Research study · 4,330 businesses · Ontario 2026
A comparative study of 4,330 local businesses and how they are represented, surfaced and recommended in AI-driven discovery. Prepared by CLIXERA. Ontario, Canada | 2026.
Ottawa businesses analyzed: 2,178. Toronto businesses analyzed: 2,152. Total businesses analyzed: 4,330.
Artificial intelligence is becoming part of how consumers discover local businesses.
A person looking for a restaurant, dentist, gym, coffee shop or service provider no longer has to begin with a traditional search engine. They can ask an AI assistant a direct question and receive a relatively short set of suggestions.
That changes an important part of local discovery.
The challenge is no longer simply whether a business exists online or ranks in a search engine. Increasingly, another question matters: Does the business enter the consumer's AI-generated consideration set?
CLIXERA conducted the Battle of Ontario research study to examine that question.
The study analyzed 4,330 local businesses drawn from Business Improvement Area business data across Canada's two largest Ontario cities: 2,178 businesses in Ottawa and 2,152 businesses in Toronto.
The research examined business-level AI visibility, recommendation visibility, digital readiness and a separate citywide consumer-shopping experiment.
Overall AI visibility was remarkably similar between the cities.
When the analysis was restricted to recommendation-oriented questions, the two cities were identical: 29% of businesses in each city appeared in the recommendation scenarios tested.
The inverse is equally important. 59% of tested Ottawa businesses and 57% of tested Toronto businesses never appeared in the relevant AI responses measured.
A separate citywide shopper experiment produced a different result. A verified local Main Street business from the scored sample appeared in 57% of Ottawa shopper responses, compared with 19% in Toronto. Major chains or recognizable brands appeared in 71% of Ottawa responses and 33% of Toronto responses.
The study also identified a more fundamental digital-readiness issue. Among the broader businesses reviewed, 26% in Ottawa and 20% in Toronto either had no website identified in the public directory information or could not be confidently matched to a Google business listing.
These findings do not establish that AI intentionally favours large brands, nor do they demonstrate that appearing in an AI response causes a purchase.
They identify something more fundamental: Being a real, established local business does not automatically mean being represented in the AI-generated shortlist a consumer may see.
As AI becomes part of local discovery, that distinction may become increasingly important to businesses, BIAs, municipalities and economic-development organizations.
41%
Ottawa mention rate
43%
Toronto mention rate
29%
Recommendation rate (both cities)
4,330
Businesses analyzed
Local business discovery has continually evolved.
Consumers once depended heavily on physical storefronts, word of mouth, newspapers, telephone directories and local advertising.
The internet changed that behaviour. Websites, search engines, Google Maps, reviews, social media and online directories became important intermediaries between a consumer's need and the business ultimately selected.
AI introduces another potential change. Traditional search generally asks the consumer to evaluate a collection of results. AI can perform part of that selection process before the consumer reaches a business website.
A person can ask: Where should I eat in Ottawa? Which dentist would you recommend in Toronto? Where should I get coffee? Who should I use for auto repair? The resulting answer may contain only a small number of businesses.
This creates a different competitive environment. A business that appears on page one, page two or page three of a traditional search still has some degree of visibility. A business excluded from a short AI-generated answer may never enter that particular consumer's initial consideration set.
This study examines that emerging layer of local discovery. It does not assume AI is replacing traditional search. Instead, it asks how local businesses are represented when AI becomes part of the process consumers use to discover and evaluate their options.
The study began with one central question: When consumers ask AI for local businesses, which businesses actually make the answer?
From that question, the research examined four related issues.
These are deliberately different measurements. A business being known to an AI system is not necessarily the same as being mentioned. Being mentioned is not necessarily the same as being recommended. And being recommended does not establish that a consumer ultimately chooses the business.
The study focuses on the earlier stage: Does the business enter consideration?
The study analyzed 4,330 businesses across Ottawa and Toronto.
Businesses were sourced from publicly available Business Improvement Area business information across the two cities.
BIA-based data was selected because the research was specifically interested in local commercial districts and Main Street businesses.
The objective was not to build a database dominated by Canada's largest chains. The objective was to examine the digital environment faced by independent and local businesses operating in real commercial communities.
The same general research framework was applied to both cities. The resulting study should be understood as an analysis of the defined business populations reviewed. It is not a census of every operating business in either Ottawa or Toronto.
