AI & marketing
AI in digital marketing: what Canadian brands are actually doing.
AI use among Canadian businesses has risen fast, and marketing is one of the first places it shows up. The results are more mixed than the headlines suggest. Here's what the data says, where AI earns its place, and where it quietly costs you.
Every marketing conversation in Toronto seems to land on AI at some point. Teams feel pressure to use it, agencies are adding it to every proposal, and founders want to know whether they're falling behind.
The honest answer is that most businesses are still early, the gains are real but uneven, and the brands getting value are the ones treating AI as a tool for a specific job. This piece pulls together the most useful recent data, with an eye on Canada where we can find it.
How many Canadian businesses use AI?
The best Canadian source is the quarterly Canadian Survey on Business Conditions. According to Statistics Canada's October 2026 analysis, 19.2% of businesses said in the second quarter of 2026 that they had used AI to produce goods or deliver services over the previous 12 months. A year earlier, that figure was 12.2%.
Intentions are rising faster. In the third quarter of 2026, 25.2% of businesses said they plan to use AI over the next 12 months, up from 14.5% in 2025 and 10.6% in 2024. Mid-sized firms moved most: planned use among businesses with 20 to 99 employees jumped from 15.0% to 32.8%. Professional, scientific and technical services, which includes marketing services, led all industries at 54.8%.
The same survey shows what businesses plan to use AI for. Marketing is clearly on the list.
| Planned AI application (Q3 2026) | Share of businesses planning to use AI |
|---|---|
| Data analytics using AI | 41.7% |
| Large language models | 39.3% (up from 9.3% in 2024) |
| Virtual agents or chatbots | 31.8% |
| Text analytics using AI | 29.0% |
| Marketing automation using AI | 23.4% |
| Recommendation systems | 17.1% |
Source: Statistics Canada, Canadian Survey on Business Conditions, third quarter of 2026. Figures are self-reported.
People are moving faster than their employers. KPMG in Canada's 2025 Generative AI Adoption Index found that 51% of Canadian employees use generative AI at work, up from 22% in 2023. Only 29% said their employer has a comprehensive AI policy. In practice, that means a lot of AI is already touching your marketing whether or not anyone planned it.
What marketers are actually doing with it
Marketing-specific surveys are mostly global or US-weighted, so read them as directional for Canada.
- Adoption is high, depth is not. Salesforce's 2026 State of Marketing report, based on 4,450 marketers surveyed in late 2025, found 75% have adopted AI. Yet 84% still run generic campaigns, and 98% hit barriers to personalization, most often because of data.
- A real minority is still holding back. A Gartner survey of 418 marketing leaders, reported by Marketing Dive in 2025, found 27% had limited or no generative AI adoption in their campaigns. Among adopters, 47% reported a large benefit for campaign evaluation and reporting.
- Content creation leads. HubSpot's 2026 State of AI in Marketing data, from 1,754 marketers, shows 80% use AI for content creation. Only 10% describe their AI maturity as "transformational."
The pattern is consistent. Almost everyone has started. Few have rebuilt how they work around it.
Where AI genuinely helps
Creative iteration
AI is very good at producing first drafts and variations: ten headline angles, five versions of an ad for different audiences, a rough storyboard. That makes testing cheaper. The strategy behind the message still needs to come from people who understand the customer.
Analysis and reporting
Summarizing survey responses, reviews, call notes and campaign reports is where many teams see the quickest return. It's also where nearly half of Gartner's adopters reported a large benefit. AI speeds up the reading. Deciding what the numbers mean is still your job.
Personalization
AI makes it practical to tailor messages by segment, stage or channel. The catch, as the Salesforce data shows, is that personalization depends on connected, clean customer data. Without it, AI just produces more generic content faster.
Operations
Tagging assets, drafting briefs, cleaning lists, building reports and routing leads are repetitive tasks that AI and automation handle well. Our guide to what to automate in marketing covers how to choose.
Where AI fails
Brand sameness
When every competitor uses the same tools with similar prompts, content converges. In HubSpot's 2026 data, 53% of marketers said they struggle to differentiate their content, and 52% said AI has made content so easy to create that it's less effective overall. Audiences notice too. In a 2024 NielsenIQ study covered by Marketing Dive, viewers rated AI-generated video ads as more "annoying," "boring" and "confusing" than traditionally made ones.
