available in the 2026 State of the Market report.
Which markets have the lowest plumber AI visibility scores?
Vancouver and Dallas are tied at the bottom with average scores of 3.9 each, across 559 and 598 businesses respectively. Denver (4.0), Seattle (4.1), and Chicago (4.1) round out the five lowest-ranked markets. The gap between Manitoba's 41.7 and Vancouver's 3.9 is not primarily a reviews or ratings problem: it reflects structural differences in how contractor data is formatted and published.
Do most plumbers have a website?
89% of contractors globally have a website, so the problem is not website ownership. The gap sits at the structured data layer: only 10% of contractors globally have JSON-LD implemented. That 79-point difference between website adoption and structured data adoption is the primary technical reason most plumbers are invisible to AI discovery systems despite having an established web presence.
Why does Manitoba's median score differ from its average score?
Manitoba's median score is 37.0, sitting 4.7 points below its average of 41.7. That gap reflects a concentration effect: a subset of well-optimized contractors pulls the regional average upward, while the majority of businesses cluster near or below the median. The pattern holds across every top-ranked market. The typical plumber in Manitoba is not scoring 41.7. They are scoring closer to 37.0, which means reaching the average threshold is an achievable competitive move for most businesses in the province. See the full Manitoba plumber listings for city-level score distribution.