2,178
Ottawa businesses analyzed
2,152
Toronto businesses analyzed
4,330
Total businesses analyzed
CLIXERA examined local-business discovery across several stages rather than treating AI visibility as a single measurement.
This framework allowed the research to distinguish between simply existing online and actually entering an AI-generated answer.
At the centre of CLIXERA's digital-evidence analysis were three basic questions.
Who are you? Can the business be clearly identified? Is its name and identity consistent across public sources?
What do you do? Can someone clearly determine the products, services or specialties the business provides? Does the website explain what the business should reasonably be considered for?
Where are you? Is the physical location or service area clear? Can the business be confidently associated with the city and community it serves?
These questions are intentionally simple. A sophisticated AI system still depends on information. If the public digital footprint surrounding a business is incomplete, inconsistent or ambiguous, understanding that business becomes more difficult.
The research therefore considered publicly available evidence including websites, Google business presence, business listings, reviews, location information, service descriptions and third-party references.
This analysis does not claim that any individual factor guarantees inclusion in an AI response. The purpose is to examine the digital evidence available to represent the business.
The first primary measurement was Mention Rate. Mention Rate represents the proportion of tested businesses that appeared at least once in a relevant ChatGPT response.
The difference between the two cities was small. The more significant observation is that fewer than half of the tested local businesses in either city were observed in the relevant AI responses.
This led to the corresponding Shut Out Rate. A business classified as shut out was not observed in any of the relevant responses included in the defined test.
This does not mean ChatGPT cannot identify the business or that it will never appear in another response. It means that under the questions and testing conditions used for this research, the business was not surfaced.
41%
Ottawa mention rate
43%
Toronto mention rate
59%
Ottawa shut-out rate
57%
Toronto shut-out rate
CLIXERA separately measured what happened when AI was asked to recommend or help choose a business.
This is different from a general mention. A business may be referenced in informational content without being included when the consumer expresses a stronger intent to choose a provider.
The observed Recommendation Rate was identical in both cities. Fewer than three in ten tested businesses appeared in the recommendation-oriented scenarios measured.
This distinction between being present online and being selected for consideration is central to the study.
29%
Ottawa recommendation rate
29%
Toronto recommendation rate
Business-level visibility tells only part of the story. CLIXERA therefore conducted a separate experiment designed to resemble the way an ordinary consumer might use AI.
Seven fixed questions were used. Each question was run three times for each city. Only the city was changed. This produced 21 shopper responses for Ottawa and 21 shopper responses for Toronto.
The purpose was not to estimate the outcome of every possible AI query. It was to observe who occupied recommendation space when the AI was given broad consumer questions rather than being directed toward a particular business.
The shopper experiment produced one of the largest observed differences between Ottawa and Toronto.
CLIXERA measured how often an answer included at least one verified local Main Street business from the city's scored sample.
Ottawa's local businesses entered the citywide shopper answers considerably more often in this experiment.
This result is particularly interesting because Toronto had a slightly higher overall business-level Mention Rate. The two measurements therefore capture different aspects of AI visibility.
Toronto businesses were slightly more likely to appear somewhere in the broader business-level testing. Ottawa businesses were considerably more successful at entering the specific citywide shopper answers tested.
Because the shopper experiment contained 21 responses per city, these results should be treated as observed experimental rates rather than population estimates for all AI searches conducted in Ottawa or Toronto.
57%
Ottawa local shortlist rate
19%
Toronto local shortlist rate
The same shopper responses were examined for major chains and recognizable brands.
These categories can overlap. An AI response may include both an independent local business and a major brand. The percentages therefore should not add to 100%.
The important observation is that major brands occupied substantial recommendation space in both markets. In Ottawa, they appeared in more than seven out of ten shopper responses tested.
This does not demonstrate that the AI system intentionally favours large companies. It raises a different question: What advantages might larger organizations have when an AI system is attempting to identify businesses it can confidently recommend?
57%
Ottawa local Main Street appeared
19%
Toronto local Main Street appeared
71%
Ottawa major brand appeared
33%
Toronto major brand appeared
Large organizations often have fundamentally different digital footprints from independent businesses.
An independent business may have a website, a Google business profile, several listings and a smaller collection of reviews.