Canadian consumers are split. Leger's 2026 AI Index found 57% find AI-generated advertising acceptable, but 34% would be less likely to buy from a brand that uses AI-generated content, while 12% would be more likely.
Hallucination and accuracy
Generative AI can state wrong things with confidence. In KPMG's 2025 Canadian research, 58% of employees were extremely or very concerned about hallucinations or inaccuracies, and 46% said the concern keeps them from using the tools at work. For marketing, an invented statistic or a wrong product claim is a reputational and, in regulated categories, a legal problem. Everything that goes public needs a human check.
Measurement
Many teams can't say whether AI is paying off. HubSpot's data shows at least a third of marketers can't confidently say their AI investment is working. At the business level, a 2026 Statistics Canada study found AI adopters had 16.8% higher labour productivity than non-adopters, but the gap fell to 5.1% and was no longer statistically significant once it accounted for things like data analytics, cloud and training. In plain terms, AI amplifies capabilities a business already has. It doesn't replace them. Our piece on marketing measurement and signal loss explains how to set up a baseline you can trust.
A practical way to start: Think, Plan, Execute
Think: pick the problem, not the tool
Write down the one or two marketing problems that cost you the most: slow creative production, weak reporting, generic messaging, too much manual work. Check what data you have and whether it's reliable. Decide what must stay human, usually positioning, brand voice and final approval. This is the same diagnostic thinking behind our AI and digital strategy work.
Plan: set a baseline and guardrails
Measure how things perform today: time per asset, cost per lead, conversion rate, hours spent on reporting. Write a short AI policy covering approved tools, what data can and can't be shared, and who reviews output before it goes live. Document your brand voice so the tools have something distinctive to work from.
Execute: test small, then scale what works
Run a time-boxed pilot on one workflow, for example AI-assisted ad variations for one campaign. Compare against the baseline. Keep what improves results, drop what doesn't, and only then expand. It's slower than buying every tool at once, and far cheaper.
It's also worth thinking about the other side of AI: how your brand shows up when customers use AI to research. We cover that in how to get found in AI search.
How Three Dots Brands approaches this
We help brands in Toronto and across Canada use AI where it improves results, not because it's expected. Using our Think, Plan, Execute approach, we start with the business problem, set a measurable baseline, then build AI into the workflows where it earns its place, with people responsible for strategy and quality. If you're not sure where AI fits for you, or whether something more basic is holding your marketing back, our marketing diagnostic is a good place to start.
Frequently asked questions
How many Canadian businesses use AI?
According to Statistics Canada, 19.2% of Canadian businesses said in the second quarter of 2026 that they had used AI to produce goods or deliver services over the previous 12 months, up from 12.2% a year earlier. In the third quarter of 2026, 25.2% said they planned to use AI over the next 12 months.
What is generative AI actually good for in marketing?
It is most useful for producing and testing more creative variations, summarizing research and performance data, scaling personalization once your data is in order, and removing repetitive operational work. It works best as a first draft and analysis assistant, with people setting the strategy and checking the output.
What are the biggest risks of using AI in marketing?
The three most common are brand sameness, where content starts to look like everyone else's; inaccurate or invented information getting published; and spending on tools without a way to measure whether they improve results. Each one is manageable with a clear brand point of view, human review and a baseline to measure against.
Should I hire an AI marketing agency in Toronto?
Hire for marketing judgment first and AI skills second. A good partner should be able to explain which problem AI will solve for you, how they will check its output, and how they will measure the result. Be cautious of anyone who leads with tools before they understand your goals, audience and data.
Sources
- Statistics Canada, Analysis on planned use of artificial intelligence by businesses in Canada, third quarter of 2026 (2026)
- Statistics Canada, Artificial intelligence adoption and productivity in Canadian firms (2026)
- KPMG in Canada, Canadian employees call for clear and transparent AI policies as adoption grows (2025)
- Salesforce, State of Marketing, 10th edition (2026)
- Marketing Dive, 27% of CMOs remain reluctant to adopt generative AI: Gartner (2025)
- HubSpot, Current state of generative AI in marketing (2026)
- Leger, AI Index: AI usage in Canada (2026)
- Marketing Dive, Consumers call AI-generated video ads annoying, confusing, per NIQ (2024)