Both may be excellent businesses. But the amount of public information available to describe them can be dramatically different.
This creates what we describe as an information advantage.
AI systems do not experience Main Street physically. They cannot see a busy restaurant, watch customers enter a store or know that residents have trusted a particular business for 20 years unless useful evidence of that business exists in the digital environment available to them.
Large brands often enter that environment with considerably more evidence. That is different from claiming AI has an intentional preference for large corporations.
Local commerce also introduces a geographic issue. A consumer may consider "local" to mean nearby. An AI system does not necessarily apply the same neighbourhood boundary.
A request for a local service can produce businesses within the immediate neighbourhood, elsewhere in the city, in another commercial district, from a regional operator, or from a national chain.
The recommendation may still satisfy the consumer. But from the perspective of a BIA or local economic-development organization, the outcome can be different. A consumer looking for a service available within the neighbourhood may ultimately spend that money elsewhere.
The current study does not quantify the economic value of this displacement and should not be interpreted as proving that AI is causing spending to leave Main Street.
It does identify a question that warrants further study: When consumers ask AI for something available within their community, how often does the resulting recommendation keep that consumer in the community?
AI Mention Rates varied by business category.
Toronto recorded the higher observed rate in each of the four categories. The differences, however, were not uniform.
Food & Drink differed by only one percentage point. Shopping differed by two. Larger observed differences occurred in Health & Wellness and Professional Services.
These category results should be interpreted alongside the number of eligible businesses within each category rather than assuming that every observed percentage difference represents a statistically meaningful difference between the cities.
| Category | Ottawa | Toronto |
|---|---|---|
| Food & Drink | 34% | 35% |
| Health & Wellness | 52% | 63% |
| Shopping | 31% | 33% |
| Professional Services | 41% | 53% |
The study also examined businesses that could not progress cleanly through the digital analysis because basic online foundations were missing.
Businesses were classified in this group when either no website was identified in the public directory data, or the business could not be confidently matched to a Google business listing.
These percentages are not AI visibility scores. They identify a separate digital-readiness issue. Nor does the research establish that missing one of these foundations caused a business to be absent from an AI answer.
The significance is more fundamental. AI discovery is developing while a meaningful portion of local businesses are still addressing earlier generations of digital visibility.
For some businesses, the challenge is not yet sophisticated AI optimization. It is establishing a clear, consistent and verifiable digital presence in the first place.
26%
Ottawa missing basic digital foundations
20%
Toronto missing basic digital foundations
The findings point toward a distinction that may become increasingly important in local commerce.
CLIXERA describes it as the AI Consideration Gap: The difference between the businesses that exist and serve a market and the businesses that enter an AI-generated consumer consideration set.
This is not the same as search ranking. It is not the same as website traffic. And it is not proof of a lost sale. It describes an earlier point in the customer journey.
Consider a simplified path:
If a business does not enter the shortlist, it may never reach the later stages of that particular decision. The competitive battle may therefore begin before the consumer visits the business's website.
The findings do not suggest that businesses should abandon SEO, Google, websites, reviews or listings in favour of an entirely new form of "AI optimization." The opposite may be more useful.
AI systems need digital evidence from which to understand businesses. That makes the fundamentals increasingly important.
A local business should be represented online in a way that clearly answers: Who are you? What do you do? Where do you do it?
That requires accurate business information, clear website content, understandable services, consistent listings, authentic reviews, strong location information and credible references from other sources.
The objective should not be to manipulate an AI system. The objective is to ensure that the digital representation of the business accurately reflects the real business.
The implications extend beyond individual businesses.
BIAs, chambers of commerce, municipalities and economic-development organizations have spent years helping businesses become digitally capable. Those efforts have included websites, e-commerce, social media, online listings, reviews and digital advertising.
AI introduces another consideration: Can the digital ecosystem clearly understand the businesses within a community?
A commercial district can be physically vibrant while individual businesses within it remain digitally difficult to identify or understand.
That suggests future digital-readiness programs may need to consider not only whether a business is online, but whether its online information is complete, consistent, understandable, geographically clear, and supported by credible public evidence.
This matters because local business visibility has an economic dimension. If consumers increasingly use AI to create their initial shortlist, exclusion from that shortlist could become another competitive disadvantage for independent businesses.
AI is not the cause of the challenges facing Main Street. Commercial rents, labour costs, e-commerce, changing commuting patterns, consumer behaviour and many other forces have reshaped local retail and services.
AI should instead be understood as another emerging layer within that environment.
The Battle of Ontario was created to make the research accessible to a broader audience.
Seven observed metrics were converted into a simple head-to-head competition. One point was awarded for a higher observed rate. A tie awarded half a point to each city.
The Battle scoring system is a communications framework created by CLIXERA for this project. It is not a statistical index or an industry-standard measure of AI visibility.
Small differences in observed percentages should not automatically be interpreted as statistically significant. The underlying measurements, rather than the Battle score, are the research findings.
1.5
Ottawa final score
5.5
Toronto final score
| Contest | Ottawa | Toronto | Observed Winner |
|---|---|---|---|
| Overall Visibility | 41% | 43% | Toronto |
| Recommendation Rate | 29% | 29% | Tie |
| Local Shortlist | 57% | 19% | Ottawa |
| Food & Drink | 34% | 35% | Toronto |
| Health & Wellness | 52% | 63% | Toronto |
| Shopping | 31% | 33% | Toronto |
| Professional Services | 41% | 53% | Toronto |
FINAL SCORE: Ottawa 1.5 — Toronto 5.5. Toronto wins the Battle of Ontario.
This research should be interpreted within its defined scope.
It is a point-in-time study. AI systems and the public information available about businesses continually change.
AI responses are probabilistic. Repeating a question can produce different businesses or different ordering.
The business population is BIA-based. The study does not represent every business operating in Ottawa or Toronto.
Mention does not equal purchase. The research measures inclusion in AI responses, not subsequent consumer transactions.
Association does not establish causation. The existence of reviews, listings, website content or other digital evidence does not prove that any one factor caused an AI system to mention a business.
Major-brand visibility does not establish algorithmic bias. The research observed major brands appearing frequently. It does not establish that an AI model is intentionally designed to favour them.
The citywide shopper experiment has a limited base. It contains 21 responses per city and should be interpreted as a controlled exploratory experiment rather than a population estimate.
These limitations are important because AI visibility research is still an emerging field. The objective of this study is to report what was observed under defined conditions rather than claim certainty about how every AI system will behave.
CLIXERA used a combination of structured business information, public web evidence and AI analysis in conducting the research.
GPT-6 Astra was used as an analytical layer within the CLIXERA research workflow to assess and synthesize this public digital evidence.
The consumer-facing recommendation tests and the analytical evidence layer should be understood as separate parts of the research methodology.
For reproducibility and interpretation, the final technical record of the study should document the exact model/version used for each testing layer, collection dates, prompt set, inclusion rules, deduplication methodology and classification procedures.
The Battle of Ontario began with a simple question: When people ask AI for local businesses, who actually gets mentioned?
Across 4,330 Ottawa and Toronto businesses, the answer was not simply a contest between two cities. It revealed a larger change in local discovery.
A business can exist physically in a community. It can have customers. It can have a website. It can appear in Google. And it can still be absent when an AI system creates a shortlist.
Large brands enter this environment with substantial information advantages, while some independent businesses are still missing fundamental pieces of their digital identity.
That does not mean AI will replace Google, nor does it mean AI is responsible for the economic challenges facing Main Street. It means another gate to consumer consideration is emerging.
For businesses, the practical challenge is increasingly to ensure that their digital presence accurately and consistently communicates who they are, what they do and where they operate.
For BIAs, municipalities and economic-development organizations, there is a larger question worth watching: When a resident asks AI where to spend their next dollar, will the answer lead them to Main Street?
Research: Battle of Ontario: AI Edition. Prepared by CLIXERA. Year: 2026. Markets: Ottawa and Toronto, Ontario.
Business source: Public BIA business information. Primary areas studied: AI visibility, recommendation visibility, local shortlist presence, digital readiness and competitive visibility.
Citywide shopper questions: 7 per city. Runs per question: 3. Citywide shopper responses: 21 per city.
4,330
Businesses analyzed
2,178
Ottawa
2,152
Toronto
21
Shopper responses per city
AI doesn't walk down Main Street. It can only understand the Main Street the internet shows it. — CLIXERA Research | 2026
